26TH INTERNATIONAL CONFERENCE ON COMPUTING IN HIGH ENERGY & NUCLEAR PHYSICS (CHEP2023)

Session

Track 9 - Artificial Intelligence and Machine Learning

May 8, 2023, 11:00 AM

Hampton Roads VII (Norfolk Waterside Marriott)

Conveners

Track 9 - Artificial Intelligence and Machine Learning: Applications 1

• SOFIA VALLECORSA (CERN)
• Jana Schaarschmidt (University of Washington (US))

Track 9 - Artificial Intelligence and Machine Learning: Applications 2

• Sandro Wenzel (CERN)
• Stefano Dal Pra (INFN-CNAF)

Track 9 - Artificial Intelligence and Machine Learning: Reconstruction

• Jana Schaarschmidt (University of Washington (US))
• Sandro Wenzel (CERN)

Track 9 - Artificial Intelligence and Machine Learning: Tracking

• Sandro Wenzel (CERN)
• SOFIA VALLECORSA (CERN)

Track 9 - Artificial Intelligence and Machine Learning: Analysis

• SOFIA VALLECORSA (CERN)
• Jana Schaarschmidt (University of Washington (US))

Track 9 - Artificial Intelligence and Machine Learning: General Methods and Tools

• Jana Schaarschmidt (University of Washington (US))
• Stefano Dal Pra (INFN-CNAF)

Track 9 - Artificial Intelligence and Machine Learning: Online Applications

• SOFIA VALLECORSA (CERN)
• Stefano Dal Pra (INFN-CNAF)
• Jana Schaarschmidt (University of Washington (US))

43. MLHad: Simulating Hadronization with Machine Learning

Wilkinson, Michael (University of Cincinnati)

5/8/23, 11:00 AM

Track 9 - Artificial Intelligence and Machine Learning

Oral

Hadronization is an important step in Monte Carlo event generators, where quarks and gluons are bound into physically observable hadrons. Today’s generators rely on finely-tuned empirical models, such as the Lund string model; while these models have been quite successful overall, there remain phenomenological areas where they do not match data well. In this talk, we present MLHad, a...

262. SYMBA: Symbolic Computation of Squared Amplitudes in High Energy Physics with Machine Learning

Mr Alnuqaydan, Abdulhakim (University of Kentucky)

5/8/23, 11:15 AM

Track 9 - Artificial Intelligence and Machine Learning

Oral

The calculation of particle interaction squared amplitudes is a key step in the calculation of cross sections in high-energy physics. These lengthy calculations are currently done using domain-specific symbolic algebra tools, where the time required for the calculations grows rapidly with the number of final state particles involved. While machine learning has proven to be highly successful in...

542. On Estimating Gradients of Discrete Stochastic Programs for Detector Design Optimization

Heinrich, Lukas (Technical University of Munich)

5/8/23, 11:30 AM

Track 9 - Artificial Intelligence and Machine Learning

Oral

The recent advances in Machine Learning and high-dimensional gradient-based optimization has led to increased interest in the question of whether we can use such methods to optimize the design of future detectors for high-level physics objectives. However this program faces a fundamental obstacle: The quality of a detector design must be judged on the physics inference it enables, but both...

601. Multi-Module based VAE to predict HVCM faults in the SNS accelerator

Mr Alanazi, Yasir (Jefferson Lab)

5/8/23, 11:45 AM

Track 9 - Artificial Intelligence and Machine Learning

Oral

We present a Multi-Module framework based on Conditional Variational Autoencoder (CVAE) to detect anomalies in the High Voltage Converter Modulators (HVCMs) which have historically been a cause of major down time for the Spallation Neutron Source (SNS) facility. Previous studies using machine learning techniques were to predict faults ahead of time in the SNS accelerator using a Single...

132. Resilient Variational Autencoder for Unsupervised Anomaly Detection at the SLAC Linac Cohrerent Light Source

Humble, Ryan (Stanford University)

5/8/23, 12:00 PM

Track 9 - Artificial Intelligence and Machine Learning

Oral

Significant advances in utilizing deep learning for anomaly detection have been made in recent years. However, these methods largely assume the existence of a normal training set (i.e., uncontaminated by anomalies), or even a completely labeled training set. In many complex engineering systems, such as particle accelerators, labels are sparse and expensive; in order to perform anomaly...

