25th International Conference on Computing in High Energy & Nuclear Physics

Session

Artificial Intelligence

18 May 2021, 10:50

Conveners

Artificial Intelligence: Tue AM

• Simone Pigazzini (ETH Zurich (CH))
• Eduardo Rodrigues (University of Liverpool (GB))

Artificial Intelligence: Tue PM

• Patrick Fuhrmann (Deutsches Elektronen-Synchrotron (DE))
• Sofia Vallecorsa (CERN)

Artificial Intelligence: Wed AM

• Agnieszka Dziurda (Polish Academy of Sciences (PL))
• Joosep Pata (National Institute of Chemical Physics and Biophysics (EE))

Artificial Intelligence: Wed PM

• Joosep Pata (National Institute of Chemical Physics and Biophysics (EE))
• Agnieszka Dziurda (Polish Academy of Sciences (PL))

Artificial Intelligence: Thu AM

• Jason Webb (Brookhaven National Lab)
• Gian Michele Innocenti (CERN)

Artificial Intelligence: Thu PM

• Graeme A Stewart (CERN)
• Jason Webb (Brookhaven National Lab)

Presentation materials

Mattermost

Video recording Thursday afternoon

Video recording Thursday morning

Video recording Tuesday afternoon

Video recording Tuesday morning

Video recording Wednesday afternoon

Video recording Wednesday morning

Zoom

126. C++ Code Generation for Fast Inference of Deep Learning Models in ROOT/TMVA

Sitong An (CERN, Carnegie Mellon University (US))

18/05/2021, 10:50

Offline Computing

Short Talk

We report the latest development in ROOT/TMVA, a new system that takes trained ONNX deep learning models and emits C++ code that can be easily included and invoked for fast inference of the model, with minimal dependency. We present an overview of the current solutions for conducting inference in C++ production environment, discuss the technical details and examples of the generated code, and...

187. Deep learning based low-dose synchrotron radiation CT reconstruction

Ling Li (Institute of High Energy Physics, CAS;University of Chinese Academy of Sciences)

18/05/2021, 11:03

Offline Computing

Short Talk

Synchrotron radiation sources are widely used in various fields, among which computed tomography (CT) is one of the most important fields. The amount of effort expended by the operator varies depending on the subject. If the number of angles needed to be used can be greatly reduced under the condition of similar imaging effects, the working time and workload of the experimentalists will be...

214. Intelligent compression for synchrotron radiation source image

Shiyuan Fu

18/05/2021, 11:16

Offline Computing

Short Talk

Synchrotron radiation sources (SRS) produce a huge amount of image data. This scientific data, which needs to be stored and transferred losslessly, will bring great pressure on storage and bandwidth. The SRS images have the characteristics of high frame rate and high resolution, and traditional image lossless compression methods can only save up to 30% in size. Focus on this problem, we...

120. Event Classification with Multi-step Machine Learning

Masahiko Saito (University of Tokyo (JP))

18/05/2021, 11:29

Offline Computing

Short Talk

The usefulness and valuableness of Multi-step ML, where a task is organized into connected sub-tasks with known intermediate inference goals, as opposed to a single large model learned end-to-end without intermediate sub-tasks, is presented. Pre-optimized ML models are connected and better performance is obtained by re-optimizing the connected one. The selection of a ML model from several...

67. The use of Boosted Decision Trees for Energy Reconstruction in JUNO experiment

Mr Arsenii Gavrikov (HSE University)

18/05/2021, 11:42

Offline Computing

Short Talk

The Jiangmen Underground Neutrino Observatory (JUNO) is a neutrino experiment with a broad physical program. The main goals of JUNO are the determination of the neutrino mass ordering and high precision investigation of neutrino oscillation properties. The precise reconstruction of the event energy is crucial for the success of the experiment.
JUNO is equiped with 17 612 + 25 600 PMT...

