Jan Kieseler

We explore the use of graph networks to deal with irregular-geometry detectors in the context of particle reconstruction. Thanks to their representation-learning capabilities, graph networks can exploit the full detector granularity, while natively managing the event sparsity and arbitrarily complex detector geometries. We introduce two distance-weighted graph network architectures, dubbed GarNet and GravNet layers, and apply them to a typical particle reconstruction task. The performance of the new architectures is evaluated on a data set of simulated particle interactions on a toy model of a highly granular calorimeter, loosely inspired by the endcap calorimeter to be installed in the CMS detector for the High-Luminosity LHC phase. We study the clustering of energy depositions, which is the basis for calorimetric particle reconstruction, and provide a quantitative comparison to alternative approaches. The proposed algorithms provide an interesting alternative to existing methods, offering equally performing or less resource-demanding solutions with less underlying assumptions on the detector geometry and, consequently, the possibility to generalize to other detectors.

Jet flavour classification is of paramount importance for a broad range of applications in modern-day high-energy-physics experiments, particularly at the LHC. In this paper we propose a novel architecture for this task that exploits modern deep learning techniques. This new model, called DeepJet, overcomes the limitations in input size that affected previous approaches. As a result, the heavy flavour classification performance improves, and the model is extended to also perform quark-gluon tagging.

The successful operation of the Large Hadron Collider (LHC) and the excellent performance of the ATLAS, CMS, LHCb and ALICE detectors in Run-1 and Run-2 with $pp$ collisions at center-of-mass energies of 7, 8 and 13 TeV as well as the giant leap in precision calculations and modeling of fundamental interactions at hadron colliders have allowed an extraordinary breadth of physics studies including precision measurements of a variety physics processes. The LHC results have so far confirmed the validity of the Standard Model of particle physics up to unprecedented energy scales and with great precision in the sectors of strong and electroweak interactions as well as flavour physics, for instance in top quark physics. The upgrade of the LHC to a High Luminosity phase (HL-LHC) at 14 TeV center-of-mass energy with 3 ab$^{-1}$ of integrated luminosity will probe the Standard Model with even greater precision and will extend the sensitivity to possible anomalies in the Standard Model, thanks to a ten-fold larger data set, upgraded detectors and expected improvements in the theoretical understanding. This document summarises the physics reach of the HL-LHC in the realm of strong and electroweak interactions and top quark physics, and provides a glimpse of the potential of a possible further upgrade of the LHC to a 27 TeV $pp$ collider, the High-Energy LHC (HE-LHC), assumed to accumulate an integrated luminosity of 15 ab$^{-1}$.

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.

The full optimization of the design and operation of instruments whose functioning relies on the interaction of radiation with matter is a super-human task, due to the large dimensionality of the space of possible choices for geometry, detection technology, materials, data-acquisition, and information-extraction techniques, and the interdependence of the related parameters. On the other hand, massive potential gains in performance over standard, "experience-driven" layouts are in principle within our reach if an objective function fully aligned with the final goals of the instrument is maximized through a systematic search of the configuration space. The stochastic nature of the involved quantum processes make the modeling of these systems an intractable problem from a classical statistics point of view, yet the construction of a fully differentiable pipeline and the use of deep learning techniques may allow the simultaneous optimization of all design parameters. In this white paper, we lay down our plans for the design of a modular and versatile modeling tool for the end-to-end optimization of complex instruments for particle physics experiments as well as industrial and medical applications that share the detection of radiation as their basic ingredient. We consider a selected set of use cases to highlight the specific needs of different applications.

We present a method to establish, experimentally, the relation between the top-quark mass m_{t}^{MC} as implemented in Monte Carlo generators and the Lagrangian mass parameter m_{t} in a theoretically well-defined renormalization scheme. We propose a simultaneous fit of m_{t}^{MC} and an observable sensitive to m_{t}, which does not rely on any prior assumptions about the relation between m_{t} and m_{t}^{MC}. The measured observable is independent of m_{t}^{MC} and can be used subsequently for a determination of m_{t}. The analysis strategy is illustrated with examples for the extraction of m_{t} from inclusive and differential cross sections for hadroproduction of top quarks.

High-energy physics detectors, images, and point clouds share many similarities in terms of object detection. However, while detecting an unknown number of objects in an image is well established in computer vision, even machine learning assisted object reconstruction algorithms in particle physics almost exclusively predict properties on an object-by-object basis. Traditional approaches from computer vision either impose implicit constraints on the object size or density and are not well suited for sparse detector data or rely on objects being dense and solid. The object condensation method proposed here is independent of assumptions on object size, sorting or object density, and further generalises to non-image-like data structures, such as graphs and point clouds, which are more suitable to represent detector signals. The pixels or vertices themselves serve as representations of the entire object, and a combination of learnable local clustering in a latent space and confidence assignment allows one to collect condensates of the predicted object properties with a simple algorithm. As proof of concept, the object condensation method is applied to a simple object classification problem in images and used to reconstruct multiple particles from detector signals. The latter results are also compared to a classic particle flow approach.

