María Spiropúlu

Physics at the Large Hadron Collider (LHC) and the International e+e- Linear Collider (ILC) will be complementary in many respects, as has been demonstrated at previous generations of hadron and lepton colliders. This report addresses the possible interplay between the LHC and ILC in testing the Standard Model and in discovering and determining the origin of new physics. Mutual benefits for the physics programme at both machines can occur both at the level of a combined interpretation of Hadron Collider and Linear Collider data and at the level of combined analyses of the data, where results obtained at one machine can directly influence the way analyses are carried out at the other machine. Topics under study comprise the physics of weak and strong electroweak symmetry breaking, supersymmetric models, new gauge theories, models with extra dimensions, and electroweak and QCD precision physics. The status of the work that has been carried out within the LHC/ILC Study Group so far is summarized in this report. Possible topics for future studies are outlined.

Improving the temporal resolution of single photon detectors has an impact on many applications, such as increased data rates and transmission distances for both classical and quantum optical communication systems, higher spatial resolution in laser ranging and observation of shorter-lived fluorophores in biomedical imaging. In recent years, superconducting nanowire single-photon detectors (SNSPDs) have emerged as the highest efficiency time-resolving single-photon counting detectors available in the near infrared. As the detection mechanism in SNSPDs occurs on picosecond time scales, SNSPDs have been demonstrated with exquisite temporal resolution below 15 ps. We reduce this value to 2.7$\pm$0.2 ps at 400 nm and 4.6$\pm$0.2 ps at 1550 nm, using a specialized niobium nitride (NbN) SNSPD. The observed photon-energy dependence of the temporal resolution and detection latency suggests that intrinsic effects make a significant contribution.

Just as classical information technology rests on a foundation built of interconnected information-processing systems, quantum information technology (QIT) must do the same. A critical component of such systems is the interconnect, a device or process that allows transfer of information between disparate physical media, for example, semiconductor electronics, individual atoms, light pulses in optical fiber, or microwave fields. While interconnects have been well engineered for decades in the realm of classical information technology, quantum interconnects (QuICs) present special challenges, as they must allow the transfer of fragile quantum states between different physical parts or degrees of freedom of the system. The diversity of QIT platforms (superconducting, atomic, solid-state color center, optical, etc.) that will form a quantum internet poses additional challenges. As quantum systems scale to larger size, the quantum interconnect bottleneck is imminent, and is emerging as a grand challenge for QIT. For these reasons, it is the position of the community represented by participants of the NSF workshop on Quantum Interconnects that accelerating QuIC research is crucial for sustained development of a national quantum science and technology program. Given the diversity of QIT platforms, materials used, applications, and infrastructure required, a convergent research program including partnership between academia, industry and national laboratories is required. This document is a summary from a U.S. National Science Foundation supported workshop held on 31 October - 1 November 2019 in Alexandria, VA. Attendees were charged to identify the scientific and community needs, opportunities, and significant challenges for quantum interconnects over the next 2-5 years.

Quantum transduction, the process of converting quantum signals from one form of energy to another, is an important area of quantum science and technology. The present perspective article reviews quantum transduction between microwave and optical photons, an area that has recently seen a lot of activity and progress because of its relevance for connecting superconducting quantum processors over long distances, among other applications. Our review covers the leading approaches to achieving such transduction, with an emphasis on those based on atomic ensembles, opto-electromechanics, and electro-optics. We briefly discuss relevant metrics from the point of view of different applications, as well as challenges for the future.

A bstract Using variational autoencoders trained on known physics processes, we develop a one-sided threshold test to isolate previously unseen processes as outlier events. Since the autoencoder training does not depend on any specific new physics signature, the proposed procedure doesn’t make specific assumptions on the nature of new physics. An event selection based on this algorithm would be complementary to classic LHC searches, typically based on model-dependent hypothesis testing. Such an algorithm would deliver a list of anomalous events, that the experimental collaborations could further scrutinize and even release as a catalog, similarly to what is typically done in other scientific domains. Event topologies repeating in this dataset could inspire new-physics model building and new experimental searches. Running in the trigger system of the LHC experiments, such an application could identify anomalous events that would be otherwise lost, extending the scientific reach of the LHC.

The holographic principle, theorized to be a property of quantum gravity, postulates that the description of a volume of space can be encoded on a lower-dimensional boundary. The anti-de Sitter (AdS)/conformal field theory correspondence or duality1 is the principal example of holography. The Sachdev-Ye-Kitaev (SYK) model of N ≫ 1 Majorana fermions2,3 has features suggesting the existence of a gravitational dual in AdS2, and is a new realization of holography4-6. We invoke the holographic correspondence of the SYK many-body system and gravity to probe the conjectured ER=EPR relation between entanglement and spacetime geometry7,8 through the traversable wormhole mechanism as implemented in the SYK model9,10. A qubit can be used to probe the SYK traversable wormhole dynamics through the corresponding teleportation protocol9. This can be realized as a quantum circuit, equivalent to the gravitational picture in the semiclassical limit of an infinite number of qubits9. Here we use learning techniques to construct a sparsified SYK model that we experimentally realize with 164 two-qubit gates on a nine-qubit circuit and observe the corresponding traversable wormhole dynamics. Despite its approximate nature, the sparsified SYK model preserves key properties of the traversable wormhole physics: perfect size winding11-13, coupling on either side of the wormhole that is consistent with a negative energy shockwave14, a Shapiro time delay15, causal time-order of signals emerging from the wormhole, and scrambling and thermalization dynamics16,17. Our experiment was run on the Google Sycamore processor. By interrogating a two-dimensional gravity dual system, our work represents a step towards a program for studying quantum gravity in the laboratory. Future developments will require improved hardware scalability and performance as well as theoretical developments including higher-dimensional quantum gravity duals18 and other SYK-like models19.

The discovery of a Higgs particle is possible in a variety of search channels at the LHC. However, the true identity of any putative Higgs boson will, at first, remain ambiguous until one has experimentally excluded other possible assignments of quantum numbers and couplings. We quantify the degree to which one can discriminate a standard model Higgs boson from ``look-alikes'' at, or close to, the moment of discovery at the LHC. We focus on the fully-reconstructible golden decay mode to a pair of $Z$ bosons and a four-lepton final state. Considering both on-shell and off-shell $Z$'s, we show how to utilize the full decay information from the events, including the distributions and correlations of the five relevant angular variables. We demonstrate how the finite phase space acceptance of any LHC detector sculpts the decay distributions, a feature neglected in previous studies. We use likelihood ratios to discriminate a standard model Higgs from look-alikes with other spins or nonstandard parity, $CP$, or form factors. For a resonance mass of $200\text{ }\text{ }\mathrm{GeV}/{c}^{2}$, we achieve a median discrimination significance of $3\ensuremath{\sigma}$ with as few as 19 events, and even better discrimination for the off-shell decays of a $145\text{ }\text{ }\mathrm{GeV}/{c}^{2}$ resonance.

Abstract Using detailed simulations of calorimeter showers as training data, we investigate the use of deep learning algorithms for the simulation and reconstruction of single isolated particles produced in high-energy physics collisions. We train neural networks on single-particle shower data at the calorimeter-cell level, and show significant improvements for simulation and reconstruction when using these networks compared to methods which rely on currently-used state-of-the-art algorithms. We define two models: an end-to-end reconstruction network which performs simultaneous particle identification and energy regression of particles when given calorimeter shower data, and a generative network which can provide reasonable modeling of calorimeter showers for different particle types at specified angles and energies. We investigate the optimization of our models with hyperparameter scans. Furthermore, we demonstrate the applicability of the reconstruction model to shower inputs from other detector geometries, specifically ATLAS-like and CMS-like geometries. These networks can serve as fast and computationally light methods for particle shower simulation and reconstruction for current and future experiments at particle colliders.

Abstract We investigate the performance of a jet identification algorithm based on interaction networks (JEDI-net) to identify all-hadronic decays of high-momentum heavy particles produced at the LHC and distinguish them from ordinary jets originating from the hadronization of quarks and gluons. The jet dynamics are described as a set of one-to-one interactions between the jet constituents. Based on a representation learned from these interactions, the jet is associated to one of the considered categories. Unlike other architectures, the JEDI-net models achieve their performance without special handling of the sparse input jet representation, extensive pre-processing, particle ordering, or specific assumptions regarding the underlying detector geometry. The presented models give better results with less model parameters, offering interesting prospects for LHC applications.

