Unsupervised anomaly detection has become a pivotal technique for model-independent searches for new physics at the LHC. In high-energy physics (HEP), anomaly detection is employed to identify rare, outlier events in collision data that deviate significantly from expected distributions. A promising approach is the application of generative machine learning models, which can efficiently detect...
Advancements in geometric deep learning offer powerful tools to study the internal structure of jets initiated by heavy quarks, particularly in the context of dead-cone effect and jet quenching. The kinematics of b-hadron decays present a challenge for substructure measurements with inclusive b-jets, which are essential for quantum chromodynamics (QCD) studies. We propose an approach using...
In this contribution, we present the machine learning-based strategy to improve the reconstruction of neutral meson events within the Large Hadron Collider forward (LHCf) experiment. The LHCf experiment is uniquely positioned in the very forward region of the LHC to investigate the hadronic interactions relevant to high-energy cosmic ray air shower simulations by measuring forward-produced...
In view of the high luminosity campaign of the LHC (HL-LHC), the computational requirements of the ATLAS experiment are expected to increase remarkably in the coming years. In particular, simulation of Monte Carlo events is immensely demanding from the computational point of view and their limited availability is one of the major sources of uncertainty in many analyses. The main bottleneck in...
The Overlap Muon Track Finder (OMTF) is a key subsystem of the CMS L1 Trigger, identifying muon tracks in the transition region between the barrel and the endcap. For the Phase-2 upgrade, we are exploring new approaches and leveraging machine learning (ML) to enhance its performance. In this project, we focus on integrating a Graph Neural Network (GNN) to improve the OMTF's ability to...
High-Energy Physics experiments are rapidly escalating in generated data volume, a trend that will intensify with the upcoming High-Luminosity LHC upgrade. This surge in data necessitates critical revisions across the data processing pipeline, with particle track reconstruction being a prime candidate for improvement. In our previous work, we introduced “TrackFormers”, a collection of...