582. Machine Learning for Etch-Pit Identification at MoEDAL

Hays, Jonathan (Queen Mary University of London)

5/8/23, 12:15 PM

Track 9 - Artificial Intelligence and Machine Learning

Oral

The MoEDAL experiment at CERN (https://home.cern/science/experiments/moedal-mapp) carries out searches for highly ionising exotic particles such as magnetic monopoles. One of the technologies deployed in this task is the Nuclear Track Detector (NTD). In the form of plastic films, these are passive detectors that are low cost and easy to handle. After exposure to the LHC collision environment...

251. Deep Learning for Amplitude Analysis in Spectroscopy

Dr Phelps, William (Christopher Newport University and Jefferson Lab)

5/8/23, 2:00 PM

Track 9 - Artificial Intelligence and Machine Learning

Oral

Abstract

We present results on Deep Learning applied to Amplitude and Partial Wave Analysis (PWA) for spectroscopic analyses. Experiments in spectroscopy often aim to observe strongly-interacting, short-lived particles that decay to multi-particle final states. These particle decays have angular distributions that our deep learning model has been trained to identify. Working with TensorFlow...

376. Machine learning based compression for scientific data

Gallén, Axel (Lund University (SE)), Ekman, Alexander (Lund University (SE))

5/8/23, 2:15 PM

Track 9 - Artificial Intelligence and Machine Learning

Oral

One common issue in vastly different fields of research and industry is the ever-increasing need for more data storage. With experiments taking more complex data at higher rates, the data recorded is quickly outgrowing the storage capabilities. This issue is very prominent in LHC experiments such as ATLAS where in five years the resources needed are expected to be many times larger than the...

439. Pion/Kaon Identification at STCF DTOF Based on Classical/Quantum Convolutional Neural Network

Li, Teng

5/8/23, 2:30 PM

Track 9 - Artificial Intelligence and Machine Learning

Oral

The Super Tau Charm Facility (STCF) proposed in China is a new-generation electron–positron collider with center-of-mass energies covering 2-7 GeV. In STCF, the discrimination of high momentum hadrons is a challenging and critical task for various physics studies. In recent years, machine learning methods have gradually become one of the mainstream methods in the PID field of high energy...

113. Particle identification with machine learning in ALICE Run 3

Kabus, Maja (CERN / Warsaw University of Technology)

5/8/23, 2:45 PM

Track 9 - Artificial Intelligence and Machine Learning

Oral

The main focus of the ALICE experiment, quark-gluon plasma measurements, requires
accurate particle identification (PID). The ALICE detectors allow identifying particles over a broad momentum interval ranging from about 100 MeV/c up to 20 GeV/c.

However, hand-crafted selections and the Bayesian method do not perform well in the
regions where the particle signals overlap. Moreover, an ML...

476. Flashsim: a ML based simulation for analysis datatiers

Vaselli, Francesco (Scuola Normale Superiore & INFN Pisa)

5/8/23, 3:00 PM

Track 9 - Artificial Intelligence and Machine Learning

Oral

Analyses in HEP experiments often rely on large MC simulated datasets. These datasets are usually produced with full-simulation approaches based on Geant4 or exploiting parametric “fast” simulations introducing approximations and reducing the computational cost. With our work we created a prototype version of a new “fast” simulation that we named “flashsim” targeting analysis level data tiers...

424. FastCaloGAN: a fast simulation of the ATLAS Calorimeter with GANs

Giannelli, Michele Faucci (INFN Roma Tor Vergata and Universita' di Roma Tor Vergata)

5/8/23, 3:15 PM

Track 9 - Artificial Intelligence and Machine Learning

Oral

AtlFast3 is the new ATLAS fast simulation that exploits a wide range of ML techniques to achieve high-precision fast simulation. The latest version of the AtlFast3 used in Run3 deploys FastCaloGANV2 which consists of 500 Generative Adversarial Networks used to simulate the showers of all particles in the ATLAS calorimeter system. The Muon Punch Through tool has also been completely rewritten...

