77 citations · 218 across the 23 of their papers we have counts for
25 papers
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…
Expected Sensitivity of the Light Dark Matter eXperiment to Long-Lived Dark Photons and Axion-Like Particles
Torsten Akesson, Clay Barton, Charles Bell +53
The Light Dark Matter eXperiment (LDMX) is an electron-beam fixed-target experiment primarily designed to achieve world-leading, model-independent sensitivity to sub-GeV dark matte…
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…
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…
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…
Edge Machine Learning for Cluster Counting in Next-Generation Drift Chambers
Deniz Yilmaz, Liangyu Wu, Julia Gonski +2
Drift chambers have long been central to collider tracking, but future machines like a Higgs factory motivate higher granularity and cluster counting for particle ID, posing new da…
LDMX -- The Light Dark Matter eXperiment
Torsten Akesson, Layan Alsaraya, Stephen Appert +116
The Light Dark Matter eXperiment (LDMX) is an electron fixed-target experiment optimized to search for sub-GeV dark matter production through the missing momentum signature. LDMX i…
Sensitivity of an Early Dark Matter Search using the Electromagnetic Calorimeter as a Target for the Light Dark Matter eXperiment
LDMX Collaboration, Torsten Åkesson, Elizabeth Berzin +53
The Light Dark Matter eXperiment (LDMX) is proposed to employ a thin tungsten target and a multi-GeV electron beam to carry out a missing momentum search for the production of dark…
ACE Science Workshop Report
Stefania Gori, Nhan Tran, Karri DiPetrillo +20
We summarize the Fermilab Accelerator Complex Evolution (ACE) Science Workshop, held on June 14-15, 2023. The workshop presented the strategy for the ACE program in two phases: ACE…
Photon-rejection Power of the Light Dark Matter eXperiment in an 8 GeV Beam
Torsten Åkesson, Cameron Bravo, Liam Brennan +44
The Light Dark Matter eXperiment (LDMX) is an electron-beam fixed-target experiment designed to achieve comprehensive model independent sensitivity to dark matter particles in the…
Differentiable Earth Mover's Distance for Data Compression at the High-Luminosity LHC
Rohan Shenoy, Javier Duarte, Christian Herwig +5
The Earth mover's distance (EMD) is a useful metric for image recognition and classification, but its usual implementations are not differentiable or too slow to be used as a loss…
New Searches for Muonphilic Particles at Proton Beam Dump Spectrometers
Diana Forbes, Christian Herwig, Yonatan Kahn +4
We introduce a new search strategy for visibly decaying muonphilic particles using a proton beam spectrometer modeled after the SpinQuest experiment at Fermilab. In this setup, a $…
Snowmass Early Career
Garvita Agarwal, Joshua L. Barrow, Mateus F. Carneiro +16
The Snowmass 2021 strategic planning process provided an essential opportunity for the United States high energy physics and astroparticle (HEPA) community to come together and dis…
FastML Science Benchmarks: Accelerating Real-Time Scientific Edge Machine Learning
Javier Duarte, Nhan Tran, Ben Hawks +4
Applications of machine learning (ML) are growing by the day for many unique and challenging scientific applications. However, a crucial challenge facing these applications is thei…
Smart sensors using artificial intelligence for on-detector electronics and ASICs
Gabriella Carini, Grzegorz Deptuch, Jennet Dickinson +19
Cutting edge detectors push sensing technology by further improving spatial and temporal resolution, increasing detector area and volume, and generally reducing backgrounds and noi…
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…
Current Status and Future Prospects for the Light Dark Matter eXperiment
Torsten Åkesson, Nikita Blinov, Lukas Brand-Baugher +45
The constituents of dark matter are still unknown, and the viable possibilities span a vast range of masses. The physics community has established searching for sub-GeV dark matter…
Applications and Techniques for Fast Machine Learning in Science
Allison McCarn Deiana, Nhan Tran, Joshua Agar +84
In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into the real-time…
Developing Robust Digital Twins and Reinforcement Learning for Accelerator Control Systems at the Fermilab Booster
D. Kafkes, M. Schram
We describe the offline machine learning (ML) development for an effort to precisely regulate the Gradient Magnet Power Supply (GMPS) at the Fermilab Booster accelerator complex vi…
A reconfigurable neural network ASIC for detector front-end data compression at the HL-LHC
Giuseppe Di Guglielmo, Farah Fahim, Christian Herwig +15
Despite advances in the programmable logic capabilities of modern trigger systems, a significant bottleneck remains in the amount of data to be transported from the detector to off…
hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices
Farah Fahim, Benjamin Hawks, Christian Herwig +27
Accessible machine learning algorithms, software, and diagnostic tools for energy-efficient devices and systems are extremely valuable across a broad range of application domains.…
Real-time Artificial Intelligence for Accelerator Control: A Study at the Fermilab Booster
Jason St. John, Christian Herwig, Diana Kafkes +11
We describe a method for precisely regulating the gradient magnet power supply at the Fermilab Booster accelerator complex using a neural network trained via reinforcement learning…
-parity Violating Decays of Bino Neutralino LSPs at the LHC
Sebastian Dumitru, Christian Herwig, Burt A. Ovrut
The -parity violating decays of Bino neutralino LSPs are analyzed within the context of the MSSM "heterotic standard model". These LSPs correspond to statistically determi…