P. Harris

Recent results at the Large Hadron Collider (LHC) have pointed to enhanced physics capabilities through the improvement of the real-time event processing techniques. Machine learning methods are ubiquitous and have proven to be very powerful in LHC physics, and particle physics as a whole. However, exploration of the use of such techniques in low-latency, low-power FPGA hardware has only just begun. FPGA-based trigger and data acquisition (DAQ) systems have extremely low, sub-microsecond latency requirements that are unique to particle physics. We present a case study for neural network inference in FPGAs focusing on a classifier for jet substructure which would enable, among many other physics scenarios, searches for new dark sector particles and novel measurements of the Higgs boson. While we focus on a specific example, the lessons are far-reaching. We develop a package based on High-Level Synthesis (HLS) called hls4ml to build machine learning models in FPGAs. The use of HLS increases accessibility across a broad user community and allows for a drastic decrease in firmware development time. We map out FPGA resource usage and latency versus neural network hyperparameters to identify the problems in particle physics that would benefit from performing neural network inference with FPGAs. For our example jet substructure model, we fit well within the available resources of modern FPGAs with a latency on the scale of 100 ns.

We propose a new method for pileup mitigation by implementing “pileup per particle identification” (PUPPI). For each particle we first define a local shape α which probes the collinear versus soft diffuse structure in the neighborhood of the particle. The former is indicative of particles originating from the hard scatter and the latter of particles originating from pileup interactions. The distribution of α for charged pileup, assumed as a proxy for all pileup, is used on an event-by-event basis to calculate a weight for each particle. The weights describe the degree to which particles are pileup-like and are used to rescale their four-momenta, superseding the need for jet-based corrections. Furthermore, the algorithm flexibly allows combination with other, possibly experimental, probabilistic information associated with particles such as vertexing and timing performance. We demonstrate the algorithm improves over existing methods by looking at jet p T and jet mass. We also find an improvement on non-jet quantities like missing transverse energy.

Jet substructure has emerged to play a central role at the Large Hadron Collider, where it has provided numerous innovative ways to search for new physics and to probe the Standard Model, particularly in extreme regions of phase space. In this article we focus on a review of the development and use of state-of-the-art jet substructure techniques by the ATLAS and CMS experiments.

This document is the final report of the ATLAS-CMS Dark Matter Forum, a forum organized by the ATLAS and CMS collaborations with the participation of experts on theories of Dark Matter, to select a minimal basis set of dark matter simplified models that should support the design of the early LHC Run-2 searches. A prioritized, compact set of benchmark models is proposed, accompanied by studies of the parameter space of these models and a repository of generator implementations. This report also addresses how to apply the Effective Field Theory formalism for collider searches and present the results of such interpretations.

A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. In order to develop and benchmark new anomaly detection methods within this framework, it is essential to have standard datasets. To this end, we have created the LHC Olympics 2020, a community challenge accompanied by a set of simulated collider events. Participants in these Olympics have developed their methods using an R&D dataset and then tested them on black boxes: datasets with an unknown anomaly (or not). Methods made use of modern machine learning tools and were based on unsupervised learning (autoencoders, generative adversarial networks, normalizing flows), weakly supervised learning, and semi-supervised learning. This paper will review the LHC Olympics 2020 challenge, including an overview of the competition, a description of methods deployed in the competition, lessons learned from the experience, and implications for data analyses with future datasets as well as future colliders.

Over the past decade, a large number of jet substructure observables have been proposed in the literature, and explored at the LHC experiments. Such observables attempt to utilize the internal structure of jets in order to distinguish those initiated by quarks, gluons, or by boosted heavy objects, such as top quarks and W bosons. This report, originating from and motivated by the BOOST2013 workshop, presents original particle-level studies that aim to improve our understanding of the relationships between jet substructure observables, their complementarity, and their dependence on the underlying jet properties, particularly the jet radius and jet transverse momentum. This is explored in the context of quark/gluon discrimination, boosted W boson tagging and boosted top quark tagging.

We explore the scale-dependence and correlations of jet substructure observables to improve upon existing techniques in the identification of highly Lorentz-boosted objects. Modified observables are designed to remove correlations from existing theoretically well-understood observables, providing practical advantages for experimental measurements and searches for new phenomena. We study such observables in $W$ jet tagging and provide recommendations for observables based on considerations beyond signal and background efficiencies.

We outline and investigate a set of benchmark simplified models with the aim of providing a minimal simple framework for an interpretation of the existing and forthcoming searches of dark matter particles at the LHC. The simplified models we consider provide microscopic QFT descriptions of interactions between the Standard Model partons and the dark sector particles mediated by the four basic types of messenger fields: scalar, pseudo-scalar, vector or axial-vector. Our benchmark models are characterised by four to five parameters, including the mediator mass and width, the dark matter mass and an effective coupling(s). In the gluon fusion production channel we resolve the top-quark in the loop and compute full top-mass effects for scalar and pseudo-scalar messengers. We show the LHC limits and reach at 8 and 14 TeV for models with all four messenger types. We also outline the complementarity of direct detection, indirect detection and LHC bounds for dark matter searches. Finally, we investigate the effects which arise from extending the simplified model to include potential new physics contributions in production. Using the scalar mediator as an example we study the impact of heavy new physics loops which interfere with the top mediated loops. Our computations are performed within the MCFM framework and we provide fully flexible public Monte Carlo implementation.

We present the implementation of binary and ternary neural networks in the hls4ml library, designed to automatically convert deep neural network models to digital circuits with field-programmable gate arrays (FPGA) firmware. Starting from benchmark models trained with floating point precision, we investigate different strategies to reduce the network's resource consumption by reducing the numerical precision of the network parameters to binary or ternary. We discuss the trade-off between model accuracy and resource consumption. In addition, we show how to balance between latency and accuracy by retaining full precision on a selected subset of network components. As an example, we consider two multiclass classification tasks: handwritten digit recognition with the MNIST data set and jet identification with simulated proton-proton collisions at the CERN Large Hadron Collider. The binary and ternary implementation has similar performance to the higher precision implementation while using drastically fewer FPGA resources.

Abstract We introduce an automated tool for deploying ultra low-latency, low-power deep neural networks with convolutional layers on field-programmable gate arrays (FPGAs). By extending the hls4ml library, we demonstrate an inference latency of 5 µ s using convolutional architectures, targeting microsecond latency applications like those at the CERN Large Hadron Collider. Considering benchmark models trained on the Street View House Numbers Dataset, we demonstrate various methods for model compression in order to fit the computational constraints of a typical FPGA device used in trigger and data acquisition systems of particle detectors. In particular, we discuss pruning and quantization-aware training, and demonstrate how resource utilization can be significantly reduced with little to no loss in model accuracy. We show that the FPGA critical resource consumption can be reduced by 97% with zero loss in model accuracy, and by 99% when tolerating a 6% accuracy degradation.

Graph neural networks have been shown to achieve excellent performance for several crucial tasks in particle physics, such as charged particle tracking, jet tagging, and clustering. An important domain for the application of these networks is the FGPA-based first layer of real-time data filtering at the CERN Large Hadron Collider, which has strict latency and resource constraints. We discuss how to design distance-weighted graph networks that can be executed with a latency of less than 1$\mu\mathrm{s}$ on an FPGA. To do so, we consider a representative task associated to particle reconstruction and identification in a next-generation calorimeter operating at a particle collider. We use a graph network architecture developed for such purposes, and apply additional simplifications to match the computing constraints of Level-1 trigger systems, including weight quantization. Using the $\mathtt{hls4ml}$ library, we convert the compressed models into firmware to be implemented on an FPGA. Performance of the synthesized models is presented both in terms of inference accuracy and resource usage.

In this community review report, we discuss applications and techniques for fast machine learning (ML) in science-the concept of integrating powerful ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms. We also present overlapping challenges across the multiple scientific domains where common solutions can be found. This community report is intended to give plenty of examples and inspiration for scientific discovery through integrated and accelerated ML solutions. This is followed by a high-level overview and organization of technical advances, including an abundance of pointers to source material, which can enable these breakthroughs.

A program to build a lepton-collider Higgs factory, to precisely measure the couplings of the Higgs boson to other particles, followed by a higher energy run to establish the Higgs self-coupling and expand the new physics reach, is widely recognized as a primary focus of modern particle physics. We propose a strategy that focuses on a new technology and preliminary estimates suggest that can lead to a compact, affordable machine. New technology investigations will provide much needed enthusiasm for our field, resulting in trained workforce. This cost-effective, compact design, with technologies useful for a broad range of other accelerator applications, could be realized as a project in the US. Its technology innovations, both in the accelerator and the detector, will offer unique and exciting opportunities to young scientists. Moreover, cost effective compact designs, broadly applicable to other fields of research, are more likely to obtain financial support from our funding agencies.