138. A deep-learning reconstruction algorithm for cluster counting

Dr Wu, Linghui (Institute of High Energy Physics)

5/9/23, 11:00 AM

Track 9 - Artificial Intelligence and Machine Learning

Oral

Future e+e- colliders are crucial to extend the search for new phenomena possibly related to the open questions that the Standard Model presently does not explain. Among the major physics programs, the flavor physics program requires particle identification (PID) performances well beyond that of most detectors designed for the current generation. The cluster counting, which measures the number...

309. Scalable, End-to-End, Machine-Learning-Based Data Reconstruction Chain for Particle Imaging Detectors

Drielsma, Francois (SLAC)

5/9/23, 11:15 AM

Track 9 - Artificial Intelligence and Machine Learning

Oral

Recent inroads in Computer Vision (CV), enabled by Machine Learning (ML), have motivated a new approach to the analysis of particle imaging detector data. Unlike previous efforts which tackled isolated CV tasks, this paper introduces an end-to-end, ML-based data reconstruction chain for Liquid Argon Time Projection Chambers (LArTPCs), the state-of-the-art in precision imaging at the intensity...

425. HyperTrack: neural combinatorics for high energy physics

Mieskolainen, Mikael (Imperial College London)

5/9/23, 11:30 AM

Track 9 - Artificial Intelligence and Machine Learning

Oral

I will introduce a new neural algorithm -- HyperTrack, designed for exponentially demanding combinatorial inverse problems of high energy physics final state reconstruction and high-level analysis at the LHC and beyond. Many of these problems can be formulated as clustering on a graph resulting in a hypergraph. The algorithm is based on a machine learned geometric-dynamical input graph...

463. Implicit Neural Representation as a Differentiable Surrogate for Photon Propagation in a Monolithic Neutrino Detector

Tsang, Patrick (SLAC)

5/9/23, 11:45 AM

Track 9 - Artificial Intelligence and Machine Learning

Oral

Modern neutrino experiments employ hundreds to tens of thousands of photon detectors to detect scintillation photons produced from the energy deposition of charged particles. A traditional approach of modeling individual photon propagation as a look-up table requires high computational resources, and therefore it is not scalable for future experiments with multi-kiloton target volume.

We...

41. Neutrino interaction vertex-finding in a DUNE far-detector using Pandora deep-learning

Chappell, Andrew (University of Warwick)

5/9/23, 12:00 PM

Track 9 - Artificial Intelligence and Machine Learning

Oral

The Deep Underground Neutrino Experiment (DUNE) will operate four large-scale Liquid-Argon Time-Projection Chambers (LArTPCs) at the far site in South Dakota, producing high-resolution images of neutrino interactions.

LArTPCs represent a step-change in neutrino interaction imaging and the resultant images can be highly detailed and complex. Extracting the maximum value from LArTPC hardware...

460. Graph Neural Network for 3D Reconstruction in Liquid Argon Time Projection Chambers

Hewes, V (University of Cincinnati)

5/9/23, 12:15 PM

Track 9 - Artificial Intelligence and Machine Learning

Oral

The Exa.TrkX team has developed a Graph Neural Network (GNN) for reconstruction of liquid argon time projection chamber (LArTPC) data. We discuss the network architecture, a multi-head attention message passing network that classifies detector hits according to the particle type that produced them. By utilizing a heterogeneous graph structure with independent subgraphs for each 2D plane’s hits...

600. End-to-End Geometric Representation Learning for Track Reconstruction

Calafiura, Paolo (LBNL)

5/9/23, 2:00 PM

Track 9 - Artificial Intelligence and Machine Learning

Oral

Significant progress has been made in applying graph neural networks (GNNs) and other geometric ML ideas to the track reconstruction problem. State-of-the-art results are obtained using approaches such as the Exatrkx pipeline, which currently applies separate edge construction, classification and segmentation stages. One can also treat the problem as an object condensation task, and cluster...

