28 citations · 63 across the 8 of their papers we have counts for
8 papers
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…
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)…
Illuminating all-hadronic final states with a photon: Exotic decays of the Higgs boson to four bottom quarks in vector boson fusion plus gamma at hadron colliders
Stephen T. Roche, Benjamin T. Carlson, Christopher R. Hayes +1
We investigate the potential to detect Higgs boson decays to four bottom quarks through a pair of pseudoscalars, a final state that is predicted by many theories beyond the Standar…
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 regression with deep boosted decision trees in FPGA for high energy physics
Benjamin Carlson, Quincy Bayer, Tae Min Hong +1
We present a novel application of the machine learning / artificial intelligence method called boosted decision trees to estimate physical quantities on field programmable gate arr…
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…