Abstract Construction of an e + e - Higgs factory has been identified as a major goal for particle physics. Such a collider will offer precise measurements of the Higgs bosons couplings to other particles. A Higgs factory extendable in energy can also establish the Higgs self-coupling, measure the Higgs coupling to the top quark, and expand the reach to probe new phenomena. We propose a strategy for an energy-extendable Higgs factory based on a new linear accelerator technology. This strategy offers a compact and cost-effective design that could be realized as an accelerator project in the US. The core technologies to be developed have broad applications to accelerators for medicine and for X-ray science. The challenge of realizing these technologies will offer unique and exciting opportunities to young scientists.

Observations of accretion disks around young brown dwarfs have led to the speculation that they may form planetary systems similar to normal stars. While there have been several detections of planetary-mass objects around brown dwarfs (2MASS 1207-3932 and 2MASS 0441-2301), these companions have relatively large mass ratios and projected separations, suggesting that they formed in a manner analogous to stellar binaries. We present the discovery of a planetary-mass object orbiting a field brown dwarf via gravitational microlensing, OGLE-2012-BLG-0358Lb. The system is a low secondary/primary mass ratio (0.080 +- 0.001), relatively tightly-separated (~0.87 AU) binary composed of a planetary-mass object with 1.9 +- 0.2 Jupiter masses orbiting a brown dwarf with a mass 0.022 M_Sun. The relatively small mass ratio and separation suggest that the companion may have formed in a protoplanetary disk around the brown dwarf host, in a manner analogous to planets.

Microlensing detections of cool planets are important for the construction of an unbiased sample to estimate the frequency of planets beyond the snow line, which is where giant planets are thought to form according to the core accretion theory of planet formation. In this paper, we report the discovery of a giant planet detected from the analysis of the light curve of a high-magnification microlensing event MOA-2010-BLG-477. The measured planet-star mass ratio is $q=(2.181\pm0.004)\times 10^{-3}$ and the projected separation is $s=1.1228\pm0.0006$ in units of the Einstein radius. The angular Einstein radius is unusually large $\theta_{\rm E}=1.38\pm 0.11$ mas. Combining this measurement with constraints on the "microlens parallax" and the lens flux, we can only limit the host mass to the range $0.13<M/M_\odot<1.0$. In this particular case, the strong degeneracy between microlensing parallax and planet orbital motion prevents us from measuring more accurate host and planet masses. However, we find that adding Bayesian priors from two effects (Galactic model and Keplerian orbit) each independently favors the upper end of this mass range, yielding star and planet masses of $M_*=0.67^{+0.33}_{-0.13}\ M_\odot$ and $m_p=1.5^{+0.8}_{-0.3}\ M_{\rm JUP}$ at a distance of $D=2.3\pm0.6$ kpc, and with a semi-major axis of $a=2^{+3}_{-1}$ AU. Finally, we show that the lens mass can be determined from future high-resolution near-IR adaptive optics observations independently from two effects, photometric and astrometric.

Machine Learning is a powerful tool to reveal and exploit correlations in a multi-dimensional parameter space. Making predictions from such correlations is a highly non-trivial task, in particular when the details of the underlying dynamics of a theoretical model are not fully understood. Using adversarial networks, we include a priori known sources of systematic and theoretical uncertainties during the training. This paves the way to a more reliable event classification on an event-by-event basis, as well as novel approaches to perform parameter fits of particle physics data. We demonstrate the benefits of the method explicitly in an example considering effective field theory extensions of Higgs boson production in association with jets.

Abstract Studies of fully-reconstructed jets in heavy-ion collisions aim at extracting thermodynamical and transport properties of hot and dense QCD matter. Recently, a plethora of new jet substructure observables have been theoretically and experimentally developed that provide novel precise insights on the modifications of the parton radiation pattern induced by a QCD medium. This report, summarizing the main lines of discussion at the 5th Heavy Ion Jet Workshop and CERN TH institute ‘Novel tools and observables for jet physics in heavy-ion collisions’ in 2017, presents a first attempt at outlining a strategy for isolating and identifying the relevant physical processes that are responsible for the observed medium-induced jet modifications. These studies combine theory insights, based on the Lund parton splitting map, with sophisticated jet reconstruction techniques, including grooming and background subtraction algorithms.

This document summarises the proposal of the LHC Dark Matter Working Group on how to present LHC results on s-channel simplified dark matter models and to compare them to direct (indirect) detection experiments.

Large-scale particle physics experiments face challenging demands for high-throughput computing resources both now and in the future. New heterogeneous computing paradigms on dedicated hardware with increased parallelization, such as Field Programmable Gate Arrays (FPGAs), offer exciting solutions with large potential gains. The growing applications of machine learning algorithms in particle physics for simulation, reconstruction, and analysis are naturally deployed on such platforms. We demonstrate that the acceleration of machine learning inference as a web service represents a heterogeneous computing solution for particle physics experiments that potentially requires minimal modification to the current computing model. As examples, we retrain the ResNet-50 convolutional neural network to demonstrate state-of-the-art performance for top quark jet tagging at the LHC and apply a ResNet-50 model with transfer learning for neutrino event classification. Using Project Brainwave by Microsoft to accelerate the ResNet-50 image classification model, we achieve average inference times of 60 (10) ms with our experimental physics software framework using Brainwave as a cloud (edge or on-premises) service, representing an improvement by a factor of approximately 30 (175) in model inference latency over traditional CPU inference in current experimental hardware. A single FPGA service accessed by many CPUs achieves a throughput of 600–700 inferences per second using an image batch of one, comparable to large batch-size GPU throughput and significantly better than small batch-size GPU throughput. Deployed as an edge or cloud service for the particle physics computing model, coprocessor accelerators can have a higher duty cycle and are potentially much more cost-effective.

We describe the implementation of Boosted Decision Trees in the hls4ml library, which allows the translation of a trained model into FPGA firmware through an automated conversion process. Thanks to its fully on-chip implementation, hls4ml performs inference of Boosted Decision Tree models with extremely low latency. With a typical latency less than 100 ns, this solution is suitable for FPGA-based real-time processing, such as in the Level-1 Trigger system of a collider experiment. These developments open up prospects for physicists to deploy BDTs in FPGAs for identifying the origin of jets, better reconstructing the energies of muons, and enabling better selection of rare signal processes.

Despite advances in the programmable logic capabilities of modern trigger systems, a significant bottleneck remains in the amount of data to be transported from the detector to off-detector logic where trigger decisions are made. We demonstrate that a neural network autoencoder model can be implemented in a radiation tolerant ASIC to perform lossy data compression alleviating the data transmission problem while preserving critical information of the detector energy profile. For our application, we consider the high-granularity calorimeter from the CMS experiment at the CERN Large Hadron Collider. The advantage of the machine learning approach is in the flexibility and configurability of the algorithm. By changing the neural network weights, a unique data compression algorithm can be deployed for each sensor in different detector regions, and changing detector or collider conditions. To meet area, performance, and power constraints, we perform a quantization-aware training to create an optimized neural network hardware implementation. The design is achieved through the use of high-level synthesis tools and the hls4ml framework, and was processed through synthesis and physical layout flows based on a LP CMOS 65 nm technology node. The flow anticipates 200 Mrad of ionizing radiation to select gates, and reports a total area of 3.6 mm^2 and consumes 95 mW of power. The simulated energy consumption per inference is 2.4 nJ. This is the first radiation tolerant on-detector ASIC implementation of a neural network that has been designed for particle physics applications.

To enable the reusability of massive scientific datasets by humans and machines, researchers aim to adhere to the principles of findability, accessibility, interoperability, and reusability (FAIR) for data and artificial intelligence (AI) models. This article provides a domain-agnostic, step-by-step assessment guide to evaluate whether or not a given dataset meets these principles. We demonstrate how to use this guide to evaluate the FAIRness of an open simulated dataset produced by the CMS Collaboration at the CERN Large Hadron Collider. This dataset consists of Higgs boson decays and quark and gluon background, and is available through the CERN Open Data Portal. We use additional available tools to assess the FAIRness of this dataset, and incorporate feedback from members of the FAIR community to validate our results. This article is accompanied by a Jupyter notebook to visualize and explore this dataset. This study marks the first in a planned series of articles that will guide scientists in the creation of FAIR AI models and datasets in high energy particle physics.

