ML4Jets2024

Session

Reconstruction

5 Nov 2024, 09:00

LPNHE, Paris, France

Conveners

Reconstruction

• Fabrice Balli (Université Paris-Saclay (FR))

Reconstruction

• Brendon Bullard (SLAC National Accelerator Laboratory (US))

Reconstruction

• Katherine Fraser (Harvard University)

111. Deep learning on jet modification in the presence of the QGP background

RAN LI

05/11/2024, 09:00

Jet interactions with the color-deconfined QCD medium in relativistic heavy-ion collisions are conventionally assessed by measuring the modification of the distributions of jet observables with respect to their proton-proton baselines. Deep learning methods allow us to evaluate the modification of jets on a jet-by-jet basis, and therefore significantly improve the capability of using jets to...

112. Transformer networks for constituent-based b-jet calibration with the ATLAS detector

Brendon Bullard (SLAC National Accelerator Laboratory (US))

05/11/2024, 09:20

The precise measurement of kinematic features of jets is key to the physics program of the LHC. The determination of the energy and mass of jets containing bottom quarks 𝑏-jets is particularly difficult given their distinct radiation patterns and production of undetectable neutrinos via leptonic heavy flavor decays. This talk will describe a novel calibration technique for the b-jet kinematics...

113. Jet Finding as a Real-Time Object Detection Task

Leon Bozianu (Universite de Geneve (CH))

05/11/2024, 09:40

The High Luminosity upgrade to the LHC will deliver an unprecedented luminosity to the ATLAS experiment. Ahead of this increase in data the ATLAS trigger and data acquisition system will undergo a comprehensive upgrade. The key function of the trigger system is to maintain a high signal efficiency together with a high background rejection whilst adhering to the throughput constraints of the...

50. Transformer for Energy Calibration in the ATLAS Electromagnetic Calorimeter

Ryan Roberts (University of California Berkeley (US))

05/11/2024, 10:00

The ATLAS experiment reconstructs electrons and photons from clusters of energy deposits in the electromagnetic calorimeter. The reconstructed electron and photon energy must be corrected from the measured energy deposits in the clusters to account for energy loss in passive material upstream of the calorimeter, in the passive material in the calorimeter, out of cluster energies and leakage in...

118. Synergizing Physics: Deep Learning Techniques for Time-of-Flight Reconstruction and Jet Tagging in High Energy Physics

Konrad Helms (Georg-August-Universität Göttingen)

05/11/2024, 10:50

This talk presents a synergy between quark/gluon jet tagging on LHC data, and charged hadron time-of-flight (TOF) regression on ILC data, in the form of one problem-solving mechanism that can address both tasks. They both involve processing data represented as unordered point clouds of varying sequence lengths, optimally handled using permutation-invariant architectures.

A...

115. Particle flow and flavor tagging with DNN for Higgs factories

Taikan Suehara (ICEPP, The University of Tokyo (JP))

05/11/2024, 11:10

Deep learning can give a significant impact on physics performance of electron-positron Higgs factories such as ILC and FCCee. We are working on two topics on event reconstruction to apply deep learning; one is jet flavor tagging. We apply particle transformer to ILD full simulation to obtain jet flavor, including strange tagging. The other one is particle flow, which clusters calorimeter hits...

62. Efficient Particle Tracking and Pileup Mitigation with State space model

Cheng Jiang (The University of Edinburgh (GB))

06/11/2024, 13:50

Large-scale point cloud and long-sequence processing are crucial for high energy physics applications such as pileup mitigation and track reconstruction. The HL-LHC presents inevitable challenges to machine learning models, requiring both high stability and low computational complexity. Previous studies have primarily focused on graph-based approaches which are generally effective but often...

94. Graph Neural Network-Based Track Finding in the LHCb Vertex Detector

Fotis Giasemis (Centre National de la Recherche Scientifique (FR))

06/11/2024, 14:10

The next decade will see an order of magnitude increase in data collected by high-energy physics experiments,
driven by the High-Luminosity LHC (HL-LHC). The reconstruction of charged particle trajectories (tracks) has
always been a critical part of offline data processing pipelines. The complexity of HL-LHC data will however
increasingly mandate track finding in all stages of an...

80. Accelerating Graph-based Tracking Tasks with Symbolic Regression

Nathalie Soybelman (Weizmann Institute of Science (IL))

06/11/2024, 14:30

Reconstructing particle tracks from detector hits is computationally intensive due to the large combinatorics involved. Recent work has shown that ML techniques can enhance conventional tracking methods, but complex models are often difficult to implement on heterogeneous trigger systems, such as FPGAs. While deploying neural networks on FPGAs is possible, resource limitations pose challenges....

72. Denoising Graph Super-Resolution for Improved Collider Event Reconstruction

Nilotpal Kakati (Weizmann Institute of Science (IL))

06/11/2024, 14:50

Accurately reconstructing particles from detector data is a critical challenge in experimental particle physics, where the spatial resolution of calorimeters plays a key role. This study explores the integration of super-resolution techniques into the Large Hadron Collider (LHC)-like reconstruction pipeline to enhance the granularity of calorimeter data. By applying super-resolution, we...

114. (R) Generative Neural Networks for Reconstructing Parton-Level Jet Showers after Hadronization

Umar Sohail Qureshi (Vanderbilt University)

06/11/2024, 15:10

Recreating realistic parton-level event configurations from jets is a crucial task for various physics analyses. However, hadronization processes cannot be computed using perturbative QCD. Therefore, it has been traditionally intractable to reconstruct parton-level events after hadronization.

We present a generative machine learning approach for reconstructing jet showers at the parton...