10 citations · 25 across the 23 of their papers we have counts for
26 papers
RINO: Renormalization Group Distillation with No Labels
Zichun Hao, Raghav Kansal, Abhijith Gandrakota +6
Supervised machine learning in high-energy physics (HEP) requires training on labeled simulations of particle collisions, which are known to be unfaithful to the underlying physics…
Conditional Capacity and Routing in Mixture-of-Experts Particle Transformers
Kaushik Pendiyala, Haris Zia, Trevin Lee +8
Mixture-of-Experts (MoE) models can increase parameter capacity without proportionally increasing active computation, but it is unclear how this trade-off behaves in particle-physi…
On-Detector Machine Learning for Beam-Induced Background Rejection at a 10 TeV Muon Collider
Daniel Abadjiev, Eliza Howard, Tsz Ngong You +35
A 10 TeV Muon Collider is a compelling candidate for a future energy-frontier facility, offering unprecedented opportunities to explore the fundamental laws of particle physics. Mu…
Learning to Trigger: Reinforcement Learning at the Large Hadron Collider
Zixin Ding, Shaghayegh Emami, Giovanna Salvi +7
High-throughput scientific facilities such as the Large Hadron Collider depend on real-time event filtering (\textit{triggering}) under tight constraints on bandwidth, latency, and…
Patch Hierarchical Attention Transformer for Efficient Particle Jet Tagging
Aaron Wang, Zihan Zhao, Alan Xia +5
Real-time jet tagging is critical for identifying short-lived particle decays in the high-throughput detectors of the Large Hadron Collider, where real-time trigger systems respons…
Building an AI-native Research Ecosystem for Experimental Particle Physics: A Community Vision
Thea Klaeboe Aarrestad, Alaa Abdelhamid, Haider Abidi +457
Experimental particle physics seeks to understand the universe by probing its fundamental particles and forces and exploring how they govern the large-scale processes that shape co…
Poisson Log-Normal Process for Count Data Prediction
Anushka Saha, Abhijith Gandrakota, Alexandre V. Morozov
Modeling count data is important in physics and other scientific disciplines, where measurements often involve discrete, non-negative quantities such as photon or neutrino detectio…
On-chip probabilistic inference for charged-particle tracking at the sensor edge
Arghya Ranjan Das, David Jiang, Rachel Kovach-Fuentes +31
Modern scientific instruments operate under increasingly extreme constraints on bandwidth, latency, and power. Inference at the sensor edge determines experimental data collection…
Towards a Self-Driving Trigger at the LHC: Adaptive Response in Real Time
Shaghayegh Emami, Cecilia Tosciri, Giovanna Salvi +7
Real-time data filtering and selection -- or trigger -- systems at high-throughput scientific facilities such as the experiments at the Large Hadron Collider (LHC) must process ext…
hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware
Jan-Frederik Schulte, Benjamin Ramhorst, Chang Sun +50
We present hls4ml, a free and open-source platform that translates machine learning (ML) models from modern deep learning frameworks into high-level synthesis (HLS) code that can b…
Why Is Attention Sparse In Particle Transformer?
Timothy Legge, Aaron Wang, Jacob Ortiz +7
Transformer-based models have achieved state-of-the-art performance in jet tagging at the CERN Large Hadron Collider (LHC), with the Particle Transformer (ParT) representing a lead…
An Evaluation of Representation Learning Methods in Particle Physics Foundation Models
Michael Chen, Raghav Kansal, Abhijith Gandrakota +3
We present a systematic evaluation of representation learning objectives for particle physics within a unified framework. Our study employs a shared transformer-based particle-clou…
Sub-microsecond Transformers for Jet Tagging on FPGAs
Lauri Laatu, Chang Sun, Arianna Cox +7
We present the first sub-microsecond transformer implementation on an FPGA achieving competitive performance for state-of-the-art high-energy physics benchmarks. Transformers have…
Robustness of the \textit{smartpixels} classifier for different simulated sensor geometries and non-ideal detector conditions
Danush Shekar, Ben Weiss, Morris Swartz +40
Pixel tracking detectors at upcoming collider experiments will see unprecedented charged-particle densities. Real-time data reduction on the detector will enable higher granularity…
Spatially Aware Linear Transformer (SAL-T) for Particle Jet Tagging
Aaron Wang, Zihan Zhao, Subash Katel +6
Transformers are very effective in capturing both global and local correlations within high-energy particle collisions, but they present deployment challenges in high-data-throughp…
RINO: Renormalization Group Invariance with No Labels
Zichun Hao, Raghav Kansal, Abhijith Gandrakota +4
A common challenge with supervised machine learning (ML) in high energy physics (HEP) is the reliance on simulations for labeled data, which can often mismodel the underlying colli…
United States Muon Collider Community White Paper for the European Strategy for Particle Physics Update
A. Abdelhamid, D. Acosta, P. Affleck +301
This document is being submitted to the 2024-2026 European Strategy for Particle Physics Update (ESPPU) process on behalf of the US Muon Collider community, with its preparation co…
Interpreting Transformers for Jet Tagging
Aaron Wang, Abhijith Gandrakota, Jennifer Ngadiuba +4
Machine learning (ML) algorithms, particularly attention-based transformer models, have become indispensable for analyzing the vast data generated by particle physics experiments l…
Real-time Anomaly Detection at the L1 Trigger of CMS Experiment
Abhijith Gandrakota
We present the preparation, deployment, and testing of an autoencoder trained for unbiased detection of new physics signatures in the CMS experiment Global Trigger (GT) test crate…
Population-level Dark Energy Constraints from Strong Gravitational Lensing using Simulation-Based Inference
Sreevani Jarugula, Brian Nord, Abhijith Gandrakota +1
In this work, we present a scalable approach for inferring the dark energy equation-of-state parameter () from a population of strong gravitational lens images using Simulation-…
Robust Anomaly Detection for Particle Physics Using Multi-Background Representation Learning
Abhijith Gandrakota, Lily Zhang, Aahlad Puli +4
Anomaly, or out-of-distribution, detection is a promising tool for aiding discoveries of new particles or processes in particle physics. In this work, we identify and address two o…
Fast Particle-based Anomaly Detection Algorithm with Variational Autoencoder
Ryan Liu, Abhijith Gandrakota, Jennifer Ngadiuba +2
Model-agnostic anomaly detection is one of the promising approaches in the search for new beyond the standard model physics. In this paper, we present Set-VAE, a particle-based var…
Efficient and Robust Jet Tagging at the LHC with Knowledge Distillation
Ryan Liu, Abhijith Gandrakota, Jennifer Ngadiuba +2
The challenging environment of real-time data processing systems at the Large Hadron Collider (LHC) strictly limits the computational complexity of algorithms that can be deployed.…
Physics Community Needs, Tools, and Resources for Machine Learning
Philip Harris, Erik Katsavounidis, William Patrick McCormack +18
Machine learning (ML) is becoming an increasingly important component of cutting-edge physics research, but its computational requirements present significant challenges. In this w…