A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data. The principles were also meant to apply to other digital assets, at a high level, and over time, the FAIR guiding principles have been re-interpreted or extended to include the software, tools, algorithms, and workflows that produce data. FAIR principles are now being adapted in the context of AI models and datasets. Here, we present the perspectives, vision, and experiences of researchers from different countries, disciplines, and backgrounds who are leading the definition and adoption of FAIR principles in their communities of practice, and discuss outcomes that may result from pursuing and incentivizing FAIR AI research. The material for this report builds on the FAIR for AI Workshop held at Argonne National Laboratory on June 7, 2022.

We investigate the constraints that the LHC can set on a 126 GeV Higgs boson that is an admixture of $CP$ eigenstates. Traditional analyses rely on Higgs couplings to massive vector bosons, which are suppressed for $CP$-odd couplings, so that these analyses have limited sensitivity. Instead we focus on Higgs production in gluon fusion, which occurs at the same order in ${\ensuremath{\alpha}}_{S}$ for both $CP$-even and -odd Higgs couplings to top quarks. We study the Higgs plus two jet final state followed by Higgs decay into a pair of tau leptons. We show that using the 8 TeV data set it is possible to rule out the pure $CP$-odd hypothesis in this channel alone at nearly 95% C.L, assuming that the Higgs is $CP$-even. We also provide projected limits for the 14 TeV LHC run.

We present an analysis of the anomalous microlensing event, MOA-2010-BLG-073, announced by the Microlensing Observations in Astrophysics survey on 2010-03-18. This event was remarkable because the source was previously known to be photometrically variable. Analyzing the pre-event source lightcurve, we demonstrate that it is an irregular variable over time scales >200d. Its dereddened color, $(V-I)_{S,0}$, is 1.221$\pm$0.051mag and from our lens model we derive a source radius of 14.7$\pm$1.3 $R_{\odot}$, suggesting that it is a red giant star. We initially explored a number of purely microlensing models for the event but found a residual gradient in the data taken prior to and after the event. This is likely to be due to the variability of the source rather than part of the lensing event, so we incorporated a slope parameter in our model in order to derive the true parameters of the lensing system. We find that the lensing system has a mass ratio of q=0.0654$\pm$0.0006. The Einstein crossing time of the event, $T_{\rm{E}}=44.3$\pm$0.1d, was sufficiently long that the lightcurve exhibited parallax effects. In addition, the source trajectory relative to the large caustic structure allowed the orbital motion of the lens system to be detected. Combining the parallax with the Einstein radius, we were able to derive the distance to the lens, $D_L$=2.8$\pm$0.4kpc, and the masses of the lensing objects. The primary of the lens is an M-dwarf with $M_{L,p}$=0.16$\pm0.03M_{\odot}$ while the companion has $M_{L,s}$=11.0$\pm2.0M_{\rm{J}}$ putting it in the boundary zone between planets and brown dwarfs.

Weakly-coupled TeV-scale particles may mediate the interactions between normal matter and dark matter. If so, the LHC would produce dark matter through these mediators, leading to the familiar "mono-X" search signatures, but the mediators would also produce signals without missing momentum via the same vertices involved in their production. This document from the LHC Dark Matter Working Group suggests how to compare searches for these two types of signals in case of vector and axial-vector mediators, based on a workshop that took place on September 19/20, 2016 and subsequent discussions. These suggestions include how to extend the spin-1 mediated simplified models already in widespread use to include lepton couplings. This document also provides analytic calculations of the relic density in the simplified models and reports an issue that arose when ATLAS and CMS first began to use preliminary numerical calculations of the dark matter relic density in these models.

We analyze the planetary microlensing event MOA-2010-BLG-328. The best fit yields host and planetary masses of Mh = 0.11+/-0.01 M_{sun} and Mp = 9.2+/-2.2M_Earth, corresponding to a very late M dwarf and sub-Neptune-mass planet, respectively. The system lies at DL = 0.81 +/- 0.10 kpc with projected separation r = 0.92 +/- 0.16 AU. Because of the host's a-priori-unlikely close distance, as well as the unusual nature of the system, we consider the possibility that the microlens parallax signal, which determines the host mass and distance, is actually due to xallarap (source orbital motion) that is being misinterpreted as parallax. We show a result that favors the parallax solution, even given its close host distance. We show that future high-resolution astrometric measurements could decisively resolve the remaining ambiguity of these solutions.

This White Paper is an input to the ongoing discussion about the extension and refinement of simplified Dark Matter (DM) models. It is not intended as a comprehensive review of the discussed subjects, but instead summarises ideas and concepts arising from a brainstorming workshop that can be useful when defining the next generation of simplified DM models (SDMM). In this spirit, based on two concrete examples, we show how existing SDMM can be extended to provide a more accurate and comprehensive framework to interpret and characterise collider searches. In the first example we extend the canonical SDMM with a scalar mediator to include mixing with the Higgs boson. We show that this approach not only provides a better description of the underlying kinematic properties that a complete model would possess, but also offers the option of using this more realistic class of scalar mixing models to compare and combine consistently searches based on different experimental signatures. The second example outlines how a new physics signal observed in a visible channel can be connected to DM by extending a simplified model including effective couplings. In the next part of the White Paper we outline other interesting options for SDMM that could be studied in more detail in the future. Finally, we review important aspects of supersymmetric models for DM and use them to propose how to develop more complete SDMMs. This White Paper is a summary of the brainstorming meeting "Next generation of simplified Dark Matter models" that took place at Imperial College, London on May 6, 2016, and corresponding follow-up studies on selected subjects.

Discoveries of new phenomena often involve a dedicated search for a hypothetical physics signature. Recently, novel deep learning techniques have emerged for anomaly detection in the absence of a signal prior. However, by ignoring signal priors, the sensitivity of these approaches is significantly reduced. We present a new strategy dubbed Quasi Anomalous Knowledge (QUAK), whereby we introduce alternative signal priors that capture some of the salient features of new physics signatures, allowing for the recovery of sensitivity even when the alternative signal is incorrect. This approach can be applied to a broad range of physics models and neural network architectures. In this paper, we apply QUAK to anomaly detection of new physics events at the CERN Large Hadron Collider utilizing variational autoencoders with normalizing flow.

A planetary microlensing signal is generally characterized by a short-term perturbation to the standard single lensing light curve. A subset of binary-source events can produce perturbations that mimic planetary signals, thereby introducing an ambiguity between the planetary and binary-source interpretations. In this paper, we present the analysis of the microlensing event MOA-2012-BLG-486, for which the light curve exhibits a short-lived perturbation. Routine modeling not considering data taken in different passbands yields a best-fit planetary model that is slightly preferred over the best-fit binary-source model. However, when allowed for a change in the color during the perturbation, we find that the binary-source model yields a significantly better fit and thus the degeneracy is clearly resolved. This event not only signifies the importance of considering various interpretations of short-term anomalies, but also demonstrates the importance of multi-band data for checking the possibility of false-positive planetary signals.

In the next decade, the demands for computing in large scientific experiments are expected to grow tremendously. During the same time period, CPU performance increases will be limited. At the CERN Large Hadron Collider (LHC), these two issues will confront one another as the collider is upgraded for high luminosity running. Alternative processors such as graphics processing units (GPUs) can resolve this confrontation provided that algorithms can be sufficiently accelerated. In many cases, algorithmic speedups are found to be largest through the adoption of deep learning algorithms. We present a comprehensive exploration of the use of GPU-based hardware acceleration for deep learning inference within the data reconstruction workflow of high energy physics. We present several realistic examples and discuss a strategy for the seamless integration of coprocessors so that the LHC can maintain, if not exceed, its current performance throughout its running.

This document summarises the proposal of the LHC Dark Matter Working Group on how to present LHC results on s-channel simplified dark matter models and to compare them to direct (indirect) detection experiments.