Quantum teleportation is essential for many quantum information technologies, including long-distance quantum networks. Using fiber-coupled devices, including state-of-the-art low-noise superconducting nanowire single-photon detectors and off-the-shelf optics, we achieve conditional quantum teleportation of time-bin qubits at the telecommunication wavelength of 1536.5 nm. We measure teleportation fidelities of ≥90% that are consistent with an analytical model of our system, which includes realistic imperfections. To demonstrate the compatibility of our setup with deployed quantum networks, we teleport qubits over 22 km of single-mode fiber while transmitting qubits over an additional 22 km of fiber. Our systems, which are compatible with emerging solid-state quantum devices, provide a realistic foundation for a high-fidelity quantum Internet with practical devices.2 MoreReceived 28 July 2020Accepted 16 October 2020Corrected 22 July 2021DOI:https://doi.org/10.1103/PRXQuantum.1.020317Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum channelsQuantum entanglementQuantum information architectures & platformsQuantum networksQuantum teleportationQuantum tomographyString dualitiesQuantum InformationParticles & Fields

In general-purpose particle detectors, the particle-flow algorithm may be used to reconstruct a comprehensive particle-level view of the event by combining information from the calorimeters and the trackers, significantly improving the detector resolution for jets and the missing transverse momentum. In view of the planned high-luminosity upgrade of the CERN Large Hadron Collider (LHC), it is necessary to revisit existing reconstruction algorithms and ensure that both the physics and computational performance are sufficient in an environment with many simultaneous proton-proton interactions (pileup). Machine learning may offer a prospect for computationally efficient event reconstruction that is well-suited to heterogeneous computing platforms, while significantly improving the reconstruction quality over rule-based algorithms for granular detectors. We introduce MLPF, a novel, end-to-end trainable, machine-learned particle-flow algorithm based on parallelizable, computationally efficient, and scalable graph neural networks optimized using a multi-task objective on simulated events. We report the physics and computational performance of the MLPF algorithm on a Monte Carlo dataset of top quark-antiquark pairs produced in proton-proton collisions in conditions similar to those expected for the high-luminosity LHC. The MLPF algorithm improves the physics response with respect to a rule-based benchmark algorithm and demonstrates computationally scalable particle-flow reconstruction in a high-pileup environment.

Superconducting nanowire single-photon detectors are a key technology for quantum information and science due to their high efficiency, low timing jitter, and low dark counts. In this work, we present a detector for single 1550 nm photons with up to 78% detection efficiency, timing jitter below 50 ps FWHM, 158 counts/s dark count rate, as well as a maximum count rate of 1.5 giga-counts/s at 3 dB compression. The PEACOQ detector (Performance-Enhanced Array for Counting Optical Quanta) comprises a linear array of 32 straight superconducting niobium nitride nanowires that span the mode of an optical fiber. This design supports high count rates with minimal penalties for detection efficiency and timing jitter. We show how these trade-offs can be mitigated by implementing independent readout for each nanowire and by using a temporal walk correction technique to reduce count-rate dependent timing jitter. These detectors make quantum communication practical on a 10 GHz clock.

▪ Abstract The possibility that spacetime extends beyond the familiar 3 + 1 dimensions has intrigued physicists for a century. The consequences of a dimensionally richer spacetime would be profound. Recently, new theories with higher-dimensional spacetimes have been developed to resolve the hierarchy problem in particle physics. The distinct predictions of these scenarios allow experiment to probe the existence of extra dimensions in new ways. We review the conceptual framework of these scenarios, their implications in collider and short-range gravity experiments, and their astrophysical and cosmological effects, as well as the constraints placed on them by current data.

We interpret within the phenomenological MSSM (pMSSM) the results of SUSY searches published by the CMS collaboration based on the first ~1 fb^-1 of data taken during the 2011 LHC run at 7 TeV. The pMSSM is a 19-dimensional parametrization of the MSSM that captures most of its phenomenological features. It encompasses, and goes beyond, a broad range of more constrained SUSY models. Performing a global Bayesian analysis, we obtain posterior probability densities of parameters, masses and derived observables. In contrast to constraints derived for particular SUSY breaking schemes, such as the CMSSM, our results provide more generic conclusions on how the current data constrain the MSSM.

At the Large Hadron Collider, the high-transverse-momentum events studied by experimental collaborations occur in coincidence with parasitic low-transverse-momentum collisions, usually referred to as pileup. Pileup mitigation is a key ingredient of the online and offline event reconstruction as pileup affects the reconstruction accuracy of many physics observables. We present a classifier based on Graph Neural Networks, trained to retain particles coming from high-transverse-momentum collisions, while rejecting those coming from pileup collisions. This model is designed as a refinement of the PUPPI algorithm (D. Bertolini et al., JHEP 10, 059 (2014)), employed in many LHC data analyses since 2015. Thanks to an extended basis of input information and the learning capabilities of the considered network architecture, we show an improvement in pileup-rejection performances with respect to state-of-the-art solutions.

We develop an algorithm based on an interaction network to identify high-transverse-momentum Higgs bosons decaying to bottom quark-antiquark pairs and distinguish them from ordinary jets that reflect the configurations of quarks and gluons at short distances. The algorithm's inputs are features of the reconstructed charged particles in a jet and the secondary vertices associated with them. Describing the jet shower as a combination of particle-to-particle and particle-to-vertex interactions, the model is trained to learn a jet representation on which the classification problem is optimized. The algorithm is trained on simulated samples of realistic LHC collisions, released by the CMS Collaboration on the CERN Open Data Portal. The interaction network achieves a drastic improvement in the identification performance with respect to state-of-the-art algorithms.

Pattern recognition problems in high energy physics are notably different from traditional machine learning applications in computer vision. Reconstruction algorithms identify and measure the kinematic properties of particles produced in high energy collisions and recorded with complex detector systems. Two critical applications are the reconstruction of charged particle trajectories in tracking detectors and the reconstruction of particle showers in calorimeters. These two problems have unique challenges and characteristics, but both have high dimensionality, high degree of sparsity, and complex geometric layouts. Graph Neural Networks (GNNs) are a relatively new class of deep learning architectures which can deal with such data effectively, allowing scientists to incorporate domain knowledge in a graph structure and learn powerful representations leveraging that structure to identify patterns of interest. In this work we demonstrate the applicability of GNNs to these two diverse particle reconstruction problems.

The Exa.TrkX project has applied geometric learning concepts such as metric learning and graph neural networks to HEP particle tracking. Exa.TrkX's tracking pipeline groups detector measurements to form track candidates and filters them. The pipeline, originally developed using the TrackML dataset (a simulation of an LHC-inspired tracking detector), has been demonstrated on other detectors, including DUNE Liquid Argon TPC and CMS High-Granularity Calorimeter. This paper documents new developments needed to study the physics and computing performance of the Exa.TrkX pipeline on the full TrackML dataset, a first step towards validating the pipeline using ATLAS and CMS data. The pipeline achieves tracking efficiency and purity similar to production tracking algorithms. Crucially for future HEP applications, the pipeline benefits significantly from GPU acceleration, and its computational requirements scale close to linearly with the number of particles in the event.

Superconducting nanowire single-photon detectors (SNSPDs) are the highest-performance photon-counting technology in the near infrared, but traditional designs typically trade off between timing resolution and detection efficiency. The authors utilize transmission-line engineering and differential readout to achieve a design with high detection efficiency and low jitter simultaneously. This design also enables imaging capabilities and photon-number resolution, and is compatible with commercial time taggers. The device is a versatile solution for photon counting in various applications, including quantum computing, quantum communication, biomedicine, and ranging.

The maximum temperature, the integral and the full width at half maximum of the thermoluminescence glow-peak as a function of the heating rate were studied. The glow-peaks studied are the 110°C glow-peak of Norwegian quartz, the 210°C glow-peak of LiF:Mg,Ti (TLD-700) and the 250°C glow-peak of natural CaF2:MBLE. The heating rate ranges from 2 up to 70°Cs. The experimental results are compared with theoretical calculations in order to test the theory.

We investigate how a Generative Adversarial Network could be used to generate a list of particle four-momenta from LHC proton collisions, allowing one to define a generative model that could abstract from the irregularities of typical detector geometries. As an example of application, we show how such an architecture could be used as a generator of LHC parasitic collisions (pileup). We present two approaches to generate the events: unconditional generator and generator conditioned on missing transverse energy. We assess generation performances in a realistic LHC data-analysis environment, with a pileup mitigation algorithm applied.

The Illinois Express Quantum Network (IEQNET) is a program to realize metropolitan scale quantum networking over deployed optical fiber using currently available technology. IEQNET consists of multiple sites that are geographically dispersed in the Chicago metropolitan area. Each site has one or more quantum nodes (Q-nodes) representing the communication parties in a quantum network. Q-nodes generate or measure quantum signals such as entangled photons and communicate the measurement results via standard, classical signals and conventional networking processes. The entangled photons in IEQNET nodes are generated at multiple wavelengths, and are selectively distributed to the desired users via transparent optical switches. Here we describe the network architecture of IEQNET, including the Internet-inspired layered hierarchy that leverages software-defined networking (SDN) technology to perform traditional wavelength routing and assignment between the Q-nodes. Specifically, SDN decouples the control and data planes, with the control plane being entirely implemented in the classical domain. We also discuss the IEQNET processes that address issues associated with synchronization, calibration, network monitoring, and scheduling. An important goal of IEQNET is to demonstrate the extent to which the control plane classical signals can co-propagate with the data plane quantum signals in the same fiber lines (quantum-classical signal "coexistence"). This goal is furthered by the use of tunable narrow-band optical filtering at the receivers and, at least in some cases, a wide wavelength separation between the quantum and classical channels. We envision IEQNET to aid in developing robust and practical quantum networks by demonstrating metro-scale quantum communication tasks such as entanglement distribution and quantum-state teleportation.

