New Angles on Fast Calorimeter Shower Simulation

The demands placed on computational resources by the simulation requirements of high energy physics experiments motivate the development of novel simulation tools. Machine learning based generative models offer a solution that is both fast and accurate. In this work we extend the Bounded Information Bottleneck Autoencoder (BIB-AE) architecture, designed for the simulation of particle showers in highly granular calorimeters, in two key directions. First, we generalise the model to a multi-parameter conditioning scenario, while retaining a high degree of physics fidelity. In a second step, we perform a detailed study of the effect of applying a state-of-the-art particle flow-based reconstruction procedure to the generated showers. We demonstrate that the performance of the model remains high after reconstruction. These results are an important step towards creating a more general simulation tool, where maintaining physics performance after reconstruction is the ultimate target.Comment: 26 pages, 19 figure

Radio Galaxy Classification with wGAN-Supported Augmentation

Novel techniques are indispensable to process the flood of data from the new generation of radio telescopes. In particular, the classification of astronomical sources in images is challenging. Morphological classification of radio galaxies could be automated with deep learning models that require large sets of labelled training data. Here, we demonstrate the use of generative models, specifically Wasserstein GANs (wGAN), to generate artificial data for different classes of radio galaxies. Subsequently, we augment the training data with images from our wGAN. We find that a simple fully-connected neural network for classification can be improved significantly by including generated images into the training set.Comment: 10 pages, 6 figures; accepted to ml.astro; v2: matches published versio

Recent highlights of top-quark physics with the ATLAS Experiment - FPCP2020

Four recent measurements in different areas of top-quark physics are presented. Comprehensive measurements of differential cross-sections of top-quark-antiquark pair-production are performed in the all-hadronic channel. The top-quarks and top-quark-pairs are fully reconstructed and kinematic distributions of these objects are measured at the particle and at the parton level. The measurements in the all-hadronic channel complement measurements previously performed in the lepton+jets channels in terms of range and resolution. The cross sections for the production of top quark pairs in association to a photon (ttgamma) or to a Z boson (ttZ) are measured both inclusively and differentially as a function of kinematic variables characterizing the tt+boson system. Both sets of measurements use the full Run2 data set consisting of 139/fb of integrated luminosity. Final states with three and four leptons and b-jets are used to extract ttZ rates, while tt+gamma cross sections are derived from final states with one photon, one electron and one muon of opposite sign and at least two jets. The measurements are compared to predictions obtained by NLO+PS Monte Carlo and fixed order NLO calculations. The hard scattering process in which two top-quark-antiquark pairs are produced is also called four-top-quarks production and is predicted to have a small cross-section of 12 fb in the standard model. This very rare process has not been observed yet. The background is mainly given by top-quark-antiquark production in association with heavy flavor jets. In this presentation, two analyses are presented which aim to establish experimental evidence for this process based on the full Run 2 dataset recorded with the ATLAS detector. The first analysis selects events with exactly one charged lepton and several jets or two charged leptons of opposite electric charge. The second analysis is based on a lepton pair with the same electric charge or events with more than two leptons. In both channels multivariate techniques are used to optimize the separation between signal and background events and enhance the sensitivity. Finally, both channels are combined

Première évidence de la production pp → tttt dans le cadre du modèle standard et études de performance du calorimètre à tuiles d'ATLAS pour la phase HL-LHC