Machine learning algorithms are becoming increasingly prevalent and performant in the reconstruction of events in accelerator-based neutrino experiments. These sophisticated algorithms can be computationally expensive. At the same time, the data volumes of such experiments are rapidly increasing. The demand to process billions of neutrino events with many machine learning algorithm inferences creates a computing challenge. We explore a computing model in which heterogeneous computing with GPU coprocessors is made available as a web service. The coprocessors can be efficiently and elastically deployed to provide the right amount of computing for a given processing task. With our approach, Services for Optimized Network Inference on Coprocessors (SONIC), we integrate GPU acceleration specifically for the ProtoDUNE-SP reconstruction chain without disrupting the native computing workflow. With our integrated framework, we accelerate the most time-consuming task, track and particle shower hit identification, by a factor of 17. This results in a factor of 2.7 reduction in the total processing time when compared with CPU-only production. For this particular task, only 1 GPU is required for every 68 CPU threads, providing a cost-effective solution.

A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. In order to develop and benchmark new anomaly detection methods within this framework, it is essential to have standard datasets. To this end, we have created the LHC Olympics 2020, a community challenge accompanied by a set of simulated collider events. Participants in these Olympics have developed their methods using an R&D dataset and then tested them on black boxes: datasets with an unknown anomaly (or not). This paper will review the LHC Olympics 2020 challenge, including an overview of the competition, a description of methods deployed in the competition, lessons learned from the experience, and implications for data analyses with future datasets as well as future colliders.

In this paper, we present a method of embedding physics data manifolds with metric structure into lower dimensional spaces with simpler metrics, such as Euclidean and Hyperbolic spaces. We then demonstrate that it can be a powerful step in the data analysis pipeline for many applications. Using progressively more realistic simulated collisions at the Large Hadron Collider, we show that this embedding approach learns the underlying latent structure. With the notion of volume in Euclidean spaces, we provide for the first time a viable solution to quantifying the true search capability of model agnostic search algorithms in collider physics (i.e. anomaly detection). Finally, we discuss how the ideas presented in this paper can be employed to solve many practical challenges that require the extraction of physically meaningful representations from information in complex high dimensional datasets.

We develop and study FPGA implementations of algorithms for charged particle tracking based on graph neural networks. The two complementary FPGA designs are based on OpenCL, a framework for writing programs that execute across heterogeneous platforms, and hls4ml, a high-level-synthesis-based compiler for neural network to firmware conversion. We evaluate and compare the resource usage, latency, and tracking performance of our implementations based on a benchmark dataset. We find a considerable speedup over CPU-based execution is possible, potentially enabling such algorithms to be used effectively in future computing workflows and the FPGA-based Level-1 trigger at the CERN Large Hadron Collider.

Contrary to the use of genetic programming, the neural network approach to symbolic regression can scale well with high input dimension and leverage gradient methods for faster equation searching. Common ways of constraining expression complexity have relied on multistage pruning methods with fine-tuning, but these often lead to significant performance loss. In this work, we propose SymbolNet, a neural network approach to symbolic regression in a novel framework that enables dynamic pruning of model weights, input features, and mathematical operators in a single training, where both training loss and expression complexity are optimized simultaneously. We introduce a sparsity regularization term per pruning type, which can adaptively adjust its own strength and lead to convergence to a target sparsity level. In contrast to most existing symbolic regression methods that cannot efficiently handle datasets with more than $O$(10) inputs, we demonstrate the effectiveness of our model on the LHC jet tagging task (16 inputs), MNIST (784 inputs), and SVHN (3072 inputs).

Self-Supervised Learning (SSL) is at the core of training modern large machine learning models, providing a scheme for learning powerful representations that can be used in a variety of downstream tasks. However, SSL strategies must be adapted to the type of training data and downstream tasks required. We propose RS3L, a novel simulation-based SSL strategy that employs a method of re-simulation to drive data augmentation for contrastive learning. By intervening in the middle of the simulation process and re-running simulation components downstream of the intervention, we generate multiple realizations of an event, thus producing a set of augmentations covering all physics-driven variations available in the simulator. Using experiments from high-energy physics, we explore how this strategy may enable the development of a foundation model; we show how R3SL pre-training enables powerful performance in downstream tasks such as discrimination of a variety of objects and uncertainty mitigation. In addition to our results, we make the RS3L dataset publicly available for further studies on how to improve SSL strategies.

If the recently discovered Higgs boson's couplings deviate from the Standard Model expectation, we may anticipate new resonant physics in the weak boson fusion channels resulting from high scale unitarity sum rules of longitudinal gauge boson scattering. Motivated by excesses in analyses of multi-leptons $+$ missing energy $+$ jets final states during run 1, we perform a phenomenological investigation of these channels at the LHC bounded by current Higgs coupling constraints. Such an approach constrains the prospects to observe such new physics at the LHC as a function of very few and generic parameters and allows the investigation of the strong requirement of probability conservation in the electroweak sector to high energies.

We investigate the reach of the LHC Run 2 and that of a future circular hadron collider with up to 100 TeV center of mass energy for the exploration of potential dark matter sectors. These dark sectors are conveniently and broadly described by simplified models. The simplified models we consider provide microscopic descriptions of interactions between the standard model partons and the dark sector particles mediated by the four basic types of ($s$-channel) messenger fields: scalar, pseudoscalar, vector or axial-vector. Our analysis extends and updates the previously available results for the LHC at 8 and 14 TeV to 100 TeV for models with all four messenger types. We revisit and improve the analysis at 14 TeV, by studying a variety of analysis techniques, concluding that the most discriminating variables correspond to the missing transverse energy and the azimuthal angle between jets in the final state. Going to 100 TeV, the limits on simplified models of dark matter are enhanced significantly, in particular for heavier mediators and dark sector particles, for which the available phase space at the LHC is restricted. The possibility of a 100 TeV collider provides an unprecedented coverage of the dark sector basic parameters and a unique opportunity to pin down the particle nature of dark matter and its interactions with the standard model.

Abstract Recurrent neural networks have been shown to be effective architectures for many tasks in high energy physics, and thus have been widely adopted. Their use in low-latency environments has, however, been limited as a result of the difficulties of implementing recurrent architectures on field-programmable gate arrays (FPGAs). In this paper we present an implementation of two types of recurrent neural network layers—long short-term memory and gated recurrent unit—within the hls4ml framework. We demonstrate that our implementation is capable of producing effective designs for both small and large models, and can be customized to meet specific design requirements for inference latencies and FPGA resources. We show the performance and synthesized designs for multiple neural networks, many of which are trained specifically for jet identification tasks at the CERN Large Hadron Collider.

This work introduces a highly efficient implementation of the transformer architecture on a Field-Programmable Gate Array (FPGA) by using the \texttt{hls4ml} tool. Given the demonstrated effectiveness of transformer models in addressing a wide range of problems, their application in experimental triggers within particle physics becomes a subject of significant interest. In this work, we have implemented critical components of a transformer model, such as multi-head attention and softmax layers. To evaluate the effectiveness of our implementation, we have focused on a particle physics jet flavor tagging problem, employing a public dataset. We recorded latency under 2 $\mu$s on the Xilinx UltraScale+ FPGA, which is compatible with hardware trigger requirements at the CERN Large Hadron Collider experiments.

Recent studies have shown promising results for track finding in dense environments using Graph Neural Network (GNN)-based algorithms. However, GNN-based track finding is computationally slow on CPUs, necessitating the use of coprocessors to accelerate the inference time. Additionally, the large input graph size demands a large device memory for efficient computation, a requirement not met by all computing facilities used for particle physics experiments, particularly those lacking advanced GPUs. Furthermore, deploying the GNN-based track-finding algorithm in a production environment requires the installation of all dependent software packages, exclusively utilized by this algorithm. These computing challenges must be addressed for the successful implementation of GNN-based track-finding algorithm into production settings. In response, we introduce a ``GNN-based tracking as a service'' approach, incorporating a custom backend within the NVIDIA Triton inference server to facilitate GNN-based tracking. This paper presents the performance of this approach using the Perlmutter supercomputer at NERSC.

Abstract The RHIC interaction rate at sPHENIX will reach around 3 MHz in pp collisions and requires the detector readout to reject events by a factor of over 200 to fit the DAQ bandwidth of 15 kHz. Some critical measurements, such as heavy flavor production in pp collisions, often require the analysis of particles produced at low momentum. This prohibits adopting the traditional approach, where data rates are reduced through triggering on rare high momentum probes. We explore a new approach based on real-time AI technology, adopt an FPGA-based implementation using a custom designed FELIX-712 board with the Xilinx Kintex Ultrascale FPGA, and deploy the system in the detector readout electronics loop for real-time trigger decision.

