Conveners
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- David Shih
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- Pierre Antoine Delsart (CNRS / LPSC Grenoble (FR))
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- Mihoko Nojiri (Theory Center, IPNS, KEK)
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- Eilam Gross (Weizmann Institute of Science (IL))
Identifying the origin of high-energy hadronic jets (`jet tagging') has been a critical benchmark problem for machine learning in particle physics. Jets are ubiquitous at colliders and are complex objects that serve as prototypical examples of collections of particles to be categorized. Over the last decade, machine learning-based classifiers have replaced classical observables as the state...
Attention-based transformer models have become increasingly prevalent in collider analysis, offering enhanced performance for tasks such as jet tagging. However, they are computationally intensive
and require substantial data for training. In this paper, we introduce a new jet classification network
using an MLP mixer, where two subsequent MLP operations serve to transform particle and...
This study introduces an approach to learning augmentation-independent jet representations using a Jet-based Joint Embedding Predictive Architecture (J-JEPA). This approach aims to predict various physical targets from an informative context, using target positions as joint information. We study several methods for defining the targets and context, including grouping subjets within a jet, and...
Extracting scientific understanding from particle-physics experiments requires solving diverse learning problems with high precision and good data efficiency. We present the Lorentz Geometric Algebra Transformer (L-GATr), a new multi-purpose architecture for high-energy physics. L-GATr represents high-energy data in a geometric algebra over four-dimensional space-time and is equivariant under...
Efficient jet flavour-tagging is crucial for event reconstruction and particle analyses in high energy physics (HEP). Graph Neural Networks (GNNs) excel in capturing complex relationships within graph-structured data, and we aim to enhance the classification of b-jets using this method of deep learning. Presented in this work is the first application of a novel GNN b-jet tagger using the LHCb...
The steady progress in machine learning leads to substantial performance improvements in various areas of high-energy physics, especially for object identification. Jet flavor identification (tagging) is a prominent benchmark that profits from elaborate architectures, leveraging information from low-level input variables and their correlations. Throughout the data-taking eras of the Large...
Precise tau identification is a crucial component for many studies targeting the Standard Model or searches for New Physics within the CMS physics program. The Deep Tau v2.5 algorithm is a convolutional neural network algorithm: an improved version of its predecessor, Deep Tau v2.1, deployed for the LHC Run 3. This updated version integrates several enhancements to improve classification...
The phenomena of Jet Quenching, a key signature of the Quark-Gluon Plasma (QGP) formed in Heavy-Ion (HI) collisions, provides a window of insight into the properties of this primordial liquid. In this study, we rigorously evaluate the discriminating power of Energy Flow Networks (EFNs), enhanced with substructure observables, in distinguishing between jets stemming from proton-proton (pp) and...
We improve upon the existing literature on pileup mitigation techniques studied at Large Hadron Collider (LHC) experiments for disentangling proton-proton collisions. Pileup presents a salient problem that, if not checked, hinders the search for new physics and Standard Model precision measurements such as jet energy, jet substructure, missing momentum, and lepton isolation. The primary...
Supervised deep learning methods have found great success in the field of high energy physics (HEP) and the trend within the field is to move away from high level reconstructed variables to low level detector features. However, supervised methods require labelled data, which is typically provided by a simulator. The simulations of HEP datasets become harder to validate and calibrate as we...
Observables sensitive to top quark polarization are important for characterizing and discovering new physics. The most powerful spin analyzer in the top decay is the down-type fermion from the W, which in the case of leptonic decay allows for very clean measurements. However, in many applications, it is useful to measure the polarization of hadronically decaying top quarks via an optimal...
Analysis of collision data often involves training deep learning classifiers on very specific tasks and in regions of phase-space where the training datasets have limited statistics. Models pre-trained on a larger, more generic, sample may already have a useful representation of collider data which can be leveraged by many independent downstream analysis tasks. We introduce a class of...
Machine learning is becoming increasingly popular in the context of particle physics. Supervised learning, which uses labeled Monte Carlo simulations, remains one of the most widely used methods for discriminating signals beyond the Standard Model. However, this paper suggests that supervised models may depend excessively on artifacts and approximations from Monte Carlo simulations,...
