9 citations · 16 across the 9 of their papers we have counts for
9 papers
Machine-learned particle flow as a foundation model for collider physics
Farouk Mokhtar, Joosep Pata, Michael Kagan +1
The workflow from particle collision to physics analysis passes through a series of reconstruction steps that are traditionally modular and disconnected, with no shared representat…
Building Machine Learning Challenges for Anomaly Detection in Science
Elizabeth G. Campolongo, Yuan-Tang Chou, Ekaterina Govorkova +148
Scientific discoveries are often made by finding a pattern or object that was not predicted by the known rules of science. Oftentimes, these anomalous events or objects that do not…
Fine-tuning machine-learned particle-flow reconstruction for new detector geometries in future colliders
Farouk Mokhtar, Joosep Pata, Dolores Garcia +4
We demonstrate transfer learning capabilities in a machine-learned algorithm trained for particle-flow reconstruction in high energy particle colliders. This paper presents a cross…
Learning Symmetry-Independent Jet Representations via Jet-Based Joint Embedding Predictive Architecture
Subash Katel, Haoyang Li, Zihan Zhao +3
In high energy physics, self-supervised learning (SSL) methods have the potential to aid in the creation of machine learning models without the need for labeled datasets for a vari…
Reconstruction of boosted and resolved multi-Higgs-boson events with symmetry-preserving attention networks
Haoyang Li, Marko Stamenkovic, Alexander Shmakov +12
The production of multiple Higgs bosons at the CERN LHC provides a direct way to measure the trilinear and quartic Higgs self-interaction strengths as well as potential access to b…
Novel machine learning applications at the LHC
Javier M. Duarte
Machine learning (ML) is a rapidly growing area of research in the field of particle physics, with a vast array of applications at the CERN LHC. ML has changed the way particle phy…
Large-Scale Pretraining and Finetuning for Efficient Jet Classification in Particle Physics
Zihan Zhao, Farouk Mokhtar, Raghav Kansal +2
This study introduces an innovative approach to analyzing unlabeled data in high-energy physics (HEP) through the application of self-supervised learning (SSL). Faced with the incr…
Accelerating Resonance Searches via Signature-Oriented Pre-training
Congqiao Li, Antonios Agapitos, Jovin Drews +9
The search for heavy resonances beyond the Standard Model (BSM) is a key objective at the LHC. While the recent use of advanced deep neural networks for boosted-jet tagging signifi…
Improved particle-flow event reconstruction with scalable neural networks for current and future particle detectors
Joosep Pata, Eric Wulff, Farouk Mokhtar +4
Efficient and accurate algorithms are necessary to reconstruct particles in the highly granular detectors anticipated at the High-Luminosity Large Hadron Collider and the Future Ci…