Abstract Recently, graph neural networks (GNNs) have been successfully used for a variety of particle reconstruction problems in high energy physics, including particle tracking. The Exa.TrkX pipeline based on GNNs demonstrated promising performance in reconstructing particle tracks in dense environments. It includes five discrete steps: data encoding, graph building, edge filtering, GNN, and track labeling. All steps were written in Python and run on both GPUs and CPUs. In this work, we accelerate the Python implementation of the pipeline through customized and commercial GPU-enabled software libraries, and develop a C++ implementation for inferencing the pipeline. The implementation features an improved, CUDA-enabled fixed-radius nearest neighbor search for graph building and a weakly connected component graph algorithm for track labeling. GNNs and other trained deep learning models are converted to ONNX and inferenced via the ONNX Runtime C++ API. The complete C++ implementation of the pipeline allows integration with existing tracking software. We report the memory usage and average event latency tracking performance of our implementation applied to the TrackML benchmark dataset.

Superconducting nanowire single photon detectors (SNSPDs) are the highest-performing technology for time-resolved single-photon counting from the UV to the near-infrared. The recent discovery of single-photon sensitivity in micrometer-scale superconducting wires is a promising pathway to explore for large active area devices with application to dark matter searches and fundamental physics experiments. We present 8-pixel 1 mm2 superconducting microwire single photon detectors (SMSPDs) with 1 μm-wide wires fabricated from WSi and MoSi films of various stoichiometries using electron-beam and optical lithography. Devices made from all materials and fabrication techniques show saturated internal detection efficiency at 1064 nm in at least one pixel, and the best performing device made from silicon-rich WSi shows single-photon sensitivity in all eight pixels and saturated internal detection efficiency in 6/8 pixels. This detector is the largest reported active-area SMSPD or SNSPD with near-IR sensitivity, and it extends the SMSPD to an array format. By further optimizing the photolithography techniques presented in this work, a viable pathway exists to realize larger devices with cm2-scale active area and beyond.

A missing energy discovery is possible at the LHC with the first $100\text{ }\text{ }{\mathrm{pb}}^{\ensuremath{-}1}$ of understood data. We present a realistic strategy to rapidly narrow the list of candidate theories at, or close to, the moment of discovery. The strategy is based on robust ratios of inclusive counts of simple physics objects. We study specific cases showing discrimination of look-alike models in simulated data sets that are at least 10 to 100 times smaller than used in previous studies. We discriminate supersymmetry models from nonsupersymmetric look-alikes with only $100\text{ }\text{ }{\mathrm{pb}}^{\ensuremath{-}1}$ of simulated data, using combinations of observables that trace back to differences in spin.

We show how an event topology classification based on deep learning could be used to improve the purity of data samples selected in real time at the Large Hadron Collider. We consider different data representations, on which different kinds of multi-class classifiers are trained. Both raw data and high-level features are utilized. In the considered examples, a filter based on the classifier’s score can be trained to retain $$\sim 99\%$$ of the interesting events and reduce the false-positive rate by more than one order of magnitude. By operating such a filter as part of the online event selection infrastructure of the LHC experiments, one could benefit from a more flexible and inclusive selection strategy while reducing the amount of downstream resources wasted in processing false positives. The saved resources could translate into a reduction of the detector operation cost or into an effective increase of storage and processing capabilities, which could be reinvested to extend the physics reach of the LHC experiments.

Superconducting nanowire single-photon detectors (SNSPDs) are a leading detector type for time correlated single photon counting, especially in the near-infrared. When operated at high count rates, SNSPDs exhibit increased timing jitter caused by internal device properties and features of the RF amplification chain. Variations in RF pulse height and shape lead to variations in the latency of timing measurements. To compensate for this, we demonstrate a calibration method that correlates delays in detection events with the time elapsed between pulses. The increase in jitter at high rates can be largely canceled in software by applying corrections derived from the calibration process. We demonstrate our method with a single-pixel tungsten silicide SNSPD and show it decreases high count rate jitter. The technique is especially effective at removing a long tail that appears in the instrument response function at high count rates. At a count rate of 11.4 MCounts/s, we reduce the full width at 1% maximum level (FW1%M) by 45%. The method, therefore, enables certain quantum communication protocols that are rate-limited by the FW1%M metric to operate almost twice as fast.

We demonstrate a three-node quantum network for C-band photon pairs using 2 pairs of 59 km of deployed fiber between Fermi and Argonne National Laboratories. The C-band pairs are directed to nodes using a standard telecommunication switch and synchronized to picosecond-scale timing resolution using a coexisting O- or L-band optical clock distribution system. We measure a reduction of coincidence-to-accidental ratio (CAR) of the C-band pairs from 51 $\pm$ 2 to 5.3 $\pm$ 0.4 due to Raman scattering of the O-band clock pulses. Despite this reduction, the CAR is nevertheless suitable for quantum networks.

Direct searches for electroweak pair production of new particles at the LHC are a difficult proposition, due to the large background and low signal cross sections. We demonstrate how these searches can be improved by a combination of new razor variables and shape analysis of signal and background kinematics. We assume that the pair-produced particles decay to charged leptons and missing energy, either directly or through a W boson. In both cases the final state is a pair of opposite sign leptons plus missing transverse energy. We estimate exclusion reach in terms of sleptons and charginos as realized in minimal supersymmetry. We compare this super-razor approach in detail to analyses based on other kinematic variables, showing how the super-razor uses more of the relevant kinematic information while achieving higher selection efficiency on signals, including cases with compressed spectra.

We present test beam studies and results on the timing performance and characterization of the time resolution of Lutetium–Yttrium Orthosilicate (LYSO)-based calorimeters. We demonstrate that a time resolution of 30 ps is achievable for a particular design. Furthermore, we discuss precision timing calorimetry as a tool for the mitigation of physics object performance degradation effects due to the large number of simultaneous interactions in the high luminosity environment foreseen at the Large Hadron Collider.

One possibility to make a fast and radiation resistant shower maximum (SM) detector is to use a secondary emitter as an active element. We present below test beam results, obtained with different types of photodetectors based on microchannel plates (MCPs) as the secondary emitter. We performed the measurements at the Fermilab Test Beam Facility with 120 GeV proton beam and 12 GeV and 32 GeV secondary beams. The goal of the measurement with 120 GeV protons was to determine time resolution for minimum ionizing particles (MIPs). The SM time resolution we obtained for this new type of detector is at the level of 20–30 ps. We estimate that a significant contribution to the detector response originates from secondary emission of the MCP. This work can be considered as the first step in building a new type of calorimeter based on this principle.

Author(s): Farrell, Steven; Calafiura, Paolo; Mudigonda, Mayur; Prabhat; Anderson, Dustin; Vlimant, Jean-Roch; Zheng, Stephan; Bendavid, Josh; Spiropulu, Maria; Cerati, Giuseppe; Gray, Lindsey; Kowalkowski, Jim; Spentzouris, Panagiotis; Tsaris, Aristeidis | Abstract: For the past year, the HEP.TrkX project has been investigating machine learning solutions to LHC particle track reconstruction problems. A variety of models were studied that drew inspiration from computer vision applications and operated on an image-like representation of tracking detector data. While these approaches have shown some promise, image-based methods face challenges in scaling up to realistic HL-LHC data due to high dimensionality and sparsity. In contrast, models that can operate on the spacepoint representation of track measurements (hits) can exploit the structure of the data to solve tasks efficiently. In this paper we will show two sets of new deep learning models for reconstructing tracks using space-point data arranged as sequences or connected graphs. In the first set of models, Recurrent Neural Networks (RNNs) are used to extrapolate, build, and evaluate track candidates akin to Kalman Filter algorithms. Such models can express their own uncertainty when trained with an appropriate likelihood loss function. The second set of models use Graph Neural Networks (GNNs) for the tasks of hit classification and segment classification. These models read a graph of connected hits and compute features on the nodes and edges. They adaptively learn which hit connections are important and which are spurious. The models are scaleable with simple architecture and relatively few parameters. Results for all models will be presented on ACTS generic detector simulated data.

Recent work has shown that quantum annealing for machine learning, referred to as QAML, can perform comparably to state-of-the-art machine learning methods with a specific application to Higgs boson classification. We propose QAML-Z, a novel algorithm that iteratively zooms in on a region of the energy surface by mapping the problem to a continuous space and sequentially applying quantum annealing to an augmented set of weak classifiers. Results on a programmable quantum annealer show that QAML-Z matches classical deep neural network performance at small training set sizes and reduces the performance margin between QAML and classical deep neural networks by almost 50% at large training set sizes, as measured by area under the ROC curve. The significant improvement of quantum annealing algorithms for machine learning and the use of a discrete quantum algorithm on a continuous optimization problem both opens a new class of problems that can be solved by quantum annealers and suggests the approach in performance of near-term quantum machine learning towards classical benchmarks.