Two analyses in the field of particle physics are presented in this document. First, studies on the performance of the reconstruction of muons using calorimeter information under the conditions of the High-Luminosity Large-Hadron-Collider (HL-LHC) phase of the ATLAS detector. Second, the search for the Standard Model (SM) simultaneous production of four top quarks using the full Run-II data set recorded by ATLAS. This data set corresponds to an integrated luminosity of L = 139 fb−1 of proton–proton collisions at a centre of mass energy of √s = 13 TeV. Here, the performance of the reconstruction of muons is probed for different pile-up scenarios, as those expected for the HL-LHC phase, and in light of different noise scenarios that emulate the loss of energy resolution and deterioration of detector acceptance due to ageing and irradiation of detector components. This study is conducted to test proposed detector upgrade scenarios before their implementation. The search for SM like four top quark production presented here, focuses on the decay modes with two same sign or more leptons in the final state. The search for this process is, among other factors, motivated by the very large energies involved and by the fact that it is likely on the verge of being discovered with currently available data sets. The final results are obtained in a profile likelihood fit involving the outcome of a boosted decision tree trained to discriminate between signal and background. The fit results in a production cross section of [1], which corresponds to an observed (expected) significance of Z = 4.3 (Z = 2.4). This represents the first evidence for this process. The obtained result is compatible with the SM prediction [2] within 1.7 standard deviations.Following first evidence, the possibility of reconstructing the four top quark system using a kinematic likelihood approach is developed and tested. These developments are performed with the KLFitter [3] tool set and yield an efficiency of correctly matching all four top quarks of ε = 33 ± 4% under optimal conditions in the single lepton final state. [1] ATLAS Collaboration. ‘Evidence for tt̄tt̄ production in the multilepton final state in proton–proton collisions at √s = 13 TeV with the ATLAS detector’. Eur. Phys. J. C 80 (2020) [2]Rikkert Frederix et al. ‘Large NLO corrections in tt̄W ± and tt̄tt̄ hadroproduction from supposedly subleading EW contributions’. JHEP 02 (2018) [3]Johannes Erdmann et al. ‘A likelihood-based reconstruction algorithm for top-quark pairs and the KLFitter framework’. Nucl. Instrum. Meth. A 748 (2014)Deux analyses dans le domaine de la physique des particules sont présentées dans ce document. Premièrement, des études sur les performances de la reconstruction des muons à l’aide des informations calorimétriques dans les conditions de la phase HL-LHC du détecteur ATLAS.. Deuxièmement, la recherche de la production simultanée de quatre quarks top en utilisant l’ensemble complet des données Run-II enregistrées par ATLAS. Cet ensemble des données correspond à une luminosité intégrée de L = 139 fb−1 des collisions protons-protons à un énergie dans le centre de masse de √s = 13 TeV. La performance de la reconstruction des muons est sondée pour différentes conditions de prise de données, en particulier avec des nombres de collisions parasites importants tels qu’attendu pour la phase du HL-LHC. La performance est également sondé et en vue de différents scénarios des bruits qui émulent la perte de résolution énergétique et la détérioration de l’acceptation du détecteur due au vieillissement et à l’irradiation des composants du détecteur. Cette étude est menée pour tester les scénarios proposés de mise à jour du détecteur avant leur mise en œuvre. La recherche de la production de quatre quark top, prédite par le modèle standard (SM), présentée ici, se concentre sur les modes de désintégration avec deux leptons de même signe ou plusieurs leptons dans l’état final. La recherche de ce processus est, entre autres facteurs, motivée par les très grandes énergies impliquées et par le fait qu’il est potentiellement sur le point d’être découvert avec l’ensemble des données actuellement disponibles. Les résultats finaux sont obtenus dans l’ajustement d’une fonction de vraisemblance profilée impliquant le résultat d’un boosted decision tree, entraîné à discriminer entre le signal et les bruits de fond. L’ajustement donne une section efficace de [1], ce qui correspond à une significance observée (attendue) de Z = 4,3 (Z = 2,4). Cela correspond à la première évidence de ce processus. Le résultat obtenu est compatible avec la prédiction du SM [2] à 1,7 écart-type près. Après la première évidence, la possibilité de reconstruire le système des quatre quark top en utilisant une approche de vraisemblance cinématique est explorée. Les études sont effectuées dans l’état final avec un seul lepton avec l’outil KLFitter [3] donnant une efficacité de correspondance correcte des quatre quarks top de ε = 33 ± 4 % dans des conditions optimales. [1] ATLAS Collaboration. ‘Evidence for tt̄tt̄ production in the multilepton final state in proton–proton collisions at √s = 13 TeV with the ATLAS detector’. Eur. Phys. J. C 80 (2020) [2]Rikkert Frederix et al. ‘Large NLO corrections in tt̄W ± and tt̄tt̄ hadroproduction from supposedly subleading EW contributions’. JHEP 02 (2018) [3]Johannes Erdmann et al. ‘A likelihood-based reconstruction algorithm for top-quark pairs and the KLFitter framework’. Nucl. Instrum. Meth. A 748 (2014