The promise of multi-messenger astronomy relies on the rapid detection of gravitational waves at very low latencies ($\mathcal{O}$(1\,s)) in order to maximize the amount of time available for follow-up observations. In recent years, neural-networks have demonstrated robust non-linear modeling capabilities and millisecond-scale inference at a comparatively small computational footprint, making them an attractive family of algorithms in this context. However, integration of these algorithms into the gravitational-wave astrophysics research ecosystem has proven non-trivial. Here, we present the first fully machine learning-based pipeline for the detection of gravitational waves from compact binary coalescences (CBCs) running in low-latency. We demonstrate this pipeline to have a fraction of the latency of traditional matched filtering search pipelines while achieving state-of-the-art sensitivity to higher-mass stellar binary black holes.

Abstract Matched-filtering detection techniques for gravitational-wave (GW) signals in ground-based interferometers rely on having well-modeled templates of the GW emission. Such techniques have been traditionally used in searches for compact binary coalescences (CBCs), and have been employed in all known GW detections so far. However, interesting science cases aside from compact mergers do not yet have accurate enough modeling to make matched filtering possible, including core-collapse supernovae and sources where stochasticity may be involved. Therefore the development of techniques to identify sources of these types is of significant interest. In this paper, we present a method of anomaly detection based on deep recurrent autoencoders to enhance the search region to unmodeled transients. We use a semi-supervised strategy that we name “Gravitational Wave Anomalous Knowledge” (GWAK). While the semi-supervised nature of the problem comes with a cost in terms of accuracy as compared to supervised techniques, there is a qualitative advantage in generalizing experimental sensitivity beyond pre-computed signal templates. We construct a low-dimensional embedded space using the GWAK method, capturing the physical signatures of distinct signals on each axis of the space. By introducing signal priors that capture some of the salient features of GW signals, we allow for the recovery of sensitivity even when an unmodeled anomaly is encountered. We show that regions of the GWAK space can identify CBCs, detector glitches and also a variety of unmodeled astrophysical sources.&amp;#xD;

The findable, accessible, interoperable, and reusable (FAIR) data principles serve as a framework for examining, evaluating, and improving data sharing to advance scientific endeavors. There is an emerging trend to adapt these principles for machine learning models—algorithms that learn from data without specific coding—and, more generally, AI models, due to AI’s swiftly growing impact on scientific and engineering sectors. In this paper, we propose a practical definition of the FAIR principles for AI models and provide a template program for their adoption. We exemplify this strategy with an implementation from high-energy physics, where a graph neural network is employed to detect Higgs bosons decaying into two bottom quarks.

We analyze MOA-2010-BLG-311, a high magnification (Amax > 600) microlensing event with complete data coverage over the peak, making it very sensitive to planetary signals. We fit this event with both a point lens and a two-body lens model and find that the two-body lens model is a better fit but with only Δχ2 ∼ 80. The preferred mass ratio between the lens star and its companion is q = 10−3.7 ± 0.1, placing the candidate companion in the planetary regime. Despite the formal significance of the planet, we show that because of systematics in the data the evidence for a planetary companion to the lens is too tenuous to claim a secure detection. When combined with analyses of other high-magnification events, this event helps empirically define the threshold for reliable planet detection in high-magnification events, which remains an open question.

Author(s): Albert, Andreas; Backovic, Mihailo; Boveia, Antonio; Buchmueller, Oliver; Busoni, Giorgio; Roeck, Albert De; Doglioni, Caterina; DuPree, Tristan; Fairbairn, Malcolm; Genest, Marie-Helene; Gori, Stefania; Gustavino, Giuliano; Hahn, Kristian; Haisch, Ulrich; Harris, Philip C; Hayden, Dan; Ippolito, Valerio; John, Isabelle; Kahlhoefer, Felix; Kulkarni, Suchita; Landsberg, Greg; Lowette, Steven; Mawatari, Kentarou; Riotto, Antonio; Shepherd, William; Tait, Tim MP; Tolley, Emma; Tunney, Patrick; Zaldivar, Bryan; Zinser, Markus | Abstract: Weakly-coupled TeV-scale particles may mediate the interactions between normal matter and dark matter. If so, the LHC would produce dark matter through these mediators, leading to the familiar mono-X search signatures, but the mediators would also produce signals without missing momentum via the same vertices involved in their production. This document from the LHC Dark Matter Working Group suggests how to compare searches for these two types of signals in case of vector and axial-vector mediators, based on a workshop that took place on September 19/20, 2016 and subsequent discussions. These suggestions include how to extend the spin-1 mediated simplified models already in widespread use to include lepton couplings. This document also provides analytic calculations of the relic density in the simplified models and reports an issue that arose when ATLAS and CMS first began to use preliminary numerical calculations of the dark matter relic density in these models.

New jet observables are defined which characterize both fractal and scale-dependent contributions to the distribution of hadrons in a jet. These infrared safe observables, named Extended Fractal Observables (EFOs), have been applied to quark-gluon discrimination to demonstrate their potential utility. The EFOs are found to be individually discriminating and only weakly correlated to variables used in existing discriminators. Consequently, their inclusion improves discriminator performance, as here demonstrated with particle level simulation from the parton shower.

The Galactic bulge source MOA-2010-BLG-523S exhibited short-term deviations from a standard microlensing light curve near the peak of an Amax ∼ 265 high-magnification microlensing event. The deviations originally seemed consistent with expectations for a planetary companion to the principal lens. We combine long-term photometric monitoring with a previously published high-resolution spectrum taken near peak to demonstrate that this is an RS CVn variable, so that planetary microlensing is not required to explain the light-curve deviations. This is the first spectroscopically confirmed RS CVn star discovered in the Galactic bulge.

A study has been performed of the reaction ¯p→p4K± using in-flight antiprotons from 1.1 to 2.0 GeV/c incident momentum interacting with a hydrogen jet target. The reaction is dominated by the production of a pair of φ mesons. The ¯p→pφφ cross section rises sharply above threshold and then falls continuously as a function of increasing antiproton momentum. The overall magnitude of the cross section exceeds expectations from a simple application of the Okubo-Zweig-Iizuka rule by two orders of magnitude. In a fine scan around the ξ/fJ(2230) resonance, no structure is observed. A limit is set for the double branching ratio B(→ξ¯pp)×B(→ξφφ)<6×10−5 for a spin 2 resonance of M=2.235GeV and Γ=15MeV. Received 13 January 1998DOI:https://doi.org/10.1103/PhysRevD.57.5370©1998 American Physical Society

Computing needs for high energy physics are already intensive and are expected to increase drastically in the coming years. In this context, heterogeneous computing, specifically as-a-service computing, has the potential for significant gains over traditional computing models. Although previous studies and packages in the field of heterogeneous computing have focused on GPUs as accelerators, FPGAs are an extremely promising option as well. A series of workflows are developed to establish the performance capabilities of FPGAs as a service. Multiple different devices and a range of algorithms for use in high energy physics are studied. For a small, dense network, the throughput can be improved by an order of magnitude with respect to GPUs as a service. For large convolutional networks, the throughput is found to be comparable to GPUs as a service. This work represents the first open-source FPGAs-as-a-service toolkit.

Characterizing a microlensing planet is done by modeling an observed lensing light curve. In this process, it is often confronted that solutions of different lensing parameters result in similar light curves, causing difficulties in uniquely interpreting the lens system, and thus understanding the causes of different types of degeneracy is important. In this work, we show that incomplete coverage of a planetary perturbation can result in degenerate solutions even for events where the planetary signal is detected with a high level of statistical significance. We demonstrate the degeneracy for an actually observed event OGLE-2012-BLG-0455/MOA-2012-BLG-206. The peak of this high-magnification event (Amax ∼ 400) exhibits very strong deviation from a point-lens model with Δχ2 ≳ 4000 for data sets with a total of 6963 measurements. From detailed modeling of the light curve, we find that the deviation can be explained by four distinct solutions, i.e., two very different sets of solutions, each with a twofold degeneracy. While the twofold (so-called close/wide) degeneracy is well understood, the degeneracy between the radically different solutions is not previously known. The model light curves of this degeneracy differ substantially in the parts that were not covered by observation, indicating that the degeneracy is caused by the incomplete coverage of the perturbation. It is expected that the frequency of the degeneracy introduced in this work will be greatly reduced with the improvement of the current lensing survey and follow-up experiments and the advent of new surveys.