Deterministic generation of single photons is essential for many quantum information technologies. A bulk optical nonlinearity emitting a photon pair, where the measurement of one of the photons heralds the presence of the other, is commonly used with the caveat that the single-photon emission rate is constrained due to a trade-off between multiphoton events and pair emission rate. Using an efficient and low noise photon-number-resolving superconducting nanowire detector we herald, in real time, a single photon at telecommunication wavelength. We perform a second-order photon correlation ${g}^{2}(0)$ measurement of the signal mode conditioned on the measured photon number of the idler mode for various pump powers and demonstrate an improvement of a heralded single-photon source. We develop an analytical model using a phase-space formalism that encompasses all multiphoton effects and relevant imperfections, such as loss and multiple Schmidt modes. We perform a maximum-likelihood fit to test the agreement of the model to the data and extract the best-fit mean photon number $\ensuremath{\mu}$ of the pair source for each pump power. A maximum reduction of $0.118\ifmmode\pm\else\textpm\fi{}0.012$ in the photon ${g}^{2}(0)$ correlation function at $\ensuremath{\mu}=0.327\ifmmode\pm\else\textpm\fi{}0.007$ is obtained, indicating a strong suppression of multiphoton emissions. For a fixed ${g}^{2}(0)=7\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}3}$, we increase the single pair generation probability by 25%. Our experiment, built using fiber-coupled and off-the-shelf components, delineates a path to engineering ideal sources of single photons.

We continue the study of micro-channel plate photomultiplier (MCP-PMT) as the active element of a shower maximum (SM) detector. We present test beam results obtained with Photek 240 and Photonis XP85011 MCP-PMTs devices. For proton beams, we obtained a time resolution of 9.6 ps, representing a significant improvement over past results using the same time of flight system. For electron beams, the time resolution obtained for this new type of SM detector is measured to be at the level of 13 ps when we use Photek 240 as the active element of the SM. Using the Photonis XP85011 MCP-PMT as the active element of the SM, we performed time resolution measurements with pixel readout, and achieved a TR better than 30 ps, The pixel readout was observed to improve upon the TR compared to the case where the individual channels were summed.

We present results on an exploratory research program that aims to study the feasibility of novel, sustained, dense, and efficient quantum information generation, storage, retrieval, relay and distribution devices and systems towards future advanced quantum networking architectures. The pathfinder program is anchored on original ideas and device conceptual designs that turn the phononic-sourced challenges into an opportunity towards optimized transduction chains over a broad range of length and energy/temperature scales. The proposal involves conceptual design and feasibility towards integration, commissioning and benchmarking of challenging transduction devices in a scaled quantum network setup.

Model size and inference speed at deployment time, are major challenges in many deep learning applications. A promising strategy to overcome these challenges is quantization. However, a straightforward uniform quantization to very low precision can result in significant accuracy loss. Mixed-precision quantization, based on the idea that certain parts of the network can accommodate lower precision without compromising performance compared to other parts, offers a potential solution. In this work, we present High Granularity Quantization (HGQ), an innovative quantization-aware training method designed to fine-tune the per-weight and per-activation precision in an automatic way for ultra-low latency and low power neural networks which are to be deployed on FPGAs. We demonstrate that HGQ can outperform existing methods by a substantial margin, achieving resource reduction by up to a factor of 20 and latency improvement by a factor of 5 while preserving accuracy.

In this paper we build a comprehensive analysis framework to perform direct extraction of all possible effective Higgs couplings to neutral electroweak gauge bosons in the decay to electrons and muons, the so called `golden channel'. Our framework is based on a maximum likelihood method constructed from analytic expressions of the fully differential cross sections for $h \rightarrow 4\ell$ and for the dominant irreducible $q\bar{q} \rightarrow 4\ell$ background, where $4\ell = 2e2\mu, 4e, 4\mu$. Detector effects are included by an explicit convolution of these analytic expressions with the appropriate transfer function over all center of mass variables. Using the full set of decay observables, we construct an unbinned 8-dimensional detector-level likelihood function which is continuous in the effective couplings and includes systematic uncertainties. We consider all possible $ZZ$, $Z\gamma$ and $\gamma\gamma$ couplings, allowing for general CP odd/even admixtures and any possible phases. We describe how the convolution is performed and demonstrate the validity and power of the framework with a number of supporting checks and example fits. The framework can be used to perform a variety of multi-parameter extractions, including their correlations, to determine the Higgs couplings to neutral electroweak gauge bosons using data obtained at the LHC and other future colliders.

In this paper we report measurements of the uniformity of time resolution, signal amplitude, and charged particle detection efficiency across the sensor surface of low-gain avalanche detectors (LGAD). Comparisons of the performance of sensors with different doping concentrations and different active thicknesses are presented, as well as their temperature dependence and radiation tolerance up to 6×1014 n/cm2. Results were obtained at the Fermilab test beam facility using 120 GeV proton beams, and a high precision pixel tracking detector. LGAD sensors manufactured by the Centro Nacional de Microelectrónica (CNM) and Hamamatsu Photonics (HPK) were studied. The uniformity of the sensor response in pulse height before irradiation was found to have a 2% spread. The signal detection efficiency and timing resolution in the sensitive areas before irradiation were found to be 100% and 30–40 ps, respectively. A “no-response” area between pads was measured to be about 130 μm for CNM and 170μm for HPK sensors. After a neutron fluence of 6×1014 n/cm2 the CNM sensor exhibits a large gain variation of up to a factor of 2.5 when comparing metalized and non-metalized sensor areas. An irradiated CNM sensor achieved a time resolution of 30 ps for the metalized area and 40 ps for the non-metalized area, while a HPK sensor irradiated to the same fluence achieved a 30 ps time resolution.

Abstract At the High Luminosity Large Hadron Collider (HL-LHC), traditional track reconstruction techniques that are critical for physics analysis will need to be upgraded to scale with track density. Quantum annealing has shown promise in its ability to solve combinatorial optimization problems amidst an ongoing effort to establish evidence of a quantum speedup. As a step towards exploiting such potential speedup, we investigate a track reconstruction approach by adapting the existing geometric Denby-Peterson (Hopfield) network method to the quantum annealing framework for HL-LHC conditions. We develop additional techniques to embed the problem onto existing and near-term quantum annealing hardware. Results using simulated annealing and quantum annealing with the D-Wave 2X system on the TrackML open dataset are presented, demonstrating the successful application of a quantum annealing algorithm to the track reconstruction challenge. We find that combinatorial optimization problems can effectively reconstruct tracks, suggesting possible applications for fast hardware-specific implementations at the HL-LHC while leaving open the possibility of a quantum speedup for tracking.

We continue the study of micro channel plates (MCP) as the active element of a shower maximum (SM) detector. We present below test beam results obtained with MCPs detecting directly secondary particles of an electromagnetic shower. The MCP efficiency to shower particles is close to 100%. The time resolution obtained for this new type of the SM detector is at the level of 40 ps.

The ratio of the Higgs couplings to WW and ZZ pairs, λ_{WZ}, is a fundamental parameter in electroweak symmetry breaking as well as a measure of the (approximate) custodial symmetry possessed by the gauge boson mass matrix. We show that Higgs decays to four leptons are sensitive, via tree level or one-loop interference effects, to both the magnitude and, in particular, overall sign of λ_{WZ}. Determining this sign requires interference effects, as it is nearly impossible to measure with rate information. Furthermore, simply determining the sign effectively establishes the custodial representation of the Higgs boson. We find that h→4ℓ (4ℓ≡2e2μ, 4e, 4μ) decays have excellent prospects of directly establishing the overall sign at a high luminosity 13 TeV LHC. We also examine the ultimate LHC sensitivity in h→4ℓ to the magnitude of λ_{WZ}. Our results are independent of other measurements of the Higgs boson couplings and, in particular, largely free of assumptions about the top quark Yukawa couplings which also enter at one loop. This makes h→4ℓ a unique and independent probe of electroweak symmetry breaking and custodial symmetry.

For the past year, the HEP.TrkX project has been investigating machine learning solutions to LHC particle track reconstruction problems. A variety of models were studied that drew inspiration from computer vision applications and operated on an image-like representation of tracking detector data. While these approaches have shown some promise, image-based methods face challenges in scaling up to realistic HL-LHC data due to high dimensionality and sparsity. In contrast, models that can operate on the spacepoint representation of track measurements ("hits") can exploit the structure of the data to solve tasks efficiently. In this paper we will show two sets of new deep learning models for reconstructing tracks using space-point data arranged as sequences or connected graphs. In the first set of models, Recurrent Neural Networks (RNNs) are used to extrapolate, build, and evaluate track candidates akin to Kalman Filter algorithms. Such models can express their own uncertainty when trained with an appropriate likelihood loss function. The second set of models use Graph Neural Networks (GNNs) for the tasks of hit classification and segment classification. These models read a graph of connected hits and compute features on the nodes and edges. They adaptively learn which hit connections are important and which are spurious. The models are scaleable with simple architecture and relatively few parameters. Results for all models will be presented on ACTS generic detector simulated data.