The high-luminosity upgrade of the LHC will come with unprecedented physics and computing challenges. One of these challenges is the accurate reconstruction of particles in events with up to 200 simultaneous protonproton interactions. The planned CMS High Granularity Calorimeter offers fine spatial resolution for this purpose, with more than 6 million channels, but also poses unique challenges to reconstruction algorithms aiming to reconstruct individual particle showers. In this contribution, we propose an end-to-end machine-learning method that performs clustering, classification, and energy and position regression in one step while staying within memory and computational constraints. We employ GravNet, a graph neural network, and an object condensation loss function to achieve this task. Additionally, we propose a method to relate truth showers to reconstructed showers by maximising the energy weighted intersection over union using maximal weight matching. Our results show the efficiency of our method and highlight a promising research direction to be investigated further.

Abstract We present an end-to-end reconstruction algorithm to build particle candidates from detector hits in next-generation granular calorimeters similar to that foreseen for the high-luminosity upgrade of the CMS detector. The algorithm exploits a distance-weighted graph neural network, trained with object condensation, a graph segmentation technique. Through a single-shot approach, the reconstruction task is paired with energy regression. We describe the reconstruction performance in terms of efficiency as well as in terms of energy resolution. In addition, we show the jet reconstruction performance of our method and discuss its inference computational cost. To our knowledge, this work is the first-ever example of single-shot calorimetric reconstruction of $${\mathcal {O}}(1000)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo>(</mml:mo> <mml:mn>1000</mml:mn> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> particles in high-luminosity conditions with 200 pileup.

Abstract We present the current stage of research progress towards a one-pass, completely Machine Learning (ML) based imaging calorimeter reconstruction. The model used is based on Graph Neural Networks (GNNs) and directly analyzes the hits in each HGCAL endcap. The ML algorithm is trained to predict clusters of hits originating from the same incident particle by labeling the hits with the same cluster index. We impose simple criteria to assess whether the hits associated as a cluster by the prediction are matched to those hits resulting from any particular individual incident particles. The algorithm is studied by simulating two tau leptons in each of the two HGCAL endcaps, where each tau may decay according to its measured standard model branching probabilities. The simulation includes the material interaction of the tau decay products which may create additional particles incident upon the calorimeter. Using this varied multiparticle environment we can investigate the application of this reconstruction technique and begin to characterize energy containment and performance.

In recent years, neural network-based classification has been used to improve data analysis at collider experiments. While this strategy proves to be hugely successful, the underlying models are not commonly shared with the public and they rely on experiment-internal data as well as full detector simulations. We propose a new strategy, so-called classifier surrogates, to be trained inside the experiments, that only utilise publicly accessible features and truth information. These surrogates approximate the original classifier distribution, and can be shared with the public. Subsequently, such a model can be evaluated by sampling the classification output from high-level information without requiring a sophisticated detector simulation. Technically, we show that Continuous Normalizing Flows are a suitable generative architecture that can be efficiently trained to sample classification results using Conditional Flow Matching. We further demonstrate that these models can be easily extended by Bayesian uncertainties to indicate their degree of validity when confronted with unknown inputs to the user. For a concrete example of tagging jets from hadronically decaying top quarks, we demonstrate the application of flows in combination with uncertainty estimation through either inference of a mean-field Gaussian weight posterior, or Monte Carlo sampling network weights.

A bstract The running of the top quark mass ( m t ) is probed at the next-to-next-to-leading order in quantum chromodynamics for the first time. The result is obtained by comparing calculations in the modified minimal subtraction ( $$\overline{{\text{MS}} }$$ ) renormalisation scheme to the CMS result on differential measurement of the top quark-antiquark ( $${\text{t}}\overline{{\text{t}} }$$ ) production cross section at $$\sqrt{s}$$ = 13 TeV. The scale dependence of m t is extracted as a function of the invariant mass of the $${\text{t}}\overline{{\text{t}} }$$ system, up to an energy scale of about 0.5 TeV. The observed running is found to be in good agreement with the three-loop solution of the renormalisation group equations on quantum chromodynamics.

Motivated by the success of the flavour physics programme carried out over the last decade at the Large Hadron Collider (LHC), we characterize in detail the physics potential of its High-Luminosity and High-Energy upgrades in this domain of physics. We document the extraordinary breadth of the HL/HE-LHC programme enabled by a putative Upgrade II of the dedicated flavour physics experiment LHCb and the evolution of the established flavour physics role of the ATLAS and CMS general purpose experiments. We connect the dedicated flavour physics programme to studies of the top quark, Higgs boson, and direct high-$p_T$ searches for new particles and force carriers. We discuss the complementarity of their discovery potential for physics beyond the Standard Model, affirming the necessity to fully exploit the LHC's flavour physics potential throughout its upgrade eras.

We present a first proof of concept to directly use neural network based pattern recognition to trigger on distinct calorimeter signatures from displaced particles, such as those that arise from the decays of exotic long-lived particles. The study is performed for a high granularity forward calorimeter similar to the planned high granularity calorimeter for the high luminosity upgrade of the CMS detector at the CERN Large Hadron Collider. Without assuming a particular model that predicts long-lived particles, we show that a simple convolutional neural network, that could in principle be deployed on dedicated fast hardware, can efficiently identify showers from displaced particles down to low energies while providing a low trigger rate.