621. An Object Condensation Pipeline for Charged Particle Tracking

Lieret, Kilian (Princeton University)

5/9/23, 2:15 PM

Track 9 - Artificial Intelligence and Machine Learning

Oral

Recent work has demonstrated that graph neural networks (GNNs) trained for charged particle tracking can match the performance of traditional algorithms while improving scalability. Most approaches are based on the edge classification paradigm, wherein tracker hits are connected by edges, and a GNN is trained to prune edges, resulting in a collection of connected components representing...

440. BESIII track reconstruction algorithm based on machine learning

Jia, Xiaoqian (Shandong University, CN)

5/9/23, 2:30 PM

Track 9 - Artificial Intelligence and Machine Learning

Oral

Track reconstruction is one of the most important and challenging tasks in the offline data processing of collider experiments. For the BESIII detector working in the tau-charm energy region, plenty of efforts were made previously to improve the tracking performance with traditional methods, such as template matching and Hough transform etc. However, for difficult tracking tasks, such as the...

12. Track Identification for CLAS12 using Artificial Intelligence

Gavalian, Gagik (Jefferson Lab)

5/9/23, 2:45 PM

Track 9 - Artificial Intelligence and Machine Learning

Oral

Particle track reconstruction is the most computationally intensive process in nuclear physics experiments.
Traditional algorithms use a combinatorial approach that exhaustively tests track measurements (hits) to
identify those that form an actual particle trajectory. In this article we describe the development of machine
learning models that assist the tracking algorithm by identifying...

550. Novel fully-heterogeneous GNN designs for track reconstruction at the HL-LHC

Mr Caillou, Sylvain (L2IT, Toulouse)

5/9/23, 3:00 PM

Track 9 - Artificial Intelligence and Machine Learning

Oral

Data from the LHC detectors are not easily represented using regular data structures. These detectors are comprised of several species of subdetectors and therefore produce heterogeneous data. LHC detectors are granular by design so that nearby particles may be distinguished. As a consequence, LHC data are sparse, in that many detector channels are not active during a given collision event....

102. Advances in developing deep neural networks for finding primary vertices in proton-proton collisions at the LHC

Garg, Rocky (Stanford University), Sokoloff, Michael (University of Cincinnati)

5/9/23, 3:15 PM

Track 9 - Artificial Intelligence and Machine Learning

Oral

We have been studying the use of deep neural networks (DNNs) to identify and locate primary vertices (PVs) in proton-proton collisions at the LHC. Earlier work focused on finding primary vertices in simulated LHCb data using a hybrid approach that started with kernel density estimators (KDEs) derived from the ensemble of charged track parameters heuristically and predicted “target histogram”...

348. Uncertainty Aware Machine Learning Models for Particle Physics Applications

Rajput, Kishansingh (Thomas Jefferson National Accelerator Facility)

5/9/23, 4:30 PM

Track 9 - Artificial Intelligence and Machine Learning

Oral

Machine learning (ML) and deep learning (DL) are powerful tools for modeling complex systems. However, most of the standard models in ML/DL do not provide a measure of confidence or uncertainties associated with their predictions. Further, these models can only be trained on available data. During operation, models may encounter data samples poorly reflected in training data. These data...

581. Exploring Interpretability of Deep Neural Networks in Top Tagging

Roy, Avik (University of Illinois at Urbana-Champaign)

5/9/23, 4:45 PM

Track 9 - Artificial Intelligence and Machine Learning

Oral

We explore interpretability of deep neural network (DNN) models designed for identifying jets coming from top quark decay in the high energy proton-proton collisions at the Large Hadron Collider (LHC). Using state-of-the-art methods of explainable AI (XAI), we identify which features play the most important roles in identifying the top jets, how and why feature importance varies across...

259. Fast Inclusive Flavor Tagging at LHCb

Prouve, Claire (Universidade de Santiago de Compostela (ES))

5/9/23, 5:00 PM

Track 9 - Artificial Intelligence and Machine Learning

Oral

The task of identifying B meson flavor at the primary interaction point in the LHCb detector is crucial for measurements of mixing and time-dependent CP violation.

Flavor tagging is usually done with a small number of expert systems that find important tracks to infer the B flavor from.