The high luminosity upgrade of the Large Hadron Collider (HL-LHC) at CERN is expected to provide instantaneous luminosities of 5×1034cm−2s−1. The high luminosities expected at the HL-LHC will be accompanied by a factor of 5–10 more pileup compared with LHC conditions in 2015, further increasing the challenge for particle identification and event reconstruction. Precision timing allows us to extend calorimetric measurements into such a high density environment by subtracting the energy deposits from pileup interactions. Calorimeters employing silicon as the active component have recently become a viable choice for the HL-LHC and future collider experiments which face very high radiation environments. In this paper, we present studies of basic calorimetric and precision timing measurements using a prototype composed of tungsten absorber and silicon sensor as the active medium. We show that for the bulk of electromagnetic showers induced by electrons in the range of 20–30 GeV, we can achieve time resolutions better than 25 ps per single pad sensor.

A free-space coupled superconducting nanowire single-photon detector with high efficiency at 1550 nm, sub-0.1 Hz dark count rate, and sub-15 ps timing jitter is demonstrated.

Abstract We present an application of anomaly detection techniques based on deep recurrent autoencoders (AEs) to the problem of detecting gravitational wave (GW) signals in laser interferometers. Trained on noise data, this class of algorithms could detect signals using an unsupervised strategy, i.e. without targeting a specific kind of source. We develop a custom architecture to analyze the data from two interferometers. We compare the obtained performance to that obtained with other AE architectures and with a convolutional classifier. The unsupervised nature of the proposed strategy comes with a cost in terms of accuracy, when compared to more traditional supervised techniques. On the other hand, there is a qualitative gain in generalizing the experimental sensitivity beyond the ensemble of pre-computed signal templates. The recurrent AE outperforms other AEs based on different architectures. The class of recurrent AEs presented in this paper could complement the search strategy employed for GW detection and extend the discovery reach of the ongoing detection campaigns.

The operation of long-distance quantum networks requires photons to be synchronized and must account for length variations of quantum channels. We demonstrate a 200 MHz clock-rate fiber optic-based quantum network using off-the-shelf components combined with custom-made electronics and telecommunication C-band photons. The network is backed by a scalable and fully automated synchronization system with ps-scale timing resolution. Synchronization of the photons is achieved by distributing O-band-wavelength laser pulses between network nodes. Specifically, we distribute photon pairs between three nodes, and measure a reduction of coincidence-to-accidental ratio from 77 to only 42 when the synchronization system is enabled, which permits high-fidelity qubit transmission. Our demonstration sheds light on the role of noise in quantum communication and represents a key step in realizing deployed co-existing classical-quantum networks.

The Illinois Express Quantum Network (IEQNET) is a program to realize metro-scale quantum networking over deployed optical fiber using currently available technology. IEQNET consists of multiple sites that are geographically dispersed in the Chicago metropolitan area. Each site has one or more quantum nodes (Qnodes) representing the communication parties in a quantum network. Q-nodes generate or measure quantum signals such as entangled photons and communicate the results via standard, classical, means. The entangled photons in IEQNET nodes are generated at multiple wavelengths, and are selectively distributed to the desired users via optical switches. Here we describe the network architecture of IEQNET, including the Internet-inspired layered hierarchy that leverages software-defined-networking (SDN) technology to perform traditional wavelength routing and assignment between the Q-nodes. Specifically, SDN decouples the control and data planes, with the control plane being entirely classical. Issues associated with synchronization, calibration, network monitoring, and scheduling will be discussed. An important goal of IEQNET is demonstrating the extent to which the control plane can coexist with the data plane using the same fiber lines. This goal is furthered by the use of tunable narrow-band optical filtering at the receivers and, at least in some cases, a wide wavelength separation between the quantum and classical channels. We envision IEQNET to aid in developing robust and practical quantum networks by demonstrating metro-scale quantum communication tasks such as entanglement distribution and quantum-state teleportation.

We present an analysis of the discovery reach for supersymmetric particles at the upgraded Tevatron collider, assuming that SUSY breaking results in universal soft breaking parameters at the grand unification scale, and that the lightest supersymmetric particle is stable and neutral. We first present a review of the literature, including the issues of unification, renormalization group evolution of the supersymmetry breaking parameters and the effect of radiative corrections on the effective low energy couplings and masses of the theory. We consider the experimental bounds coming from direct searches and those arising indirectly from precision data, cosmology and the requirement of vacuum stability. The issues of flavor and CP-violation are also addressed. The main subject of this study is to update sparticle production cross sections, make improved estimates of backgrounds, delineate the discovery reach in the supergravity framework, and examine how this might vary when assumptions about universality of soft breaking parameters are relaxed. With 30 fb$^{-1}$ luminosity and one detector, charginos and neutralinos, as well as third generation squarks, can be seen if their masses are not larger than 200-250 GeV, while first and second generation squarks and gluinos can be discovered if their masses do not significantly exceed 400 GeV. We conclude that there are important and exciting physics opportunities at the Tevatron collider, which will be significantly enhanced by continued Tevatron operation beyond the first phase of Run II.

Current and future high energy physics particle colliders are capable to provide instantaneous luminosities of 1034 cm-2s-1 and above. The high center of mass energy, the large number of simultaneous collision of beam particles in the experiments and the very high repetition rates of the collision events pose huge challenges. They result in extremely high particle fluxes, causing very high occupancies in the particle physics detectors operating at these machines. To reconstruct the physics events, the detectors have to make as much information as possible available on the final state particles. We discuss how timing information with a precision of around 10 ps and below can aid the reconstruction of the physics events under such challenging conditions. High energy photons play a crucial role in this context. About one third of the particle flux originating from high energy hadron collisions is detected as photons, stemming from the decays of neutral mesons. In addition, many key physics signatures under study are identified by high energy photons in the final state. They pose a particular challenge in that they can only be detected once they convert in the detector material. The particular challenge in measuring the time of arrival of a high energy photon lies in the stochastic component of the distance to the initial conversion and the size of the electromagnetic shower. They extend spatially over distances which propagation times of the initial photon and the subsequent electromagnetic shower which are large compared to the desired precision. We present studies and measurements from test beams and a cosmic muon test stand for calorimeter based timing measurements to explore the ultimate timing precision achievable for high energy photons of 10 GeV and above. We put particular focus on techniques to measure the timing with a precision of about 10 ps in association with the energy of the photon. For calorimeters utilizing scintillating materials and light guiding components, the propagation speed of the scintillation light in the calorimeter is important. We present studies and measurements of the propagation speed on a range of detector geometries. Finally, possible applications of precision timing in future high energy physics experiments are discussed.

A bstract We study the impact of precision timing detection systems on the LHC experiments’ long-lived particle search program during the HL-LHC era. We develop algorithms that allow us to reconstruct the mass of such charged particles and perform particle identification using the time-of-flight measurement. We investigate the reach for benchmark scenarios as a function of the timing resolution, and find sensitivity improvement of up to a factor of ten over searches that use ionization energy loss information, depending on the particle’s mass.

At the High Luminosity Large Hadron Collider (HL-LHC), traditional track reconstruction techniques that are critical for analysis are expected to face challenges due to scaling with track density. Quantum annealing has shown promise in its ability to solve combinatorial optimization problems amidst an ongoing effort to establish evidence of a quantum speedup. As a step towards exploiting such potential speedup, we investigate a track reconstruction approach by adapting the existing geometric Denby-Peterson (Hopfield) network method to the quantum annealing framework and to HL-LHC conditions. Furthermore, we develop additional techniques to embed the problem onto existing and near-term quantum annealing hardware. Results using simulated annealing and quantum annealing with the D-Wave 2X system on the TrackML dataset are presented, demonstrating the successful application of a quantum annealing-inspired algorithm to the track reconstruction challenge. We find that combinatorial optimization problems can effectively reconstruct tracks, suggesting possible applications for fast hardware-specific implementations at the LHC while leaving open the possibility of a quantum speedup for tracking.

Abstract The Exa.TrkX project presents a graph neural network (GNN) technique for low-level reconstruction of neutrino interactions in a Liquid Argon Time Projection Chamber (LArTPC). GNNs are still a relatively novel technique, and have shown great promise for similar reconstruction tasks in the Large Hadron Collider (LHC). Graphs describing particle interactions are formed by treating each detector hit as a node, with edges describing the relationships between hits. We utilise a multi-head attention message passing network which performs graph convolutions in order to label each node with a particle type. We present an updated variant of our GNN architecture, with several improvements. After testing the model on more realistic simulation with regions of unresponsive wires, the target was modified from edge classification to node classification in order to increase robustness. Removing edges as a classification target opens up a broader possibility space for edge-forming techniques; we explore the model’s performance across a variety of approaches, such as Delaunay triangulation, kNN, and radius-based methods. We also extend this model to the 3D context, sharing information between detector views. By using reconstructed 3D spacepoints to map detector hits from each wire plane, the model naively constructs 2D representations that are independent yet fully consistent.