The design of instruments that rely on the interaction of radiation with matter for their operation is a quite complex task if our goal is to achieve near optimality on some well-defined utility fu...

A method is discussed that allows combining sets of differential or inclusive measurements. It is assumed that at least one measurement was obtained with simultaneously fitting a set of nuisance parameters, representing sources of systematic uncertainties. As a result of beneficial constraints from the data all such fitted parameters are correlated among each other. The best approach for a combination of these measurements would be the maximisation of a combined likelihood, for which the full fit model of each measurement and the original data are required. However, only in rare cases this information is publicly available. In absence of this information most commonly used combination methods are not able to account for these correlations between uncertainties, which can lead to severe biases as shown in this article. The method discussed here provides a solution for this problem. It relies on the public result and its covariance or Hessian, only, and is validated against the combined-likelihood approach. A dedicated software package implementing this method is also presented. It provides a text-based user interface alongside a C++ interface. The latter also interfaces to ROOT classes for simple combination of binned measurements such as differential cross sections.

We investigate the effect of longitudinal and transverse calorimeter segmentation on event-by-event software compensation for hadronic showers. To factorize out sampling and electronics effects, events are simulated in which a single charged pion is shot at a homogenous lead glass calorimeter, split into longitudinal and transverse segments of varying size. As an approximation of an optimal reconstruction, a neural network-based energy regression is trained. The architecture is based on blocks of convolutional kernels customized for shower energy regression using local energy densities; biases at the edges of the training dataset are mitigated using a histogram technique. With this approximation, we find that a longitudinal and transverse segment size less than or equal to 0.5 and 1.3 nuclear interaction lengths, respectively, is necessary to achieve an optimal energy measurement. In addition, an intrinsic energy resolution of $8\%/\sqrt{E}$ for pion showers is observed.

Abstract The performance demands of future particle-physics experiments investigating the high-energy frontier pose a number of new challenges, forcing us to find improved solutions for the detection, identification, and measurement of final-state particles in subnuclear collisions. One such challenge is the precise measurement of muon momentum at very high energy, where an estimate of the curvature provided by conceivable magnetic fields in realistic detectors proves insufficient for achieving good momentum resolution when detecting, e.g., a narrow, high mass resonance decaying to a muon pair. In this work we study the feasibility of an entirely new avenue for the measurement of the energy of muons based on their radiative losses in a dense, finely segmented calorimeter. This is made possible by exploiting spatial information of the clusters of energy from radiated photons in a regression task. The use of a task-specific deep learning architecture based on convolutional layers allows us to treat the problem as one akin to image reconstruction, where images are constituted by the pattern of energy released in successive layers of the calorimeter. A measurement of muon energy with better than 20% relative resolution is shown to be achievable for ultra-TeV muons.

Recent measurements in the top quark sector at the CERN Large Hadron Collider are discussed. This review discusses the most recent measurements of inclusive and differential top quark cross-sections in strong and electroweak production of top quarks and related measurements, such as top quark properties, as well as searches, including EFT approaches.

The full optimization of the design and operation of instruments whose functioning relies on the interaction of radiation with matter is a super-human task, given the large dimensionality of the space of possible choices for geometry, detection technology, materials, data-acquisition, and information-extraction techniques, and the interdependence of the related parameters. On the other hand, massive potential gains in performance over standard, "experience-driven" layouts are in principle within our reach if an objective function fully aligned with the final goals of the instrument is maximized by means of a systematic search of the configuration space. The stochastic nature of the involved quantum processes make the modeling of these systems an intractable problem from a classical statistics point of view, yet the construction of a fully differentiable pipeline and the use of deep learning techniques may allow the simultaneous optimization of all design parameters. In this document we lay down our plans for the design of a modular and versatile modeling tool for the end-to-end optimization of complex instruments for particle physics experiments as well as industrial and medical applications that share the detection of radiation as their basic ingredient. We consider a selected set of use cases to highlight the specific needs of different applications.

The future proton-proton collider (FCC-hh) will deliver collisions at a center of mass energy up to $\sqrt{s}=100$ TeV at an unprecedented instantaneous luminosity of $L=3~10^{35}$ cm$^{-2}$s$^{-1}$, resulting in extremely challenging radiation and luminosity conditions. By delivering an integrated luminosity of few tens of ab$^{-1}$, the FCC-hh will provide an unrivalled discovery potential for new physics. Requiring high sensitivity for resonant searches at masses up to tens of TeV imposes strong constraints on the design of the calorimeters. Resonant searches in final states containing jets, taus and electrons require both excellent energy resolution at multi-TeV energies as well as outstanding ability to resolve highly collimated decay products resulting from extreme boosts. In addition, the FCC-hh provides the unique opportunity to precisely measure the Higgs self-coupling in the di-photon and b-jets channel. Excellent photon and jet energy resolution at low energies as well as excellent angular resolution for pion background rejection are required in this challenging environment. This report describes the calorimeter studies for a multi-purpose detector at the FCC-hh. The calorimeter active components consist of Liquid Argon, scintillating plastic tiles and Monolithic Active Pixel Sensors technologies. The technological choices, design considerations and achieved performances in full Geant4 simulations are discussed and presented. The simulation studies are focused on the evaluation of the concepts. Standalone studies under laboratory conditions as well as first tests in realistic FCC-hh environment, including pileup rejection capabilities by making use of fast signals and high granularity, have been performed. These studies have been performed within the context of the preparation of the FCC conceptual design reports (CDRs).