Computational demands in gravitational-wave astronomy are expected to at least double over the next five years. As kilometre-scale interferometers are brought to design sensitivity, real-time delivery of gravitational-wave alerts will become increasingly important to enable multimessenger follow-up. Here we discuss a novel implementation and deployment of deep learning inference for real-time data denoising and astrophysical source identification. This objective is accomplished using a generic inference-as-a-service model capable of adapting to the future needs of gravitational-wave data analysis. The implementation allows seamless incorporation of hardware accelerators and also enables the use of commercial or private as-a-service computing. Low-latency and offline computing in gravitational-wave astronomy addresses key challenges in scalability and reliability and provides a data analysis platform particularly optimized for deep learning applications. There is a growing need for data cleaning and source identification for gravitational-wave detectors in real time. A deep learning inference-as-a-service framework using off-the-shelf software and hardware can address these challenges in a scalable and reliable way.

Abstract The findable, accessible, interoperable, and reusable (FAIR) data principles provide a framework for examining, evaluating, and improving how data is shared to facilitate scientific discovery. Generalizing these principles to research software and other digital products is an active area of research. Machine learning models—algorithms that have been trained on data without being explicitly programmed—and more generally, artificial intelligence (AI) models, are an important target for this because of the ever-increasing pace with which AI is transforming scientific domains, such as experimental high energy physics (HEP). In this paper, we propose a practical definition of FAIR principles for AI models in HEP and describe a template for the application of these principles. We demonstrate the template’s use with an example AI model applied to HEP, in which a graph neural network is used to identify Higgs bosons decaying to two bottom quarks. We report on the robustness of this FAIR AI model, its portability across hardware architectures and software frameworks, and its interpretability.

Microlensing can provide an important tool to study binaries, especially those composed of faint or dark objects. However, accurate analysis of binary-lens light curves is often hampered by the well-known degeneracy between close (s<1) and wide (s>1) binaries, which can be very severe due to an intrinsic symmetry in the lens equation. Here s is the normalized projected binary separation. In this paper, we propose a method that can resolve the close/wide degeneracy using the effect of a lens orbital motion on lensing light curves. The method is based on the fact that the orbital effect tends to be important for close binaries while it is negligible for wide binaries. We demonstrate the usefulness of the method by applying it to an actually observed binary-lens event MOA-2011-BLG-040/OGLE-2011-BLG-0001, which suffers from severe close/wide degeneracy. From this, we are able to uniquely specify that the lens is composed of K and M-type dwarfs located at ~3.5 kpc from the Earth.

Cutting edge detectors push sensing technology by further improving spatial and temporal resolution, increasing detector area and volume, and generally reducing backgrounds and noise. This has led to a explosion of more and more data being generated in next-generation experiments. Therefore, the need for near-sensor, at the data source, processing with more powerful algorithms is becoming increasingly important to more efficiently capture the right experimental data, reduce downstream system complexity, and enable faster and lower-power feedback loops. In this paper, we discuss the motivations and potential applications for on-detector AI. Furthermore, the unique requirements of particle physics can uniquely drive the development of novel AI hardware and design tools. We describe existing modern work for particle physics in this area. Finally, we outline a number of areas of opportunity where we can advance machine learning techniques, codesign workflows, and future microelectronics technologies which will accelerate design, performance, and implementations for next generation experiments.

Abstract In this paper, we investigate how field programmable gate arrays can serve as hardware accelerators for real-time semantic segmentation tasks relevant for autonomous driving. Considering compressed versions of the ENet convolutional neural network architecture, we demonstrate a fully-on-chip deployment with a latency of 4.9 ms per image, using less than 30% of the available resources on a Xilinx ZCU102 evaluation board. The latency is reduced to 3 ms per image when increasing the batch size to ten, corresponding to the use case where the autonomous vehicle receives inputs from multiple cameras simultaneously. We show, through aggressive filter reduction and heterogeneous quantization-aware training, and an optimized implementation of convolutional layers, that the power consumption and resource utilization can be significantly reduced while maintaining accuracy on the Cityscapes dataset.

We describe progress on a novel process-chain being used to produce eight 1.4m hexagonal segments as prototypes for the European Extremely Large Telescope - a Master Spherical Segment as a reference, and seven aspheric segments. A new pilot plant integrates a bespoke full-aperture test-tower designed and built by OpTIC Glyndwr, with a Zeeko 1.6m polishing machine. The process chain starts with aspherising hexagonal segments on the Cranfield BoX&trade; grinder, followed by smoothing, corrective-polishing and edge-rectification using the Zeeko CNC platform. The paper describes the technology and progress, and anticipates how the process-chain is expected to evolve through the seven segments to increase both process-speed and surface-quality.

Machine learning (ML) in the past decade has been one of the most popular topics of research within the computing community. Interest within the computing field ranges across all levels of the computation stack. We show this stack in Figure 1. This work introduces an open framework, called AIgean, to build and deploy machine learning (ML) algorithms on a heterogeneous cluster of devices (CPUs and FPGAs). Users can flexibly modify any layer of the machine learning stack in Figure 1 to suit their need. This allows both machine learning domain experts to focus on higher algorithmic layers, and distributed systems experts to create the communication layers below.

This White Paper is an input to the ongoing discussion about the extension and refinement of simplified Dark Matter (DM) models. Based on two concrete examples, we show how existing simplified DM models (SDMM) can be extended to provide a more accurate and comprehensive framework to interpret and characterise collider searches. In the first example we extend the canonical SDMM with a scalar mediator to include mixing with the Higgs boson. We show that this approach not only provides a better description of the underlying kinematic properties that a complete model would possess, but also offers the option of using this more realistic class of scalar mixing models to compare and combine consistently searches based on different experimental signatures. The second example outlines how a new physics signal observed in a visible channel can be connected to DM by extending a simplified model including effective couplings. This discovery scenario uses the recently observed excess in the high-mass diphoton searches of ATLAS and CMS for a case study to show that such a pragmatic approach can aid the experimental search programme to verify/falsify a potential signal and to study its underlying nature. In the next part of the White Paper we outline other interesting options for SDMM that could be studied in more detail in the future. Finally, we discuss important aspects of supersymmetric models for DM and how these could help to develop of more complete SDMM.

Over the past decade, a large number of jet substructure observables have been proposed in the literature, and explored at the LHC experiments. Such observables attempt to utilize the internal structure of jets in order to distinguish those initiated by quarks, gluons, or by boosted heavy objects, such as top quarks and W bosons. This report, originating from and motivated by the BOOST2013 workshop, presents original particle-level studies that aim to improve our understanding of the relationships between jet substructure observables, their complementarity, and their dependence on the underlying jet properties, particularly the jet radius and jet transverse momentum. This is explored in the context of quark/gluon discrimination, boosted W boson tagging and boosted top quark tagging.

Machine learning methods are ubiquitous and have proven to be very powerful in LHC physics, and particle physics as a whole. However, exploration of such techniques in low-latency, low-power FPGA (Field Programmable Gate Array) hardware has only just begun. FPGA-based trigger and data acquisition systems have extremely low, sub-microsecond latency requirements that are unique to particle physics. We present a case study for neural network inference in FPGAs focusing on a classifier for jet substructure which would enable many new physics measurements. While we focus on a specific example, the lessons are far-reaching. A compiler package is developed based on High-Level Synthesis (HLS) called HLS4ML to build machine learning models in FPGAs. The use of HLS increases accessibility across a broad user community and allows for a drastic decrease in firmware development time. We map out FPGA resource usage and latency versus neural network hyperparameters to allow for directed resource tuning in the low latency environment and assess the impact on our benchmark Physics performance scenario For our example jet substructure model, we fit well within the available resources of modern FPGAs with latency on the scale of 100~ns.

Common and community software packages, such as ROOT, Geant4 and event generators have been a key part of the LHC's success so far and continued development and optimisation will be critical in the future. The challenges are driven by an ambitious physics programme, notably the LHC accelerator upgrade to high-luminosity, HL-LHC, and the corresponding detector upgrades of ATLAS and CMS. In this document we address the issues for software that is used in multiple experiments (usually even more widely than ATLAS and CMS) and maintained by teams of developers who are either not linked to a particular experiment or who contribute to common software within the context of their experiment activity. We also give space to general considerations for future software and projects that tackle upcoming challenges, no matter who writes it, which is an area where community convergence on best practice is extremely useful.