Particle tracking is a challenging pattern recognition task at the Large Hadron Collider (LHC) and the High Luminosity-LHC. Conventional algorithms, such as those based on the Kalman Filter, achieve excellent performance in reconstructing the prompt tracks from the collision points. However, they require dedicated configuration and additional computing time to efficiently reconstruct the large radius tracks created away from the collision points. We developed an end-to-end machine learning-based track finding algorithm for the HL-LHC, the Exa.TrkX pipeline. The pipeline is designed so as to be agnostic about global track positions. In this work, we study the performance of the Exa.TrkX pipeline for finding large radius tracks. Trained with all tracks in the event, the pipeline simultaneously reconstructs prompt tracks and large radius tracks with high efficiencies. This new capability offered by the Exa.TrkX pipeline may enable us to search for new physics in real time.

Superconducting nanowire single photon detectors (SNSPDs) are the highest-performing technology for time-resolved single-photon counting from the UV to the near-infrared. The recent discovery of single-photon sensitivity in micrometer-scale superconducting wires is a promising pathway to explore for large active area devices with application to dark matter searches and fundamental physics experiments. We present 8-pixel $1 mm^2$ superconducting microwire single photon detectors (SMSPDs) with $1\,\mathrm{μm}$-wide wires fabricated from WSi and MoSi films of various stoichiometries using electron-beam and optical lithography. Devices made from all materials and fabrication techniques show saturated internal detection efficiency at 1064 nm in at least one pixel, and the best performing device made from silicon-rich WSi shows single-photon sensitivity in all 8 pixels and saturated internal detection efficiency in 6/8 pixels. This detector is the largest reported active-area SMSPD or SNSPD with near-IR sensitivity published to date, and the first report of an SMSPD array. By further optimizing the photolithography techniques presented in this work, a viable pathway exists to realize larger devices with $cm^2$-scale active area and beyond.

Abstract High-dimensional quantum entanglement is a cornerstone for advanced technology enabling large-scale noise-tolerant quantum systems, fault-tolerant quantum computing, and distributed quantum networks. The recently developed biphoton frequency comb (BFC) provides a powerful platform for high-dimensional quantum information processing in its spectral and temporal quantum modes. Here we propose and generate a singly-filtered high-dimensional BFC via spontaneous parametric down-conversion by spectrally shaping only the signal photons with a Fabry-Pérot cavity. High-dimensional energy-time entanglement is verified through Franson-interference recurrences and temporal correlation with low-jitter detectors. Frequency- and temporal- entanglement of our singly-filtered BFC is then quantified by Schmidt mode decomposition. Subsequently, we distribute the high-dimensional singly-filtered BFC state over a 10 km fiber link with a post-distribution time-bin dimension lower bounded to be at least 168. Our demonstrations of high-dimensional entanglement and entanglement distribution show the singly-filtered quantum frequency comb’s capability for high-efficiency quantum information processing and high-capacity quantum networks.

We present a lightweight Python framework for distributed training of neural networks on multiple GPUs or CPUs. The framework is built on the popular Keras machine learning library. The Message Passing Interface (MPI) protocol is used to coordinate the training process, and the system is well suited for job submission at supercomputing sites. We detail the software's features, describe its use, and demonstrate its performance on systems of varying sizes on a benchmark problem drawn from high-energy physics research.

Experimentalists and theorists are still celebrating the Nobel-worthy discovery of the Higgs boson that was announced in July 2012 at CERN’s Large Hadron Collider. Now they are working on the profound implications of that discovery.

One possibility to make a fast and radiation resistant shower maximum detector is to use a secondary emitter as an active element. We report our studies of microchannel plate photomultipliers (MCPs) as the active element of a shower-maximum detector. We present test beam results obtained using Photonis XP85011 to detect secondary particles of an electromagnetic shower. We focus on the use of the multiple pixels on the Photonis MCP in order to find a transverse two-dimensional shower distribution. A spatial resolution of 0.8 mm was obtained with an 8 GeV electron beam. A method for measuring the arrival time resolution for electromagnetic showers is presented, and we show that time resolution better than 40 ps can be achieved.

This paper presents a graph neural network (GNN) technique for low-level reconstruction of neutrino interactions in a Liquid Argon Time Projection Chamber (LArTPC). GNNs are still a relatively novel technique, and have shown great promise for similar reconstruction tasks in the LHC. In this paper, a multihead attention message passing network is used to classify the relationship between detector hits by labelling graph edges, determining whether hits were produced by the same underlying particle, and if so, the particle type. The trained model is 84% accurate overall, and performs best on the EM shower and muon track classes. The model’s strengths and weaknesses are discussed, and plans for developing this technique further are summarised.

Precision tracking and vertex reconstruction play a crucial role in heavy flavor physics at CDF, in reconstructing the charm and beauty decay vertices in beauty and top events. A significant upgrade to the CDF detector, including a new silicon tracker, will support an extensive physics program with the high luminosity provided by the Main Injector accelerator upgrade. The specifications and design considerations for this new silicon tracker/vertex detector are discussed.

The work contained herein constitutes a report of the ''Beyond the Standard Model'' working group for the Workshop ''Physics at TeV Colliders'', Les Houches, France, 26 May-6 June, 2003. The research presented is original, and was performed specifically for the workshop. Tools for calculations in the minimal supersymmetric standard model are presented, including a comparison of the dark matter relic density predicted by public codes. Reconstruction of supersymmetric particle masses at the LHC and a future linear collider facility is examined. Less orthodox supersymmetric signals such as non-pointing photons and R-parity violating signals are studied. Features of extra dimensional models are examined next, including measurement strategies for radions and Higgs', as well as the virtual effects of Kaluza Klein modes of gluons. Finally, there is an update on LHC Z' studies.

Particle colliders operating at high luminosities present challenging environments for high energy physics event reconstruction and analysis. We discuss how timing information, with a precision on the order of 10 ps, can aid in the reconstruction of physics events under such conditions. We present calorimeter based timing measurements from test beam experiments in which we explore the ultimate timing precision achievable for high energy photons or electrons of 10 GeV and above. Using a prototype calorimeter consisting of a 1.7×1.7×1.7 cm3 lutetium–yttrium oxyortho-silicate (LYSO) crystal cube, read out by micro-channel plate photomultipliers, we demonstrate a time resolution of 33.5±2.1 ps for an incoming beam energy of 32 GeV. In a second measurement, using a 2.5×2.5×20 cm3 LYSO crystal placed perpendicularly to the electron beam, we achieve a time resolution of 59±11 ps using a beam energy of 4 GeV. We also present timing measurements made using a shashlik-style calorimeter cell made of LYSO and tungsten plates, and demonstrate that the apparatus achieves a time resolution of 54±5 ps for an incoming beam energy of 32 GeV.

Precision timing detectors for high energy physics experiments with temporal resolutions of a few 10 ps are of pivotal importance to master the challenges posed by the highest energy particle accelerators such as the LHC. Calorimetric timing measurements have been a focus of recent research, enabled by exploiting the temporal coherence of electromagnetic showers. Scintillating crystals with high light yield as well as silicon sensors are viable sensitive materials for sampling calorimeters. Silicon sensors have very high efficiency for charged particles. However, their sensitivity to photons, which comprise a large fraction of the electromagnetic shower, is limited. To enhance the efficiency of detecting photons, materials with higher atomic numbers than silicon are preferable. In this paper we present test beam measurements with a Cadmium-Telluride (CdTe) sensor as the active element of a secondary emission calorimeter with focus on the timing performance of the detector. A Schottky type CdTe sensor with an active area of 1cm2 and a thickness of 1 mm is used in an arrangement with tungsten and lead absorbers. Measurements are performed with electron beams in the energy range from 2 GeV to 200 GeV. A timing resolution of 20 ps is achieved under the best conditions.

Scintillator based calorimeter technology is studied with the aim to achieve particle detection with a time resolution on the order of a few 10 ps for photons and electrons at energies of a few GeV and above. We present results from a prototype of a 1.4×1.4×11.4 cm3 sampling calorimeter cell consisting of tungsten absorber plates and Cerium-doped Lutetium Yttrium Orthosilicate (LYSO) crystal scintillator plates. The LYSO plates are read out with wave lengths shifting fibers which are optically coupled to fast photo detectors on both ends of the fibers. The measurements with electrons were performed at the Fermilab Test Beam Facility (FTBF) and the CERN SPS H2 test beam. In addition to the baseline setup plastic scintillation counter and a MCP-PMT were used as trigger and as a reference for a time of flight measurement (TOF). We also present measurements with a fast laser to further characterize the response of the prototype and the photo sensors. All data were recorded using a DRS4 fast sampling digitizer. These measurements are part of an R&D program whose aim is to demonstrate the feasibility of building a large scale electromagnetic calorimeter with a time resolution on the order of 10 ps, to be used in high energy physics experiments.