Recent advances show that replacing all of those expert systems with one ML algorithm that considers...

327. Using a Neural Network to Approximate the Negative Log Likelihood Distribution

Liu, Shenghua (University of Notre Dame)

5/9/23, 5:15 PM

Track 9 - Artificial Intelligence and Machine Learning

Oral

An increasingly frequent challenge faced in HEP data analysis is to characterize the agreement between a prediction that depends on a dozen or more model parameters–such as predictions coming from an effective field theory (EFT) framework–and the observed data. Traditionally, such characterizations take the form of a negative log likelihood (NLL) distribution, which can only be evaluated...

336. Efficient search for new physics using Active Learning in the ATLAS Experiment

Bhatti, Zubair (New York University)

5/9/23, 5:30 PM

Track 9 - Artificial Intelligence and Machine Learning

Oral

Searches for new physics set exclusion limits in parameter spaces of typically up to 2 dimensions. However, the relevant theory parameter space is usually of a higher dimension but only a subspace is covered due to the computing time requirements of signal process simulations. An Active Learning approach is presented to address this limitation. Compared to the usual grid sampling, it reduces...

226. A method for inferring signal strength modifiers by conditional invertible neural networks

Farkas, Máté Zoltán (CMS)

5/9/23, 5:45 PM

Track 9 - Artificial Intelligence and Machine Learning

Oral

The continuous growth in model complexity in high-energy physics (HEP) collider experiments demands increasingly time-consuming model fits. We show first results on the application of conditional invertible networks (cINNs) to this challenge. Specifically, we construct and train a cINN to learn the mapping from signal strength modifiers to observables and its inverse. The resulting network...

154. JetNet library for machine learning in high energy physics

Pareja, Carlos

5/11/23, 11:15 AM

Track 9 - Artificial Intelligence and Machine Learning

Oral

Machine learning (ML) has become ubiquitous in high energy physics (HEP) for many tasks, including classification, regression, reconstruction, and simulations. To facilitate development in this area, and to make such research more accessible, and reproducible, we require standard, easy-to-access, datasets and metrics. To this end, we develop the open source Python JetNet library with easily...

616. Artificial Intelligence and Machine Learning for EPIC: an Overview

Fanelli, Cristiano

5/11/23, 11:30 AM

Track 9 - Artificial Intelligence and Machine Learning

Oral

The newly formed EPIC Collaboration has recently laid the foundations of its software infrastructure. Noticeably, several forward-looking aspects of the software are favorable for Artificial Intelligence (AI) and Machine Learning (ML) applications and utilization of heterogeneous resources. EPIC has a unique opportunity to integrate AI/ML from the beginning: the number of AI/ML activities is...

478. New developments of TMVA/SOFIE: Code Generation and Fast Inference for Graph Neural Networks

Moneta, Lorenzo (CERN)

5/11/23, 11:45 AM

Track 9 - Artificial Intelligence and Machine Learning

Oral

The recent developments in ROOT/TMVA focus on fast machine learning inference, which enables analysts to deploy their machine learning models rapidly on large scale datasets. A new tool has been recently developed, SOFIE, allowing for generating C++ code for evaluation of deep learning models, which are trained from external tools such as Tensorflow or PyTorch.
While Python-based deep...

166. Embedded Continual Learning for HEP

Barbone, Marco (Imperial College London)

5/11/23, 12:00 PM

Track 9 - Artificial Intelligence and Machine Learning

Oral

Neural Networks (NN) are often trained offline on large datasets and deployed on specialized hardware for inference, with a strict separation between training and inference. However, in many realistic applications the training environment differs from the real world or data arrive in a streaming fashion and are continuously changing. In these scenarios, the ability to continuously train and...

82. FAIR AI Models in High Energy Physics

Li, Haoyang (UCSD)

5/11/23, 12:15 PM

Track 9 - Artificial Intelligence and Machine Learning

Oral

The findable, accessible, interoperable, and reusable (FAIR) data principles have provided a framework for examining, evaluating, and improving how we share data with the aim of facilitating scientific discovery. Efforts have been made to generalize these principles to research software and other digital products. Artificial intelligence (AI) models---algorithms that have been trained on data...