Abstract The high luminosity upgrade of the LHC will provide unique physics opportunities, such as the observation of rare processes and precision measurements. However, the accompanying harsh radiation environment will also pose unprecedented challenged to the detector performance and hardware. In this paper, we study the radiation induced damage and its macroscopic isothermal annealing behaviour of the bulk material from new 8” silicon wafers using diode test structures. The sensor properties are determined through measurements of the diode capacitance and leakage current for three thicknesses, two material types, and neutron fluences from 6.5· 10 14 to 1 · 10 16 n eq /cm 2 .

In this article we examine recent developments in the research area concerning the creation of end-to-end models for the complete optimization of measuring instruments. The models we consider rely on differentiable programming methods and on the specification of a software pipeline including all factors impacting performance -- from the data-generating processes to their reconstruction and the extraction of inference on the parameters of interest of a measuring instrument -- along with the careful specification of a utility function well aligned with the end goals of the experiment. Building on previous studies originated within the MODE Collaboration, we focus specifically on applications involving instruments for particle physics experimentation, as well as industrial and medical applications that share the detection of radiation as their data-generating mechanism.

With the increasing usage of machine-learning in high-energy physics analyses, the publication of the trained models in a reusable form has become a crucial question for analysis preservation and reuse. The complexity of these models creates practical issues for both reporting them accurately and for ensuring the stability of their behaviours in different environments and over extended timescales. In this note we discuss the current state of affairs, highlighting specific practical issues and focusing on the most promising technical and strategic approaches to ensure trustworthy analysis-preservation. This material originated from discussions in the LHC Reinterpretation Forum and the 2023 PhysTeV workshop at Les Houches.

This thesis presents the first experimental calibration of the top-quark Monte-Carlo mass. It also provides the top-quark mass-independent and most precise top-quark pair production cross-section meas

In this chapter, the measurement of the $${\mathrm{t}\bar{\mathrm{t}}}$$ production cross sections $$\sigma _{{\mathrm{t}\bar{\mathrm{t}}}}$$ at $$\sqrt{s}=7\,\text {TeV} $$ and $$\sqrt{s}=8\,\text {TeV} $$ is presented.

The high-luminosity upgrade of the LHC will come with unprecedented physics and computing challenges. One of these challenges is the accurate reconstruction of particles in events with up to 200 simultaneous proton-proton interactions. The planned CMS High Granularity Calorimeter offers fine spatial resolution for this purpose, with more than 6 million channels, but also poses unique challenges to reconstruction algorithms aiming to reconstruct individual particle showers. In this contribution, we propose an end-to-end machine-learning method that performs clustering, classification, and energy and position regression in one step while staying within memory and computational constraints. We employ GravNet, a graph neural network, and an object condensation loss function to achieve this task. Additionally, we propose a method to relate truth showers to reconstructed showers by maximising the energy weighted intersection over union using maximal weight matching. Our results show the efficiency of our method and highlight a promising research direction to be investigated further.

The high-luminosity upgrade of the LHC will come with unprecedented physics and computing challenges. One of these challenges is the accurate reconstruction of particles in events with up to 200 simultaneous proton-proton interactions. The planned CMS High Granularity Calorimeter offers fine spatial resolution for this purpose, with more than 6 million channels, but also poses unique challenges to reconstruction algorithms aiming to reconstruct individual particle showers. In this contribution, we propose an end-to-end machine-learning method that performs clustering, classification, and energy and position regression in one step while staying within memory and computational constraints. We employ GravNet, a graph neural network, and an object condensation loss function to achieve this task. Additionally, we propose a method to relate truth showers to reconstructed showers by maximising the energy weighted intersection over union using maximal weight matching. Our results show the efficiency of our method and highlight a promising research direction to be investigated further.

This record contains the fully-preprocessed training/validation and testing datasets used to train and evaluate the final models for "Calorimetric Measurement of Multi-TeV Muons via Deep Regression" by Jan Kieseler, Giles C. Strong, Filippo Chiandotto, Tommaso Dorigo, &amp; Lukas Layer, (2021), arXiv:2107.02119 [physics.ins-det] (https://arxiv.org/abs/2107.02119). The files are LZF-compressed HDF5 format and designed to be used directly with the code-base available at https://github.com/GilesStrong/calo_muon_regression. Please use the 'issues' tab on the GitHub repo for any questions or problems with these datasets. The training dataset consists of 886,716 muons with energies in the continuous range [50,8000] GeV split into 36 subsamples (folds). The zeroth fold of this dataset is used as our validation data. The testing dataset contains 429,750 muons, generated at fixed values of muon energy (E=100, 500, 900, 1300, 1700, 2100, 2500, 2900, 3300, 3700, 4100 GeV), and split into 18 folds. The input features are the raw hits in the calorimeter (stored in a sparse COO representation), and the high-level features discussed in the paper.

The detector responses are subject to the reconstruction procedure, common for data and simulation.