We discuss a model-independent strategy for boosting new physics searches with the help of an unsupervised anomaly detection algorithm. Prior to a search, each input event is preprocessed by the algorithm - a variational autoencoder (VAE). Based on the loss assigned to each event, input data can be split into a background control sample and a signal enriched sample. Following this strategy, one can enhance the sensitivity to new physics with no assumption on the underlying new physics signature. Our results show that a typical BSM search on the signal enriched group is more sensitive than an equivalent search on the original dataset.

To address the unprecedented scale of HL-LHC data, the Exa.TrkX project is investigating a variety of machine learning approaches to particle track reconstruction. The most promising of these solutions, graph neural networks (GNN), process the event as a graph that connects track measurements (detector hits corresponding to nodes) with candidate line segments between the hits (corresponding to edges). Detector information can be associated with nodes and edges, enabling a GNN to propagate the embedded parameters around the graph and predict node-, edge- and graph-level observables. Previously, message-passing GNNs have shown success in predicting doublet likelihood, and we here report updates on the state-of-the-art architectures for this task. In addition, the Exa.TrkX project has investigated innovations in both graph construction, and embedded representations, in an effort to achieve fully learned end-to-end track finding. Hence, we present a suite of extensions to the original model, with encouraging results for hitgraph classification. In addition, we explore increased performance by constructing graphs from learned representations which contain non-linear metric structure, allowing for efficient clustering and neighborhood queries of data points. We demonstrate how this framework fits in with both traditional clustering pipelines, and GNN approaches. The embedded graphs feed into high-accuracy doublet and triplet classifiers, or can be used as an end-to-end track classifier by clustering in an embedded space. A set of post-processing methods improve performance with knowledge of the detector physics. Finally, we present numerical results on the TrackML particle tracking challenge dataset, where our framework shows favorable results in both seeding and track finding.

Studies have been done and continue on the design and construction of a Shashlik detector using Radiation hard quartz capillaries filled with wavelength shifting liquid to collect the scintillation light from LYSO crystals for use as a calorimeter in the Phase II CMS upgrade at CERN. The work presented here focuses on the studies of the capillaries and liquids that would best suit the purpose of the detector. Comparisons are made of various liquids, concentrations, and capillary construction techniques will be discussed.

The next-generation exascale network integrated architecture (NGENIA-ES) is a project specifically designed to accomplish new levels of network and computing capabilities in support of global science collaborations through the development of a new class of intelligent, agile networked systems. Its path to success is built upon our ongoing developments in multiple areas, strong ties among our high energy physics, computer and network science, and engineering teams, and our close collaboration with key technology developers and providers deeply engaged in the national strategic computing initiative (NSCI). This paper describes the building of a new class of distributed systems, our work with the leadership computing facilities (LFCs), the use of software-defined networking (SDN) methods, and the use of data-driven methods for the scheduling and optimization of network resources. Sections I–III present the challenges of data-intensive research and the important ingredients of this ecosystem. Sections IV–VI describe some crucial elements of the foreseen solution and some of the progress so far. Sections VII–IX go into the details of orchestration, software-defined networking, and scheduling optimization. Finally, Section X talks about engagement and partnerships, and Section XI gives a summary. References are given at the end.

Author(s): Choma, Nicholas; Murnane, Daniel; Ju, Xiangyang; Calafiura, Paolo; Conlon, Sean; Farrell, Steven; Prabhat; Cerati, Giuseppe; Gray, Lindsey; Klijnsma, Thomas; Kowalkowski, Jim; Spentzouris, Panagiotis; Vlimant, Jean-Roch; Spiropulu, Maria; Aurisano, Adam; Hewes, Jeremy; Tsaris, Aristeidis; Terao, Kazuhiro; Usher, Tracy | Abstract: To address the unprecedented scale of HL-LHC data, the Exa.TrkX project is investigating a variety of machine learning approaches to particle track reconstruction. The most promising of these solutions, graph neural networks (GNN), process the event as a graph that connects track measurements (detector hits corresponding to nodes) with candidate line segments between the hits (corresponding to edges). Detector information can be associated with nodes and edges, enabling a GNN to propagate the embedded parameters around the graph and predict node-, edge- and graph-level observables. Previously, message-passing GNNs have shown success in predicting doublet likelihood, and we here report updates on the state-of-the-art architectures for this task. In addition, the Exa.TrkX project has investigated innovations in both graph construction, and embedded representations, in an effort to achieve fully learned end-to-end track finding. Hence, we present a suite of extensions to the original model, with encouraging results for hitgraph classification. In addition, we explore increased performance by constructing graphs from learned representations which contain non-linear metric structure, allowing for efficient clustering and neighborhood queries of data points. We demonstrate how this framework fits in with both traditional clustering pipelines, and GNN approaches. The embedded graphs feed into high-accuracy doublet and triplet classifiers, or can be used as an end-to-end track classifier by clustering in an embedded space. A set of post-processing methods improve performance with knowledge of the detector physics. Finally, we present numerical results on the TrackML particle tracking challenge dataset, where our framework shows favorable results in both seeding and track finding.

The Deep Space Quantum Link project aims to press the foundations of quantum optics by testing gravity at large distance scales using quantum mechanics. The tests involve transmitting photons over unprecedented distances using space-based quantum networking platforms.

Abstract We present an application of anomaly detection techniques based on deep recurrent autoencoders to the problem of detecting gravitational wave signals in laser interferometers. Trained on noise data, this class of algorithms could detect signals using an unsupervised strategy, i.e., without targeting a specific kind of source. We develop a custom architecture to analyze the data from two interferometers. We compare the obtained performance to that obtained with other autoencoder architectures and with a convolutional classifier. The unsupervised nature of the proposed strategy comes with a cost in terms of accuracy, when compared to more traditional supervised techniques. On the other hand, there is a qualitative gain in generalizing the experimental sensitivity beyond the ensemble of pre-computed signal templates. The recurrent autoencoder outperforms other autoencoders based on different architectures. The class of recurrent autoencoders presented in this paper could complement the search strategy employed for gravitational wave detection and extend the reach of the ongoing detection campaigns.

The CERN-Latin-American School of High-Energy Physics is intended to give young physicists an introduction to the theoretical aspects of recent advances in elementary particle physics. These proceedings contain lectures on quantum field theory, quantum chromodynamics, physics beyond the Standard Model, neutrino physics, flavour physics and CP violation, particle cosmology, high-energy astro-particle physics, and heavy-ion physics, as well as trigger and data acquisition, and commissioning and early physics analysis of the ATLAS and CMS experiments. Also included are write-ups of short review projects performed by the student discussions groups.

Abstract Particle detectors based on scintillation light are particularly well suited for precision timing applications with resolutions of a few 10’s of ps. The large primary signal and the initial rise time of the scintillation light result in very favorable signal-to-noise conditions with fast signals. In this paper we describe timing studies using a LYSO-based sampling calorimeter with wavelength-shifting capillary light extraction and silicon photomultipliers as photosensors. We study the contributions of various steps of the signal generation to the total time resolution, and demonstrate its feasibility as a radiation-hard technology for calorimeters at high intensity hadron colliders.

At the Large Hadron Collider, the high transverse-momentum events studied by experimental collaborations occur in coincidence with parasitic low transverse-momentum collisions, usually referred to as pileup. Pileup mitigation is a key ingredient of the online and offline event reconstruction as pileup affects the reconstruction accuracy of many physics observables. We present a classifier based on Graph Neural Networks, trained to retain particles coming from high-transverse-momentum collisions, while rejecting those coming from pileup collisions. This model is designed as a refinement of the PUPPI algorithm, employed in many LHC data analyses since 2015. Thanks to an extended basis of input information and the learning capabilities of the considered network architecture, we show an improvement in pileup-rejection performances with respect to state-of-the-art solutions.

Superconducting nanowire single-photon detectors (SNSPDs) are the highest performing photon-counting technology in the near-infrared (NIR). Due to delay-line effects, large area SNSPDs typically trade-off timing resolution and detection efficiency. Here, we introduce a detector design based on transmission line engineering and differential readout for device-level signal conditioning, enabling a high system detection efficiency and a low detector jitter, simultaneously. To make our differential detectors compatible with single-ended time taggers, we also engineer analog differential-to-single-ended readout electronics, with minimal impact on the system timing resolution. Our niobium nitride differential SNSPDs achieve $47.3\,\% \pm 2.4\,\%$ system detection efficiency and sub-$10\,\mathrm{ps}$ system jitter at $775\,\mathrm{nm}$, while at $1550\,\mathrm{nm}$ they achieve $71.1\,\% \pm 3.7\,\%$ system detection efficiency and $13.1\,\mathrm{ps} \pm 0.4\,\mathrm{ps}$ system jitter. These detectors also achieve sub-100 ps timing response at one one-hundredth maximum level, $30.7\,\mathrm{ps} \pm 0.4\,\mathrm{ps}$ at $775\,\mathrm{nm}$ and $47.6\,\mathrm{ps} \pm 0.4\,\mathrm{ps}$ at $1550\,\mathrm{nm}$, enabling time-correlated single-photon counting with high dynamic range response functions. Furthermore, thanks to the differential impedance-matched design, our detectors exhibit delay-line imaging capabilities and photon-number resolution. The properties and high-performance metrics achieved by our system make it a versatile photon-detection solution for many scientific applications.