532. Symbolic Regression on FPGAs for Fast Machine Learning Inference

Mr Tsoi, Ho Fung (University of Wisconsin Madison)

5/11/23, 12:30 PM

Track 9 - Artificial Intelligence and Machine Learning

Oral

The high-energy physics community is investigating the feasibility of deploying more machine-learning-based solutions on FPGAs to meet modern physics experiments' sensitivity and latency demands. In this contribution, we introduce a novel end-to-end procedure that utilises a forgotten method in machine learning, i.e. symbolic regression (SR). It searches equation space to discover algebraic...

48. Machine Learning for Real-Time Processing of ATLAS Liquid Argon Calorimeter Signals with FPGAs

Voigt, Johann (TU Dresden)

5/11/23, 2:00 PM

Track 9 - Artificial Intelligence and Machine Learning

Oral

The Large Hadron Collider (LHC) at CERN is the largest and most powerful particle collider today. The Phase-II Upgrade of the LHC will increase the instantaneous luminosity by a factor of 7 leading to the High Luminosity LHC (HL-LHC). At the HL-LHC, the number of proton-proton collisions in one bunch crossing (called pileup) increases significantly, putting more stringent requirements on the...

218. Applications of Lipschitz neural networks to the Run 3 LHCb trigger system

Delaney, Blaise (Massachusetts Institute of Technology)

5/11/23, 2:15 PM

Track 9 - Artificial Intelligence and Machine Learning

Oral

The data-taking conditions expected of Run 3 pose unprecedented challenges for the DAQ systems of the LHCb experiment at the LHC. The LHCb collaboration is pioneering the adoption of a fully-software trigger to cope with the expected increase in luminosity and, thus, event rate. The upgraded trigger system has required advances in the use of hardware architectures, software and algorithms....

76. The Deployment of Realtime ML in Changing Environments

Brown, Christopher (Imperial College (GB))

5/11/23, 2:30 PM

Track 9 - Artificial Intelligence and Machine Learning

Oral

The High-Luminosity LHC upgrade of the CMS experiment will utilise a large number of Machine Learning (ML) based algorithms in its hardware-based trigger. These ML algorithms will facilitate the selection of potentially interesting events for storage and offline analysis. Strict latency and resource requirements limit the size and complexity of these models due to their use in a high-speed...

129. Improved Clustering in the Belle II Electromagnetic Calorimeter with Graph Neural Networks

Haide, Isabel (Karlsruhe Institute of Technology)

5/11/23, 2:45 PM

Track 9 - Artificial Intelligence and Machine Learning

Oral

For the Belle II experiment, the electromagnetic calorimeter (ECL) plays a crucial role in both the trigger decisions and the offline analysis.
The performance of existing clustering algorithms degrades with rising backgrounds that are expected for the increasing luminosity in Belle II. In offline analyses, this mostly impacts the energy resolution for low-energy photons; for the trigger,...

170. Development of a Deep-learning based Full Event Interpretation (DFEI) algorithm for the future LHCb trigger

Mr Eschle, Jonas (University of Zürich)

5/11/23, 3:00 PM

Track 9 - Artificial Intelligence and Machine Learning

Oral

In a decade from now, the Upgrade II of LHCb experiment will face an instantaneous luminosity ten times higher than in the current Run 3 conditions. This will bring LHCb to a new era, with huge event sizes and typically several signal heavy-hadron decays per event. The trigger scope will shift from selecting interesting events to select interesting parts of multi-signal events. To allow for an...

548. Acceleration of a CMS DNN based Algorithm

Barbone, Marco (Imperial College London)

5/11/23, 3:15 PM

Track 9 - Artificial Intelligence and Machine Learning

Oral

The search for exotic long-lived particles (LLP) is a key area of the current LHC physics programme and is expected to remain so into the High-Luminosity (HL)-LHC era. As in many areas of the LHC physics programme Machine Learning algorithms play a crucial role in this area, in particular Deep Neural Networks (DNN), which are able to use large numbers of low-level features to achieve enhanced...