69. A Deep Learning approach to LHCb Calorimeter reconstruction using a Cellular Automaton

Nuria Valls Canudas (La Salle, Ramon Llull University (ES))

18/05/2021, 15:00

Online Computing

Short Talk

The optimization of reconstruction algorithms has become a key aspect in LHCb as it is currently undergoing a major upgrade that will considerably increase the data processing rate. Aiming to accelerate the second most time consuming reconstruction process of the trigger, we propose an alternative reconstruction algorithm for the Electromagnetic Calorimeter of LHCb. Together with the use of...

131. Fast simulation of the electromagnetic calorimeter response using Self-Attention Generative Adversarial Networks

Alexander Rogachev (Yandex School of Data Analysis (RU))

18/05/2021, 15:13

Offline Computing

Short Talk

Simulation is one of the key components in high energy physics. Historically it relies on the Monte Carlo methods which require a tremendous amount of computation resources. These methods may have difficulties with the expected High Luminosity Large Hadron Collider need, so the experiment is in urgent need of new fast simulation techniques. The application of Generative Adversarial Networks is...

159. Graph Variational Autoencoder for Detector Reconstruction and Fast Simulation in High-Energy Physics

Ali Hariri (American University of Beirut (LB))

18/05/2021, 15:26

Offline Computing

Short Talk

Accurate and fast simulation of particle physics processes is crucial for the high-energy physics community. Simulating particle interactions with the detector is both time consuming and computationally expensive. With its proton-proton collision energy of 13 TeV, the Large Hadron Collider is uniquely positioned to detect and measure the rare phenomena that can shape our knowledge of new...

199. Particle identification with an electromagnetic calorimeter using a Convolutional Neural Network

Mr Alex Rua Herrera (DS4DS, La Salle, Universitat Ramon Llull)

18/05/2021, 15:39

Online Computing

Short Talk

Based on the fact that showers in calorimeters depend on the type of particle, this note attempts to perform a particle classifier for electromagnetic and hadronic particles on an electromagnetic calorimeter, based on the energy deposit of individual cells. Using data from a Geant4 simulation of a proposal of a Crystal Fiber Calorimeter (SPACAL), foreseen for a future upgrade of the LHCb...

175. Conditional Wasserstein Generative Adversarial Networks for Fast Detector Simulation

John Blue (Davidson College)

18/05/2021, 15:52

Offline Computing

Short Talk

Detector simulation in high energy physics experiments is a key yet computationally expensive step in the event simulation process. There has been much recent interest in using deep generative models as a faster alternative to the full Monte Carlo simulation process in situations in which the utmost accuracy is not necessary. In this work we investigate the use of conditional Wasserstein...

113. Pixel Detector Background Generation using Generative Adversarial Networks at Belle II

Mr Hosein Hashemi (LMU)

19/05/2021, 10:50

Offline Computing

Short Talk

The pixel vertex detector (PXD) is an essential part of the Belle II detector recording particle positions. Data from the PXD and other sensors allow us to reconstruct particle tracks and decay vertices. The effect of background hits on track reconstruction is simulated by adding measured or simulated background hit patterns to the hits produced by simulated signal particles. This model...

163. Machine learning for surface prediction in ACTS

Mr Benjamin Huth (Universität Regensburg)

19/05/2021, 11:03

Offline Computing

Short Talk

We present an ongoing R&D activity for machine-learning-assisted navigation through detectors to be used for track reconstruction. We investigate different approaches of training neural networks for surface prediction and compare their results. This work is carried out in the context of the ACTS tracking toolkit.

92. Deep neural network techniques in the calibration of space-charge distortion fluctuations for the ALICE TPC

Ernst Hellbar (GSI - Helmholtzzentrum fur Schwerionenforschung GmbH (DE))

19/05/2021, 11:16

Offline Computing

Short Talk

The Time Projection Chamber (TPC) of the ALICE experiment at the CERN LHC was upgraded for Run 3 and Run 4. Readout chambers based on Gas Electron Multiplier (GEM) technology and a new readout scheme allow continuous data taking at the highest interaction rates expected in Pb-Pb collisions. Due to the absence of a gating grid system, a significant amount of ions created in the multiplication...