The choice of the top-quark mass value in a certain scheme affects the predicted production cross sections for $${\text {t}\bar{\text {t}}}$$ pairs as well as the kinematics of their decay products. Section 6.1 is dedicated to the extraction of the top-quark pole mass $$m_t^{\text {pole}}$$ from $$\sigma _{{\text {t}\bar{\text {t}}}}$$ . A determination of $$m_t^{\text {MC}}$$ and studies to extract a well-defined value for $$m_t^{\text {pole}}$$ from the kinematics of decay products are presented in Sect. 6.2.

The LHC [1] is a proton-proton ring collider with a circumference of 27 km designed for $$\sqrt{s}=$$ 14 TeV located at CERN (European Organization for Nuclear Research) near Geneva, Switzerland. During its first running period (Run 1) from 2010 until 2012, it was operating at $$\sqrt{s}=$$ 7 TeV and 8 TeV for pp collisions.

The properties of the top quark are important parameters of the SM and determine the precision of our understanding of nature to a wide extent.

This thesis presents the first experimental calibration of the top-quark Monte-Carlo mass. It also provides the top-quark mass-independent and most precise top-quark pair production cross-section measurement to date. The most precise measurements of the top-quark mass obtain the top-quark mass parameter (Monte-Carlo mass) used in simulations, which are partially based on heuristic models. Its interpretation in terms of mass parameters used in theoretical calculations, e.g. a running or a pole mass, has been a long-standing open problem with far-reaching implications beyond particle physics, even affecting conclusions on the stability of the vacuum state of our universe. In this thesis, this problem is solved experimentally in three steps using data obtained with the compact muon solenoid (CMS) detector. The most precise top-quark pair production cross-section measurements to date are performed. The Monte-Carlo mass is determined and a new method for extracting the top-quark mass from theoretical calculations is presented. Lastly, the top-quark production cross-sections are obtained for the first time without residual dependence on the top-quark mass, are interpreted using theoretical calculations to determine the top-quark running- and pole mass with unprecedented precision, and are fully consistently compared with the simultaneously obtained top-quark Monte-Carlo mass

The precision of the extracted value of $$m_t^{\text {MC}}$$ from the $$\text {m}_{\text {lb}}^{\text {min}}$$ shape is mostly limited by the uncertainties on the JES, the hadronization modeling, the top $$p_{\mathrm {T}}$$ modeling and $$Q^2$$ scale. These variations are strongly constrained when fitted simultaneously with the $${\mathrm{t}\bar{\mathrm{t}}}$$ production cross sections, as described in Chap. 5 . In consequence, an increased precision can be expected when combining both approaches.

Particle physics studies the fundamental components of matter and their interactions. Within the last decades impressive advancements in this field have been achieved.

The work presented in this thesis focuses on precision measurements of the $${\mathrm{t}\bar{\mathrm{t}}}$$ production cross section and detailed studies on the top quark mass, both as the parameter implemented in MC simulation and in theoretically well-defined schemes, and the experimental relation of these mass parameters.

The cross section for top quark pair production is measured in proton-proton collisions at = 8 TeV in a data sample corresponding to 2.4 fb−1 of integrated luminosity collected by the CMS experiment in 2012. The measurement is performed with events with two leptons (electrons or muons), as well as with identified b-quark jets in the final state. The combined measured cross section is σt = 227 ± 3 (stat.) ± 11 (syst.) ± 10 (lumi.) pb, in agreement with theoretical predictions.

Measurements of inclusive and differential top-quark pair production cross sections at a center of mass energy of 8 and 7 TeV are presented. The total cross section is measured in the lepton+jets and the dileptonic decay modes. Differential cross sections are obtained as a function of various kinematic observables, including the transverse momentum and rapidity of the (anti)top quark, kinematics of the top-antitop system, and jet multiplicity in the event. The precise results are used to extract the strong coupling constant from inclusive top-pair production cross sections. All measurements use LHC data collected by the CMS experiment in 2011 and 2012.

We propose a two-stage strategy to search for new long-lived particles that could be produced at the CERN LHC, become trapped in detector material, and decay later. In the first stage, metal rods are exposed to LHC collisions in an experimental cavern. In the second stage, they are immersed in liquid argon at a different location, where out-of-time decays could be detected. Using a benchmark of pair-produced long-lived gluinos, we show that this experiment would have unique sensitivity to gluino-neutralino mass splittings down to 3 GeV, in previously uncovered lifetimes of days to years.

Within the context of studies for novel measurement solutions for future particle physics experiments, we developed a performant kNN-based regressor to infer the energy of highly-relativistic muons from the pattern of their radiation losses in a dense and granular calorimeter. The regressor is based on a pool of weak kNN learners, which learn by adapting weights and biases to each training event through stochastic gradient descent. The effective number of parameters optimized by the procedure is in the 60 millions range, thus comparable to that of large deep learning architectures. We test the performance of the regressor on the considered application by comparing it to that of several machine learning algorithms, showing comparable accuracy to that achieved by boosted decision trees and neural networks.

