28 citations · 51 across the 8 of their papers we have counts for
8 papers
Chopping and distilling variational autoencoders for real-time anomaly detection in high energy physics
Max Cohen, Rajat Gupta, Sterre Hoogendoorn +3
Anomaly detection (AD) has recently emerged as an exciting alternative to conventional search strategies in high energy physics using artificial intelligence (AI) and machine learn…
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…
Memristive tabular variational autoencoder for compression of analog data in high energy physics
Rajat Gupta, Yuvaraj Elangovan, Tae Min Hong +5
We present an implementation of edge AI to compress data on an in-memory analog content-addressable memory (ACAM) device. A variational autoencoder is trained on a simulated sample…
Machine Learning on Heterogeneous, Edge, and Quantum Hardware for Particle Physics (ML-HEQUPP)
Julia Gonski, Jennifer Ott, Jenni Ott +129
The next generation of particle physics experiments will face a new era of challenges in data acquisition, due to unprecedented data rates and volumes along with extreme environmen…
Ring-based ML calibration with in situ pileup correction for real-time jet triggers
Benjamin T. Carlson, Stephen T. Roche, Michael Hemmett +1
We present a machine learning (ML) method to calibrate hadronic jet energy in real-time trigger systems of the High-Luminosity Large Hadron Collider (HL-LHC) using an efficient imp…
Nanosecond hardware regression trees in FPGA at the LHC
Pavel Serhiayenka, Stephen Roche, Benjamin Carlson +1
We present a generic parallel implementation of the decision tree-based machine learning (ML) method in hardware description language (HDL) on field programmable gate arrays (FPGA)…
Nanosecond anomaly detection with decision trees and real-time application to exotic Higgs decays
Stephen Roche, Quincy Bayer, Benjamin Carlson +4
We present an interpretable implementation of the autoencoding algorithm, used as an anomaly detector, built with a forest of deep decision trees on FPGA, field programmable gate a…
Nanosecond machine learning event classification with boosted decision trees in FPGA for high energy physics
Tae Min Hong, Benjamin Carlson, Brandon Eubanks +4
We present a novel implementation of classification using the machine learning / artificial intelligence method called boosted decision trees (BDT) on field programmable gate array…