Is Dark Matter part of a Dark Sector? The possibility of a dark sector neutral under Standard Model (SM) forces furnishes an attractive explanation for the existence of Dark Matter (DM), and is a compelling new-physics direction to explore in its own right, with potential relevance to fundamental questions as varied as neutrino masses, the hierarchy problem, and the Universe's matter-antimatter asymmetry. Because dark sectors are generically weakly coupled to ordinary matter, and because they can naturally have MeV-to-GeV masses and respect the symmetries of the SM, they are only mildly constrained by high-energy collider data and precision atomic measurements. Yet upcoming and proposed intensity-frontier experiments will offer an unprecedented window into the physics of dark sectors, highlighted as a Priority Research Direction in the 2018 Dark Matter New Initiatives (DMNI) BRN report. Support for this program -- in the form of dark-sector analyses at multi-purpose experiments, realization of the intensity-frontier experiments receiving DMNI funds, an expansion of DMNI support to explore the full breadth of DM and visible final-state signatures (especially long-lived particles) called for in the BRN report, and support for a robust dark-sector theory effort -- will enable comprehensive exploration of low-mass thermal DM milestones, and greatly enhance the potential of intensity-frontier experiments to discover dark-sector particles decaying back to SM particles.

Accessible machine learning algorithms, software, and diagnostic tools for energy-efficient devices and systems are extremely valuable across a broad range of application domains. In scientific domains, real-time near-sensor processing can drastically improve experimental design and accelerate scientific discoveries. To support domain scientists, we have developed hls4ml, an open-source software-hardware codesign workflow to interpret and translate machine learning algorithms for implementation with both FPGA and ASIC technologies. We expand on previous hls4ml work by extending capabilities and techniques towards low-power implementations and increased usability: new Python APIs, quantization-aware pruning, end-to-end FPGA workflows, long pipeline kernels for low power, and new device backends include an ASIC workflow. Taken together, these and continued efforts in hls4ml will arm a new generation of domain scientists with accessible, efficient, and powerful tools for machine-learning-accelerated discovery.

AIgean , pronounced like the sea, is an open framework to build and deploy machine learning (ML) algorithms on a heterogeneous cluster of devices (CPUs and FPGAs). We leverage two open source projects: Galapagos , for multi-FPGA deployment, and hls4ml , for generating ML kernels synthesizable using Vivado HLS. AIgean provides a full end-to-end multi-FPGA/CPU implementation of a neural network. The user supplies a high-level neural network description, and our tool flow is responsible for the synthesizing of the individual layers, partitioning layers across different nodes, as well as the bridging and routing required for these layers to communicate. If the user is an expert in a particular domain and would like to tinker with the implementation details of the neural network, we define a flexible implementation stack for ML that includes the layers of Algorithms, Cluster Deployment &amp; Communication, and Hardware. This allows the user to modify specific layers of abstraction without having to worry about components outside of their area of expertise, highlighting the modularity of AIgean . We demonstrate the effectiveness of AIgean with two use cases: an autoencoder, and ResNet-50 running across 10 and 12 FPGAs. AIgean leverages the FPGA’s strength in low-latency computing, as our implementations target batch-1 implementations.

Searches for Dark Matter at the LHC are commonly described in terms of simplied models with scalar, pseudo-scalar, vector and axial-vector mediators. In this work we explore the constraints imposed on such models from the observed Dark Matter relic abundance. We present these constraints over a range of mediator masses relevant for the LHC and for future, higher energy colliders. We additionally compute bounds from a photon line search for the decay of a pseudo-scalar mediator to di-photons that includes the mediator mass region near 750 GeV. Finally, we compare cosmological constraints with the reach of a possible future 100 TeV circular hadron collider, indirect, and direct detection experiments.

This document summarises the proposal of the LHC Dark Matter Working Group on how to present LHC results on $s$-channel simplified dark matter models and to compare them to direct (indirect) detection experiments.

This paper seeks to examine the extent and rationale of Malawian and South African campaigns incorporating America-style practices and becoming Americanised. Specifically the paper explores the existence of evidence that supports the notion of Americanisation in both Malawian and South African politics. The paper adopted a mixed methods approach. Semi structured interviews were conducted with senior politicians (campaign directors, research directors and publicity secretaries) of six political parties represented in the National Assembly and media managers. Focus groups were also conducted with media practitioners and voters on the use of experts, advisers and professionals in political campaigns in Malawi. Content analysis of secondary sources (on-line publications and newspapers) for lack of primary data was conducted for evidence of Americanisation and use of political marketing professionals (in-house or otherwise) in conducting electoral campaigns in South Africa. Results show evidence of Americanisation directly or indirectly since 1994 in both Malawi and South Africa. The Alliance for Democracy of Malawi had a team of American consultants working with its campaign team as early in 1994 when the country held its first democratic elections. The other political parties had experts from either the UK or other countries like Israel who have had contacts with their American counterparts or were influenced in some way by their work. In South Africa, the African National Congress (ANC) hired a firm that was advised by Frank Greer and Stan Greenberg, organisers of Clinton’s successful 1992 presidential campaigns. Results further show that the growth in the use of marketing and campaign professionals has been largely related to democratisation, development of the media and changes in the social-economic factors in these study countries. Through transnational diffusion, these practices were adopted and adapted to meet local needs. In fact, Americanisation supplements country-specific situations (hybridisation). The results provide useful knowledge and insight to international political marketing consultants and advisers to design country-specific campaign strategies taking into account the media environment and other country-specific factors. Further research ideas stemming from this study include the inclusion of other countries in Southern African region to make the results of uniform application to the entire African continent. The study provides useful knowledge and insight into international political marketing. The paper’s contribution is to generate knowledge about political campaign practices in Malawi and South Africa, two countries in the SADC region thereby anchoring the study of political marketing and campaigning in Africa. It is hoped that this study leads to further discussion and research on the role of political marketing and campaign professionalisation in Africa.

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We study the performance of a cloud-based GPU-accelerated inference server to speed up event reconstruction in neutrino data batch jobs. Using detector data from the ProtoDUNE experiment and employing the standard DUNE grid job submission tools, we attempt to reprocess the data by running several thousand concurrent grid jobs, a rate we expect to be typical of current and future neutrino physics experiments. We process most of the dataset with the GPU version of our processing algorithm and the remainder with the CPU version for timing comparisons. We find that a 100-GPU cloud-based server is able to easily meet the processing demand, and that using the GPU version of the event processing algorithm is two times faster than processing these data with the CPU version when comparing to the newest CPUs in our sample. The amount of data transferred to the inference server during the GPU runs can overwhelm even the highest-bandwidth network switches, however, unless care is taken to observe network facility limits or otherwise distribute the jobs to multiple sites. We discuss the lessons learned from this processing campaign and several avenues for future improvements.

Searchable abstracts of presentations at key conferences in endocrinology ISSN 1470-3947 (print) | ISSN 1479-6848 (online)

Searchable abstracts of presentations at key conferences in endocrinology ISSN 1470-3947 (print) | ISSN 1479-6848 (online)

Particle physics today faces the challenge of explaining the mystery of dark matter, the origin of matter over anti-matter in the Universe, the origin of the neutrino masses, the apparent fine-tuning of the electro-weak scale, and many other aspects of fundamental physics. Perhaps the most striking frontier to emerge in the search for answers involves new physics at mass scales comparable to familiar matter, below the GeV-scale, or even radically below, down to sub-eV scales, and with very feeble interaction strength. New theoretical ideas to address dark matter and other fundamental questions predict such feebly interacting particles (FIPs) at these scales, and indeed, existing data provide numerous hints for such possibility. A vibrant experimental program to discover such physics is under way, guided by a systematic theoretical approach firmly grounded on the underlying principles of the Standard Model. This document represents the report of the FIPs 2022 workshop, held at CERN between the 17 and 21 October 2022 and aims to give an overview of these efforts, their motivations, and the decadal goals that animate the community involved in the search for FIPs.

Real-time noise regression algorithms are crucial for maximizing the science outcomes of the LIGO, Virgo, and KAGRA gravitational-wave detectors. This includes improvements in the detectability, source localization and pre-merger detectability of signals thereby enabling rapid multi-messenger follow-up. In this paper, we demonstrate the effectiveness of \textit{DeepClean}, a convolutional neural network architecture that uses witness sensors to estimate and subtract non-linear and non-stationary noise from gravitational-wave strain data. Our study uses LIGO data from the third observing run with injected compact binary signals. As a demonstration, we use \textit{DeepClean} to subtract the noise at 60 Hz due to the power mains and their sidebands arising from non-linear coupling with other instrumental noise sources. Our parameter estimation study on the injected signals shows that \textit{DeepClean} does not do any harm to the underlying astrophysical signals in the data while it can enhances the signal-to-noise ratio of potential signals. We show that \textit{DeepClean} can be used for low-latency noise regression to produce cleaned output data at latencies $\sim 1-2$\, s. We also discuss various considerations that may be made while training \textit{DeepClean} for low latency applications.

