3 citations · 4 across the 9 of their papers we have counts for
9 papers
AI Agents Can Already Autonomously Perform Experimental High Energy Physics
Eric A. Moreno, Samuel Bright-Thonney, Andrzej Novak +2
Large language model-based AI agents are now able to autonomously execute substantial portions of a high energy physics (HEP) analysis pipeline with minimal expert-curated input. G…
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…
AutoSciDACT: Automated Scientific Discovery through Contrastive Embedding and Hypothesis Testing
Samuel Bright-Thonney, Christina Reissel, Gaia Grosso +6
Novelty detection in large scientific datasets faces two key challenges: the noisy and high-dimensional nature of experimental data, and the necessity of making statistically robus…
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…
A Neural Network-Based Search for Unmodeled Transients in LIGO-Virgo-KAGRA's Third Observing Run
Ryan Raikman, Eric A. Moreno, Katya Govorkova +13
This paper presents the results of a Neural Network (NN)-based search for short-duration gravitational-wave transients in data from the third observing run of LIGO, Virgo, and KAGR…
Low Latency Transformer Inference on FPGAs for Physics Applications with hls4ml
Zhixing Jiang, Dennis Yin, Yihui Chen +7
This study presents an efficient implementation of transformer architectures in Field-Programmable Gate Arrays(FPGAs) using hls4ml. We demonstrate the strategy for implementing the…
Rapid Likelihood Free Inference of Compact Binary Coalescences using Accelerated Hardware
Deep Chatterjee, Ethan Marx, William Benoit +12
We report a gravitational-wave parameter estimation algorithm, AMPLFI, based on likelihood-free inference using normalizing flows. The focus of AMPLFI is to perform real-time param…
A machine-learning pipeline for real-time detection of gravitational waves from compact binary coalescences
Ethan Marx, William Benoit, Alec Gunny +12
The promise of multi-messenger astronomy relies on the rapid detection of gravitational waves at very low latencies ((1\,s)) in order to maximize the amount of time av…
Ultra Fast Transformers on FPGAs for Particle Physics Experiments
Zhixing Jiang, Dennis Yin, Elham E Khoda +6
This work introduces a highly efficient implementation of the transformer architecture on a Field-Programmable Gate Array (FPGA) by using the \texttt{hls4ml} tool. Given the demons…