This paper presents GARCON program, illustrating its functionality on a simple HEP analysis example. The program automatically performs rectangular cuts optimization and verification for stability in a multi-dimensional phase space. The program has been successfully used by a number of very different analyses presented in the CMS Physics Technical Design Report. The current version GARCON 2.0 incorporates the feedback the authors have received. User's Manual is included as a part of the note.

Results are reported from a search for the effects of contact interactions using events with a high-mass, oppositely charged muon pair. The events are collected in proton-proton collisions at √s=7  TeV using the Compact Muon Solenoid detector at the Large Hadron Collider. The data sample corresponds to an integrated luminosity of 5.3  fb^(-1). The observed dimuon mass spectrum is consistent with that expected from the standard model. The data are interpreted in the context of a quark- and muon-compositeness model with a left-handed isoscalar current and an energy scale parameter Λ. The 95% confidence level lower limit on Λ is 9.5 TeV under the assumption of destructive interference between the standard model and contact-interaction amplitudes. For constructive interference, the limit is 13.1 TeV. These limits are comparable to the most stringent ones reported to date.

In this technical note we present technical details on various aspects of the framework introduced in arXiv:1401.2077 aimed at extracting effective Higgs couplings in the $h\to 4\ell$ `golden channel'. Since it is the primary feature of the framework, we focus in particular on the convolution integral which takes us from `truth' level to `detector' level and the numerical and analytic techniques used to obtain it. We also briefly discuss other aspects of the framework.

We observe that the comment of [1, arXiv:2302.07897] is consistent with [2] on key points: i) the microscopic mechanism of the experimentally observed teleportation is size winding and ii) the system thermalizes and scrambles at the time of teleportation. These properties are consistent with a gravitational interpretation of the teleportation dynamics, as opposed to the late-time dynamics. The objections of [1] concern counterfactual scenarios outside of the experimentally implemented protocol.

We report the first demonstration of using the Quantum Instrumentation and Control Kit (QICK) system on RFSoC-FPGA technology to drive the electro-optic intensity modulator that generate time-bin entangled photon pairs and to detect the photon signals. With the QICK system, we achieve high levels of performance metrics including coincidence-to-accidental ratio exceeding 150, and entanglement visibility exceeding 95%, consistent with performance metrics achieved using conventional waveform generators. We also demonstrate simultaneous detector readout using the digitization functional of QICK, achieving internal system synchronization time resolution of 3.2 ps. The work reported in this paper represents an explicit demonstration of the feasibility for replacing commercial waveform generators and time taggers with RFSoC-FPGA technology in the operation of a quantum network, representing a cost reduction of more than an order of magnitude.

We investigate the optimal coexisting classical light wavelengths to use alongside C- band quantum networks to minimize noise from spontaneous Raman scattering and discuss techniques for optimizing coexisting time synchronization systems for teleportation and entanglement swapping.

We demonstrate a quantum-limited silicon photonic coherent receiver with 26.0 dB shot noise clearance, 34.6 µ W knee power, and 0.00200 mm 2 chip area. We measure squeezed vacuum with the integrated receiver and demonstrate phase-locking to the squeezed quadrature.

We report our progress towards the first experimental d emonstration of three photon time-bin GHZ entangled states. We also develop a theoretical model based on phase-space techniques to support the experimental results.

We theoretically investigate quantum multi-phase estimation based on integrated quantum photonics. We design a quantum optical circuit, simulate the phase estimation protocol, and develop a generalized model to predict phase variance.

We detail our experiments towards generating GHZ states encoded into time-bin qubits using a switch. We present a theoretical model founded on phase-space techniques to corroborate our experimental findings.

High-dimensional quantum entanglement is a cornerstone for advanced technology enabling large-scale noise-tolerant quantum systems, fault-tolerant quantum computing, and distributed quantum networks. The recently developed biphoton frequency comb (BFC) provides a powerful platform for high-dimensional quantum information processing in its spectral and temporal quantum modes. Here we propose and generate a singly-filtered high-dimensional BFC via spontaneous parametric down-conversion by spectrally shaping only the signal photons with a Fabry-Perot cavity. High-dimensional energy-time entanglement is verified through Franson-interference recurrences and temporal correlation with low-jitter detectors. Frequency- and temporal- entanglement of our singly-filtered BFC is then quantified by Schmidt mode decomposition. Subsequently, we distribute the high-dimensional singly-filtered BFC state over a 10 km fiber link with a post-distribution time-bin dimension lower bounded to be at least 168. Our demonstrations of high-dimensional entanglement and entanglement distribution show the capability of the singly-filtered quantum frequency comb for high-efficiency quantum information processing and high-capacity quantum networks.

Entanglement distribution based on time-bin qubits is an attractive option for emerging quantum networks. We demonstrate a 4.09 GHz repetition rate source of photon pairs entangled across early and late time bins separated by 80 ps. Simultaneous high rates and high visibilities are achieved through frequency multiplexing the spontaneous parametric down conversion output into 8 time-bin entangled pairs. We demonstrate entanglement visibilities as high as 99.4%, total entanglement rates up to 3.55e6 coincidences/s, and predict a straightforward path towards achieving up to an order of magnitude improvement in rates without compromising visibility. Finally, we resolve the density matrices of the entangled states for each multiplexed channel and express distillable entanglement rates in ebit/s, thereby quantifying the tradeoff between visibility and coincidence rates that contributes to useful entanglement distribution. This source is a fundamental building block for high-rate entanglement-based quantum key distribution systems or advanced quantum networks.

High-dimensional entanglement provides unique ways of transcending the limitations of current approaches in quantum information processing, quantum communications based on qubits. The generation of time-frequency qudit states offer significantly increased quantum capacities while keeping the number of photons constant, but pose significant challenges regarding the possible measurements for certification of entanglement. Here, we develop a new scheme and experimentally demonstrate the certification of 24-dimensional entanglement and a 9-dimensional quantum steering. We then subject our photon-pairs to dispersion conditions equivalent to the transmission through 600-km of fiber and still certify 21-dimensional entanglement. Furthermore, we use a steering inequality to prove 7-dimensional entanglement in a semi-device independent manner, proving that large chromatic dispersion is not an obstacle in distributing and certifying high-dimensional entanglement and quantum steering. Our highly scalable scheme is based on commercial telecommunication optical fiber components and recently developed low-jitter high-efficiency single-photon detectors, thus opening new pathways towards advanced large-scale quantum information processing and high-performance, noise-tolerant quantum communications with time-energy measurements

Strong motivation for investing in quantum sensing arises from the need to investigate phenomena that are very weakly coupled to the matter and fields well described by the Standard Model. These can be related to the problems of dark matter, dark sectors not necessarily related to dark matter (for example sterile neutrinos), dark energy and gravity, fundamental constants, and problems with the Standard Model itself including the Strong CP problem in QCD. Resulting experimental needs typically involve the measurement of very low energy impulses or low power periodic signals that are normally buried under large backgrounds. This report documents the findings of the 2023 Quantum Sensors for High Energy Physics workshop which identified enabling quantum information science technologies that could be utilized in future particle physics experiments, targeting high energy physics science goals.

The challenging environment of real-time data processing systems at the Large Hadron Collider (LHC) strictly limits the computational complexity of algorithms that can be deployed. For deep learning models, this implies that only models with low computational complexity that have weak inductive bias are feasible. To address this issue, we utilize knowledge distillation to leverage both the performance of large models and the reduced computational complexity of small ones. In this paper, we present an implementation of knowledge distillation, demonstrating an overall boost in the student models' performance for the task of classifying jets at the LHC. Furthermore, by using a teacher model with a strong inductive bias of Lorentz symmetry, we show that we can induce the same inductive bias in the student model which leads to better robustness against arbitrary Lorentz boost.

Model-agnostic anomaly detection is one of the promising approaches in the search for new beyond the standard model physics. In this paper, we present Set-VAE, a particle-based variational autoencoder (VAE) anomaly detection algorithm. We demonstrate a 2x signal efficiency gain compared with traditional subjettiness-based jet selection. Furthermore, with an eye to the future deployment to trigger systems, we propose the CLIP-VAE, which reduces the inference-time cost of anomaly detection by using the KL-divergence loss as the anomaly score, resulting in a 2x acceleration in latency and reducing the caching requirement.