The full optimization of the design and operation of instruments whose functioning relies on the interaction of radiation with matter is a super-human task, given the large dimensionality of the space of possible choices for geometry, detection technology, materials, data-acquisition, and information-extraction techniques, and the interdependence of the related parameters. On the other hand, massive potential gains in performance over standard, "experience-driven" layouts are in principle within our reach if an objective function fully aligned with the final goals of the instrument is maximized by means of a systematic search of the configuration space. The stochastic nature of the involved quantum processes make the modeling of these systems an intractable problem from a classical statistics point of view, yet the construction of a fully differentiable pipeline and the use of deep learning techniques may allow the simultaneous optimization of all design parameters. In this document we lay down our plans for the design of a modular and versatile modeling tool for the end-to-end optimization of complex instruments for particle physics experiments as well as industrial and medical applications that share the detection of radiation as their basic ingredient. We consider a selected set of use cases to highlight the specific needs of different applications.

We present the current stage of research progress towards a one-pass, completely Machine Learning (ML) based imaging calorimeter reconstruction. The model used is based on Graph Neural Networks (GNNs) and directly analyzes the hits in each HGCAL endcap. The ML algorithm is trained to predict clusters of hits originating from the same incident particle by labeling the hits with the same cluster index. We impose simple criteria to assess whether the hits associated as a cluster by the prediction are matched to those hits resulting from any particular individual incident particles. The algorithm is studied by simulating two tau leptons in each of the two HGCAL endcaps, where each tau may decay according to its measured standard model branching probabilities. The simulation includes the material interaction of the tau decay products which may create additional particles incident upon the calorimeter. Using this varied multiparticle environment we can investigate the application of this reconstruction technique and begin to characterize energy containment and performance.

The running of the top quark mass ($m_\mathrm{t}$) is probed at the next-to-next-to-leading order (NNLO) in quantum chromodynamics (QCD) for the first time. The result is obtained by comparing calculations in the modified minimal subtraction ($\mathrm{\bar{MS}}$) renormalisation scheme to a differential measurement of the top quark-antiquark ($\mathrm{t\bar{t}}$) production cross section at $\sqrt{s} = 13~\mathrm{TeV}$. The scale dependence of $m_\mathrm{t}$ is extracted as a function of the invariant mass of the $\mathrm{t\bar{t}}$ system, up to an energy scale of about $0.5~\mathrm{TeV}$. The observed running is found to be in good agreement with the three-loop solution of the QCD renormalisation group equations.

The high luminosity upgrade of the LHC will provide unique physics opportunities, such as the observation of rare processes and precision measurements. However, the accompanying harsh radiation environment will also pose unprecedented challenged to the detector performance and hardware. In this paper, we study the radiation induced damage and its macroscopic isothermal annealing behaviour of the bulk material from new 8" silicon wafers using diode test structures. The sensor properties are determined through measurements of the diode capacitance and leakage current for three thicknesses, two material types, and neutron fluences from $6.5\cdot 10^{14}$ to $10^{16}\,\mathrm{neq/cm^2}$.

Data is in HDF5 format (with LZF compression). For specifics and details, please see https://github.com/GilesStrong/mode_diffprog_22_challenge, N.B. Link active after 01/08/22 The training file contains two datasets: `'x0'`: a set of voxelwise X0 predictions (float32) `'targs'`: a set of voxelwise classes (int): 0 = soil 1 = wall The format of the datasets is a rank-4 array, with dimensions corresponding to (samples, z position, x position, y position). All passive volumes are of the same size: 10x10x10 m, with cubic voxels of size 1x1x1 m, i.e. every passive volume contains 1000 voxels. The arrays are ordered such that zeroth z layer is the bottom layer of the passive volume, and the ninth layer is the top layer. It can be read using e.g. the code below: <em>with open('train.h5') as h5:</em> <em> inputs = h5['x0'][()]</em> <em> targets = h5['targs'][()]</em> The test file only contains the X0 inputs: <em>with open('test.h5') as h5:</em> <em> inputs = h5['x0'][()]</em>

Data is in HDF5 format (with LZF compression). For specifics and details, please see https://github.com/GilesStrong/mode_diffprog_22_challenge The training file contains two datasets: `'x0'`: a set of voxelwise X0 predictions (float32) `'targs'`: a set of voxelwise classes (int): 0 = soil 1 = wall The format of the datasets is a rank-4 array, with dimensions corresponding to (samples, z position, x position, y position). All passive volumes are of the same size: 10x10x10 m, with cubic voxels of size 1x1x1 m, i.e. every passive volume contains 1000 voxels. The arrays are ordered such that zeroth z layer is the bottom layer of the passive volume, and the ninth layer is the top layer. It can be read using e.g. the code below: <em>with h5py.File('train.h5', 'r') as f:</em> <em> inputs = h5['x0'][()]</em> <em> targets = h5['targs'][()]</em> The test file only contains the X0 inputs: <em>with h5py.File('test.h5', 'r') as h5:</em> <em> inputs = h5['x0'][()]</em> The private testing sample also contains targets. The private and public splits can be recovered using: <em>from sklearn.model_selection import train_test_split</em> <em>pub, pri = train_test_split(targets, test_size=25000, random_state=3452, shuffle=True)</em>