185. Accelerating End-to-End Deep Learning for Particle Reconstruction using CMS open data

Davide Di Croce (University of Alabama (US))

19/05/2021, 11:29

Offline Computing

Short Talk

Machine learning algorithms are gaining ground in high energy physics for applications in particle and event identification, physics analysis, detector reconstruction, simulation and trigger. Currently, most data-analysis tasks at LHC experiments benefit from the use of machine learning. Incorporating these computational tools in the experimental framework presents new challenges.
This...

180. Development of FPGA-based neural network regression models for the ATLAS Phase-II barrel muon trigger upgrade

Rustem Ospanov (University of Science and Technology of China)

19/05/2021, 11:42

Online Computing

Short Talk

Effective selection of muon candidates is the cornerstone of the LHC physics programme. The ATLAS experiment uses the two-level trigger system for real-time selections of interesting events. The first-level hardware trigger system uses the Resistive Plate Chamber detector (RPC) for selecting muon candidates in the central (barrel) region of the detector. With the planned upgrades, the entirely...

81. Progress in developing a hybrid deep learning algorithm for identifying and locating primary vertices

Simon Akar (University of Cincinnati (US))

19/05/2021, 17:40

Online Computing

Short Talk

The locations of proton-proton collision points in LHC experiments
are called primary vertices (PVs). Preliminary results of a hybrid deep learning
algorithm for identifying and locating these, targeting the Run 3 incarnation
of LHCb, have been described at conferences in 2019 and 2020. In the past
year we have made significant progress in a variety of related areas. Using
two newer...

167. Graph Neural Network for Object Reconstruction in Liquid Argon Time Projection Chambers

Jeremy Edmund Hewes (University of Cincinnati (US))

19/05/2021, 17:53

Offline Computing

Short Talk

This paper presents a graph neural network (GNN) technique for low-level reconstruction of neutrino interactions in a Liquid Argon Time Projection Chamber (LArTPC). GNNs are still a relatively novel technique, and have shown great promise for similar reconstruction tasks in the LHC. In this paper, a multihead attention message passing network is used to classify the relationship...

110. Event vertex reconstruction with deep neural networks for the DarkSide-20k experiment

Victor Goicoechea Casanueva (University of Hawai'i at Manoa (US))

19/05/2021, 18:06

Offline Computing

Short Talk

While deep learning techniques are becoming increasingly more popular in high-energy and, since recently, neutrino experiments, they are less confidently used in direct dark matter searches based on dual-phase noble gas TPCs optimized for low-energy signals from particle interactions.
In the present study, application of modern deep learning methods for event ver- tex reconstruction is...

212. Evolutionary Algorithms for Tracking Algorithm Parameter Optimization

Peter Chatain (Stanford)

19/05/2021, 18:19

Offline Computing

Short Talk

The reconstruction of charged particle trajectories, known as tracking, is one of the most complex and CPU consuming parts of event processing in high energy particle physics experiments. The most widely used and best performing tracking algorithms require significant geometry-specific tuning of the algorithm parameters to achieve best results. In this paper, we demonstrate the usage of...

76. AI Enabled Data Quality Monitoring with Hydra

Thomas Britton (JLab)

19/05/2021, 18:32

Online Computing

Short Talk

Data quality monitoring is critical to all experiments impacting the quality of any physics results. Traditionally, this is done through an alarm system, which detects low level faults, leaving higher level monitoring to human crews. Artificial Intelligence is beginning to find its way into scientific applications, but comes with difficulties, relying on the acquisition of new skill sets,...

31. Decoding Photons: Physics in the Latent Space of a BIB-AE Generative Network

Erik Buhmann (Hamburg University (DE))

20/05/2021, 10:50

Offline Computing

Short Talk

Given the increasing data collection capabilities and limited computing resources of future collider experiments, interest in using generative neural networks for the fast simulation of collider events is growing. In our previous study, the Bounded Information Bottleneck Autoencoder (BIB-AE) architecture for generating photon showers in a high-granularity calorimeter showed a high accuracy...