Matched-filtering detection techniques for gravitational-wave (GW) signals in ground-based interferometers rely on having well-modeled templates of the GW emission. Such techniques have been traditionally used in searches for compact binary coalescences (CBCs), and have been employed in all known GW detections so far. However, interesting science cases aside from compact mergers do not yet have accurate enough modeling to make matched filtering possible, including core-collapse supernovae and sources where stochasticity may be involved. Therefore the development of techniques to identify sources of these types is of significant interest. In this paper, we present a method of anomaly detection based on deep recurrent autoencoders to enhance the search region to unmodeled transients. We use a semi-supervised strategy that we name Gravitational Wave Anomalous Knowledge (GWAK). While the semi-supervised nature of the problem comes with a cost in terms of accuracy as compared to supervised techniques, there is a qualitative advantage in generalizing experimental sensitivity beyond pre-computed signal templates. We construct a low-dimensional embedded space using the GWAK method, capturing the physical signatures of distinct signals on each axis of the space. By introducing signal priors that capture some of the salient features of GW signals, we allow for the recovery of sensitivity even when an unmodeled anomaly is encountered. We show that regions of the GWAK space can identify CBCs, detector glitches and also a variety of unmodeled astrophysical sources.

Accounting for inaccuracies in Monte Carlo simulations is a crucial step in any high energy physics analysis. It becomes especially important when training machine learning models, which can amplify simulation inaccuracies and introduce large discrepancies and systematic uncertainties when the model is applied to data. In this paper, we introduce a method to transform simulated events to better match data using normalizing flows, a class of deep learning-based density estimation models. Our proposal uses a technique called chained quantile morphing, which corrects a set of observables by iteratively shifting each entry according to a conditonal cumulative density function. We demonstrate the technique on a realistic particle physics dataset, and compare it to a neural network-based reweighting method. We also introduce a new contrastive learning technique to correct high dimensional particle-level inputs, which naively cannot be efficiently corrected with morphing strategies.

Unsupervised deep learning techniques are widely used to identify anomalous behaviour. The performance of such methods is a product of the amount of training data and the model size. However, the size is often a limiting factor for the deployment on resource-constrained devices. We present a novel procedure based on knowledge distillation for compressing an unsupervised anomaly detection model into a supervised deployable one and we suggest a set of techniques to improve the detection sensitivity. Compressed models perform comparably to their larger counterparts while significantly reducing the size and memory footprint.

Likelihood-free inference is quickly emerging as a powerful tool to perform fast/effective parameter estimation. We demonstrate a technique of optimizing likelihood-free inference to make it even faster by marginalizing symmetries in a physical problem. In this approach, physical symmetries, for example, time-translation are learned using joint-embedding via self-supervised learning with symmetry data augmentations. Subsequently, parameter inference is performed using a normalizing flow where the embedding network is used to summarize the data before conditioning the parameters. We present this approach on two simple physical problems and we show faster convergence in a smaller number of parameters compared to a normalizing flow that does not use a pre-trained symmetry-informed representation.

The RHIC interaction rate at sPHENIX will reach around 3 MHz in pp collisions and requires the detector readout to reject events by a factor of over 200 to fit the DAQ bandwidth of 15 kHz. Some critical measurements, such as heavy flavor production in pp collisions, often require the analysis of particles produced at low momentum. This prohibits adopting the traditional approach, where data rates are reduced through triggering on rare high momentum probes. We explore a new approach based on real-time AI technology, adopt an FPGA-based implementation using a custom designed FELIX-712 board with the Xilinx Kintex Ultrascale FPGA, and deploy the system in the detector readout electronics loop for real-time trigger decision.

Dataset for MIT 8.S50 online class details to process it can be found here: <br> https://github.com/mit-physics-data/psets/tree/main/pset2

Dataset for MIT 8.S50 online class details to process it can be found here: <br> https://github.com/mit-physics-data/psets/tree/main/pset2

Dataset used in MIT lecture details can be found here: https://github.com/mit-physics-data/lectures/tree/main/lecture13

Dataset used in MIT lecture details can be found here: https://github.com/mit-physics-data/lectures/tree/main/lecture13

The search for dark matter is one of the main science drivers of the particle and astroparticle physics communities. Determining the nature of dark matter will require a broad approach, with a range of experiments pursuing different experimental hypotheses. Within this search program, collider experiments provide insights on dark matter which are complementary to direct/indirect detection experiments and to astrophysical evidence. To compare results from a wide variety of experiments, a common theoretical framework is required. The ATLAS and CMS experiments have adopted a set of simplified models which introduce two new particles, a dark matter particle and a mediator, and whose interaction strengths are set by the couplings of the mediator. So far, the presentation of LHC and future hadron collider results has focused on four benchmark scenarios with specific coupling values within these simplified models. In this work, we describe ways to extend those four benchmark scenarios to arbitrary couplings, and release the corresponding code for use in further studies. This will allow for more straightforward comparison of collider searches to accelerator experiments that are sensitive to smaller couplings, such as those for the US Community Study on the Future of Particle Physics (Snowmass 2021), and will give a more complete picture of the coupling dependence of dark matter collider searches when compared to direct and indirect detection searches. By using semi-analytical methods to rescale collider limits, we drastically reduce the computing resources needed relative to traditional approaches based on the generation of additional simulated signal samples.

Comparisons of the coverage of current and proposed dark matter searches can help us to understand the context in which a discovery of particle dark matter would be made. In some scenarios, a discovery could be reinforced by information from multiple, complementary types of experiments; in others, only one experiment would see a signal, giving only a partial, more ambiguous picture; in still others, no experiment would be sensitive and new approaches would be needed. In this whitepaper, we present an update to a similar study performed for the European Strategy Briefing Book performed within the dark matter at the Energy Frontier (EF10) Snowmass Topical Group We take as a starting point a set of projections for future collider facilities and a method of graphical comparisons routinely performed for LHC DM searches using simplified models recommended by the LHC Dark Matter Working Group and also used for the BSM and dark matter chapters of the European Strategy Briefing Book. These comparisons can also serve as launching point for cross-frontier discussions about dark matter complementarity.

The recent application of neural network algorithms to problems in gravitational-wave physics invites the study of how best to build production-ready applications on top of them. By viewing neural networks not as standalone models, but as components or functions in larger data processing pipelines, we can apply lessons learned from both traditional software development practices as well as successful deep learning applications from the private sector. This paper highlights challenges presented by straightforward but naïve deployment strategies for deep learning models, and identifies solutions to them gleaned from these sources. It then presents HERMES, a library of tools for implementing these solutions, and describes how HERMES is being used to develop a particular deep learning application which will be deployed during the next data collection run of the International Gravitational-Wave Observatories.

Machine learning (ML) is becoming an increasingly important component of cutting-edge physics research, but its computational requirements present significant challenges. In this white paper, we discuss the needs of the physics community regarding ML across latency and throughput regimes, the tools and resources that offer the possibility of addressing these needs, and how these can be best utilized and accessed in the coming years.

Recurrent neural networks have been shown to be effective architectures for many tasks in high energy physics, and thus have been widely adopted. Their use in low-latency environments has, however, been limited as a result of the difficulties of implementing recurrent architectures on field-programmable gate arrays (FPGAs). In this paper we present an implementation of two types of recurrent neural network layers -- long short-term memory and gated recurrent unit -- within the hls4ml framework. We demonstrate that our implementation is capable of producing effective designs for both small and large models, and can be customized to meet specific design requirements for inference latencies and FPGA resources. We show the performance and synthesized designs for multiple neural networks, many of which are trained specifically for jet identification tasks at the CERN Large Hadron Collider.

Searches for Dark Matter at the LHC are commonly described in terms of simplified models with scalar, pseudo-scalar, vector and axial-vector mediators. In this work we explore the constraints imposed on such models from the observed Dark Matter relic abundance. We present these constraints over a range of mediator masses relevant for the LHC and for future, higher energy colliders. We additionally compute bounds from a photon line search for the decay of a pseudo-scalar mediator to di-photons that includes the mediator mass region near 750 GeV. Finally, we compare cosmological constraints with the reach of a possible future 100 TeV circular hadron collider, indirect, and direct detection experiments.