Data is in HDF5 format (with LZF compression). For specifics and details, please see https://github.com/GilesStrong/mode_diffprog_22_challenge The training file contains two datasets: `'x0'`: a set of voxelwise X0 predictions (float32) `'targs'`: a set of voxelwise classes (int): 0 = soil 1 = wall The format of the datasets is a rank-4 array, with dimensions corresponding to (samples, z position, x position, y position). All passive volumes are of the same size: 10x10x10 m, with cubic voxels of size 1x1x1 m, i.e. every passive volume contains 1000 voxels. The arrays are ordered such that zeroth z layer is the bottom layer of the passive volume, and the ninth layer is the top layer. It can be read using e.g. the code below: <em>with h5py.File('train.h5', 'r') as f:</em> <em> inputs = h5['x0'][()]</em> <em> targets = h5['targs'][()]</em> The test file only contains the X0 inputs: <em>with h5py.File('test.h5', 'r') as h5:</em> <em> inputs = h5['x0'][()]</em> The private testing sample also contains targets. The private and public splits can be recovered using: <em>from sklearn.model_selection import train_test_split</em> <em>pub, pri = train_test_split(targets, test_size=25000, random_state=3452, shuffle=True)</em>

The performance demands of future particle-physics experiments investigating the high-energy frontier pose a number of new challenges, forcing us to find new solutions for the detection, identification, and measurement of final-state particles in subnuclear collisions. One such challenge is the precise measurement of muon momenta at very high energy, where the curvature provided by conceivable magnetic fields in realistic detectors proves insufficient to achieve the desired resolution. In this work we show the feasibility of an entirely new avenue for the measurement of the energy of muons based on their radiative losses in a dense, finely segmented calorimeter. This is made possible by the use of the spatial information of the clusters of deposited photon energy in the regression task. Using a homogeneous lead-tungstate calorimeter as a benchmark, we show how energy losses may provide significant complementary information for the estimate of muon energies above 1 TeV.

This data set is an output of a simulation of electrons and pions shot at a chunk of an imaging calorimeter. It is used in the case study for GarNet-on-FPGA, documented in arXiv:2008.03601. Each HDF5 file contains the following arrays: Name | Shape | Description cluster | (10000, 128, 4) | Samples for training and inference. Outermost dimension is the event (cluster). Each cluster has maximum 128 hits, each of which has four features: x, y, z, and energy. The coordinates of the hits are in cm. The energy is in GeV. The x and y coordinates are relative to the seed hit, while the z coordinate is with respect to the calorimeter front face. size | (10000) | Number of hits in each cluster. The cluster array is zero-padded when the cluster size is below 128. truth_pid | (10000) | Identity of the primary particle (0: electron, 1: pion). truth_energy | (10000) | True energy of the primary particle. raw | (10000, 4375, 2) | Raw data (actual output of the simulation). For each event (outermost dimension), hit energy and primary fraction (innermost dimension indices 0 and 1) are given for each of the 4375 sensors. Energy is in MeV. coordinates | (4375, 3) | The x, y, and z coordinates of the 4375 sensors, to be used to interpret the raw data. See the paper for the details of the simulation.

This dataset is the dataset used to train and test the object condensation particle flow approach described in arxiv:2002.03605. The data can be read with DeepJetCore 3.1 (https://github.com/DL4Jets/DeepJetCore)<br> The entries in the truth array are of dimension (batch, 200, N_truth). The truth inputs are: isElectron,<br> isGamma,<br> isPositron,<br> true_energy,<br> true_x,<br> true_y The entries in the feature array are of dimension (batch, 200, N_features), with the features being: rechit_energy,<br> rechit_x,<br> rechit_y,<br> rechit_z,<br> rechit_layer,<br> rechit_detid The "train.zip" file contains the training sample<br> The "test.zip" file the test sample The main test sample is identical to the training sample in composition, but statistically independent.<br> Other samples can be found in subfolders: test/flatNpart: sample with flat distribution of additional particles in the event w.r.t. each individual particle<br> Test/hiNPart: sample with up to 15 particles per event

This data set is an output of a simulation of electrons and pions shot at a chunk of an imaging calorimeter. It is used in the case study for GarNet-on-FPGA, documented in arXiv:2008.03601. Each HDF5 file contains the following arrays: Name | Shape | Description cluster | (10000, 128, 4) | Samples for training and inference. Outermost dimension is the event (cluster). Each cluster has maximum 128 hits, each of which has four features: x, y, z, and energy. The coordinates of the hits are in cm. The energy is in GeV. The x and y coordinates are relative to the seed hit, while the z coordinate is with respect to the calorimeter front face. size | (10000) | Number of hits in each cluster. The cluster array is zero-padded when the cluster size is below 128. truth_pid | (10000) | Identity of the primary particle (0: electron, 1: pion). truth_energy | (10000) | True energy of the primary particle. raw | (10000, 4375, 2) | Raw data (actual output of the simulation). For each event (outermost dimension), hit energy and primary fraction (innermost dimension indices 0 and 1) are given for each of the 4375 sensors. Energy is in MeV. coordinates | (4375, 3) | The x, y, and z coordinates of the 4375 sensors, to be used to interpret the raw data. See the paper for the details of the simulation.