New Angles on Fast Calorimeter Shower Simulation

The demands placed on computational resources by the simulation requirements of high energy physics experiments motivate the development of novel simulation tools. Machine learning based generative models offer a solution that is both fast and accurate. In this work we extend the Bounded Information Bottleneck Autoencoder (BIB-AE) architecture, designed for the simulation of particle showers in highly granular calorimeters, in two key directions. First, we generalise the model to a multi-parameter conditioning scenario, while retaining a high degree of physics fidelity. In a second step, we perform a detailed study of the effect of applying a state-of-the-art particle flow-based reconstruction procedure to the generated showers. We demonstrate that the performance of the model remains high after reconstruction. These results are an important step towards creating a more general simulation tool, where maintaining physics performance after reconstruction is the ultimate target

Hadrons, better, faster, stronger

Motivated by the computational limitations of simulating interactions of particles in highly-granular detectors, there exists a concerted effort to build fast and exact machine-learning-based shower simulators. This work reports progress on two important fronts. First, the previously investigated Wasserstein generative adversarial network and bounded information bottleneck autoencoder generative models are improved and successful learning of hadronic showers initiated by charged pions in a segment of the hadronic calorimeter of the International Large Detector is demonstrated for the first time. Second, we consider how state-of-the-art reconstruction software applied to generated shower energies affects the obtainable energy response and resolution. While many challenges remain, these results constitute an important milestone in using generative models in a realistic setting

Generative Models for Shower Simulation in HEP

Simulation in High Energy Physics (HEP) places a heavy burden on the availablecomputing resources and is expected to become a major bottleneck for the upcoming highluminosity phase of the LHC and for future Higgs factories, motivating a concerted effort todevelop computationally efficient solutions. Methods based on generative machine learningmethods hold promise to alleviate the computational strain produced by simulation whileproviding the physical accuracy required of a surrogate simulator.In this contribution, an overview of a growing body of work focused on simulatingshowers in highly granular calorimeters will be reported, which is making significant stepstowards realistic fast simulation tools based on deep generative models. Progress on thesimulation of both electromagnetic and hadronic showers will be presented, with a focus onthe high degree of physical fidelity and computational performance achieved. Additional stepstaken to address the challenges faced when broadening the scope of these simulators, such asthose posed by multi-parameter conditioning, will also be discussed

Fast and Accurate Electromagnetic and Hadronic Showers from Generative Models

Generative machine learning models offer a promising way to efficiently amplify classical Monte Carlo generators’ statistics for event simulation and generation in particle physics. Given the already high computational cost of simulation and the expected increase in data in the high-precision era of the LHC and at future colliders, such fast surrogate simulators are urgently needed. This contribution presents a status update on simulating particle showers in high granularity calorimeters for future colliders. Building on prior work using Generative Adversarial Networks (GANs), Wasserstein-GANs, and the information-theoretically motivated Bounded Information Bottleneck Autoencoder (BIB-AE), we further improve the fidelity of generated photon showers. The key to this improvement is a detailed understanding and optimisation of the latent space. The richer structure of hadronic showers compared to electromagnetic ones makes their precise modeling an important yet challenging problem. We present initial progress towards accurately simulating the core of hadronic showers in a highly granular scintillator calorimeter

Generative Models for Particle Shower Simulation in Fundamental Physics

Simulations in fundamental physics connect underlying first-principle theoriesand empirical descriptions of detectors with experimentally observed data. Theyare indispensable, e.g., in particle physics, where they underpin statistical infer-ence tasks and experiment design. However, these Monte-Carlo-based simulationsare computationally costly. Growing data volumes produced by upcoming experi-ments exacerbate this problem and need to be matched by a commensurate growthin simulated statistics for precision measurements. Generative machine learningmodels offer a potential way to leverage the effectiveness of classical simulationvia amplification. This contribution presents state-of-the-art performance for sim-ulating the interaction of two different types of elementary particles with differentcharacteristics — photons and pions — with high granularity detectors of 27k and8k read-out channels, respectivel