146. Distributed training and scalability for the particle clustering method UCluster

Olga Sunneborn Gudnadottir (Uppsala University (SE))

20/05/2021, 11:03

Distributed Computing, Data Management and Facilities

Short Talk

In recent years, machine learning methods have become increasingly important for the experiments of the Large Hadron Collider (LHC). They are utilized in everything from trigger systems to reconstruction to data analysis. The recent UCluster method is a general model providing unsupervised clustering of particle physics data, that can be easily modified for a variety of different tasks. In...

186. Training and Serving ML workloads with Kubeflow at CERN

Dejan Golubovic (CERN)

20/05/2021, 11:16

Distributed Computing, Data Management and Facilities

Short Talk

Machine Learning (ML) has been growing in popularity in multiple areas and groups at CERN, covering fast simulation, tracking, anomaly detection, among many others. We describe a new service available at CERN, based on Kubeflow and managing the full ML lifecycle: data preparation and interactive analysis, large scale distributed model training and model serving. We cover specific features...

207. Accelerating GAN training using highly parallel hardware on public cloud

Renato Paulo Da Costa Cardoso (Universidade de Lisboa (PT))

20/05/2021, 11:29

Distributed Computing, Data Management and Facilities

Short Talk

With the increasing number of Machine and Deep Learning applications in High Energy Physics, easy access to dedicated infrastructure represents a requirement for fast and efficient R&D. This work explores different types of cloud services to train a Generative Adversarial Network (GAN) in a parallel
environment, using Tensorflow data parallel strategy. More specifically, we parallelize the...

213. Multi-particle reconstruction in the High Granularity Calorimeter using object condensation and graph neural networks

Shah Rukh Qasim (Manchester Metropolitan University (GB))

20/05/2021, 11:42

Offline Computing

Short Talk

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...

128. Physics Validation of Novel Convolutional 2D Architectures for Speeding Up High Energy Physics Simulations

Florian Rehm (RWTH Aachen University (DE))

20/05/2021, 15:00

Offline Computing

Short Talk

The precise simulation of particle transport through detectors is a key element for the successful interpretation of high energy physics results.
However, Monte Carlo based simulation is extremely demanding in terms of computing resources. This challenge motivates investigations of faster, alternative approaches for replacing the standard Monte Carlo approach.

We apply Generative...

189. Reframing Jet Physics with New Computational Methods

Sebastian Macaluso (New York University)

20/05/2021, 15:13

Offline Computing

Short Talk

We reframe common tasks in jet physics in probabilistic terms, including jet reconstruction, Monte Carlo tuning, matrix element – parton shower matching for large jet multiplicity, and efficient event generation of jets in complex, signal-like regions of phase space. We also introduce Ginkgo, a simplified, generative model for jets, that facilitates research into these tasks with techniques...

198. Artificial Proto-Modelling: Building Precursors of a Next Standard Model from Simplified Model Results

Wolfgang Waltenberger (Austrian Academy of Sciences (AT))

20/05/2021, 15:26

Offline Computing

Short Talk

We present a novel algorithm to identify potential dispersed signals of new physics in the slew of published LHC results. It employs a random walk algorithm to introduce sets of new particles, dubbed “proto-models”, which are tested against simplified-model results from ATLAS and CMS (exploiting the SModelS software framework). A combinatorial algorithm identifies the set of analyses and/or...

169. Jet Single Shot Detection

Adrian Alan Pol (CERN)

20/05/2021, 15:39

Online Computing

Short Talk

In this paper, we apply object detection techniques based on convolutional neural networks to jet images, where the input data corresponds to the calorimeter energy deposits. In particular, we focus on the CaloJet reconstruction and tagging as a detection task with a Single Shot Detection network, called Jet-SSD. The model performs simultaneous localization and classification and additional...

179. End-to-End Jet Classification of Boosted Top Quarks with CMS Open Data

Bjorn Burkle (Brown University (US))

20/05/2021, 15:52

Online Computing

Short Talk

We describe a novel application of the end-to-end deep learning technique to the task of discriminating top quark-initiated jets from those originating from the hadronization of a light quark or a gluon. The end-to-end deep learning technique combines deep learning algorithms and low-level detector representation of the high-energy collision event. In this study, we use low-level detector...