This dataset is the dataset used to train and test the object condensation particle flow approach described in arxiv:2002.03605. The data can be read with DeepJetCore 3.1 (https://github.com/DL4Jets/DeepJetCore)<br> The entries in the truth array are of dimension (batch, 200, N_truth). The truth inputs are: isElectron,<br> isGamma,<br> isPositron,<br> true_energy,<br> true_x,<br> true_y The entries in the feature array are of dimension (batch, 200, N_features), with the features being: rechit_energy,<br> rechit_x,<br> rechit_y,<br> rechit_z,<br> rechit_layer,<br> rechit_detid The "train.zip" file contains the training sample<br> The "test.zip" file the test sample The main test sample is identical to the training sample in composition, but statistically independent.<br> Other samples can be found in subfolders: test/flatNpart: sample with flat distribution of additional particles in the event w.r.t. each individual particle<br> Test/hiNPart: sample with up to 15 particles per event

The future proton-proton collider (FCC-hh) will deliver collisions at a center of mass energy up to $\sqrt{s}=100$ TeV at an unprecedented instantaneous luminosity of $L=3~10^{35}$ cm$^{-2}$s$^{-1}$, resulting in extremely challenging radiation and luminosity conditions. By delivering an integrated luminosity of few tens of ab$^{-1}$, the FCC-hh will provide an unrivalled discovery potential for new physics. Requiring high sensitivity for resonant searches at masses up to tens of TeV imposes strong constraints on the design of the calorimeters. Resonant searches in final states containing jets, taus and electrons require both excellent energy resolution at multi-TeV energies as well as outstanding ability to resolve highly collimated decay products resulting from extreme boosts. In addition, the FCC-hh provides the unique opportunity to precisely measure the Higgs self-coupling in the di-photon and b-jets channel. Excellent photon and jet energy resolution at low energies as well as excellent angular resolution for pion background rejection are required in this challenging environment. This report describes the calorimeter studies for a multi-purpose detector at the FCC-hh. The calorimeter active components consist of Liquid Argon, scintillating plastic tiles and Monolithic Active Pixel Sensors technologies. The technological choices, design considerations and achieved performances in full Geant4 simulations are discussed and presented. The simulation studies are focused on the evaluation of the concepts. Standalone studies under laboratory conditions as well as first tests in realistic FCC-hh environment, including pileup rejection capabilities by making use of fast signals and high granularity, have been performed. These studies have been performed within the context of the preparation of the FCC conceptual design reports (CDRs).

The performance demands of future particle-physics experiments investigating the high-energy frontier pose a number of new challenges, forcing us to find improved solutions for the detection, identification, and measurement of final-state particles in subnuclear collisions. One such challenge is the precise measurement of muon momentum at very high energy, where an estimate of the curvature provided by conceivable magnetic fields in realistic detectors proves insufficient for achieving good momentum resolution when detecting, e.g., a narrow, high mass resonance decaying to a muon pair. In this work we show the feasibility of an entirely new avenue for the measurement of the energy of muons based on their radiative losses in a dense, finely segmented calorimeter. This is made possible by exploiting spatial information of the clusters of energy from radiated photons in a regression task. The use of a task-specific deep learning architecture based on convolutional layers allows us to treat the problem as one akin to image reconstruction, where images are constituted by the pattern of energy released in successive layers of the calorimeter. A measurement of muon energy with better than 20% relative resolution is shown to be achievable for ultra-TeV muons.

We propose to implement a two-stage detection strategy for exotic long-lived particles that could be produced at the CERN LHC, become trapped in detector material, and decay later. The proposed strategy relies on an array of metal rods, combined to form a high-density target. In a first stage, the rods are exposed to radiation from LHC collisions in one of the experimental caverns. In a second stage, they are individually immersed in liquid argon in a different experimental hall, where out-of-time decays could produce a detectable signal. Using a benchmark case of long-lived gluino pair production, we show that this experiment would be sensitive to a wide range of masses. Such an experiment would have unique sensitivity to gluino-neutralino mass splittings down to 3 GeV, in previously uncovered particle lifetimes ranging from days to years.

This record contains the fully-preprocessed training/validation and testing datasets used to train and evaluate the final models for "Calorimetric Measurement of Multi-TeV Muons via Deep Regression" by Jan Kieseler, Giles C. Strong, Filippo Chiandotto, Tommaso Dorigo, &amp; Lukas Layer, (2021), arXiv:2107.02119 [physics.ins-det] (https://arxiv.org/abs/2107.02119). The files are LZF-compressed HDF5 format and designed to be used directly with the code-base available at https://github.com/GilesStrong/calo_muon_regression. Please use the 'issues' tab on the GitHub repo for any questions or problems with these datasets. The training dataset consists of 886,716 muons with energies in the continuous range [50,8000] GeV split into 36 subsamples (folds). The zeroth fold of this dataset is used as our validation data. The testing dataset contains 429,750 muons, generated at fixed values of muon energy (E=100, 500, 900, 1300, 1700, 2100, 2500, 2900, 3300, 3700, 4100 GeV), and split into 18 folds. The input features are the raw hits in the calorimeter (stored in a sparse COO representation), and the high-level features discussed in the paper.