17 citations · 60 across the 31 of their papers we have counts for
35 papers
Reconfigurable On-Chip AI for Particle Physics Detectors with Logic Neural Networks
Mila Bileska, Lino Gerlach, Jennet Dickinson +4
On-device machine learning is increasingly important in applications where extreme data rates or resource constraints make centralized processing infeasible. Logic neural networks…
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…
WARP Logic Neural Networks
Lino Gerlach, Thore Gerlach, Liv Våge +2
Fast and efficient AI inference is increasingly important, and recent models that directly learn low-level logic operations have achieved state-of-the-art performance. However, exi…
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…
Reconstruction of atmospheric neutrinos in DUNE's horizontal-drift far-detector module
DUNE Collaboration, S. Abbaslu, F. Abd Alrahman +1343
This paper reports on the capabilities in reconstructing and identifying atmospheric neutrino interactions in one of the Deep Underground Neutrino Experiment's (DUNE) far detector…
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…
WARP-LUTs -- Walsh-Assisted Relaxation for Probabilistic Look Up Tables
Lino Gerlach, Liv Våge, Thore Gerlach +2
Fast and efficient machine learning is of growing interest to the scientific community and has spurred significant research into novel model architectures and hardware-aware design…
Evaluation of Novel Fast Machine Learning Algorithms for Knowledge-Distillation-Based Anomaly Detection at CMS
Lino Gerlach, Elliott Kauffman, Abhishikth Mallampalli
The CICADA (Calorimeter Image Convolutional Anomaly Detection Algorithm) project aims to detect anomalous physics signatures without bias from theoretical models in proton-proton c…
Measurement of Exclusive --argon Interactions Using ProtoDUNE-SP
DUNE Collaboration, S. Abbaslu, A. Abed Abud +1323
We present the measurement of --argon inelastic cross sections using the ProtoDUNE Single-Phase liquid argon time projection chamber in the incident kinetic energy ran…
First Measurement of -Ar and -Ar Total Inelastic Cross Sections in the Sub-GeV Energy Regime with ProtoDUNE-SP Data
DUNE Collaboration, S. Abbaslu, F. Abd Alrahman +1346
The ProtoDUNE-SP detector, a kiloton-scale prototype for the Deep Underground Neutrino Experiment (DUNE) far detector, is the largest liquid argon time projection chamber built to…
Rapid Inference of Logic Gate Neural Networks for Anomaly Detection in High Energy Physics
Lino Gerlach, Elliott Kauffman, Liv Helen Våge +1
The increasing data rates and complexity of detectors at the Large Hadron Collider (LHC) necessitate fast and efficient machine learning models, particularly for rapid selection of…
Identification of low-energy kaons in the ProtoDUNE-SP detector
DUNE Collaboration, S. Abbaslu, F. Abd Alrahman +1343
The Deep Underground Neutrino Experiment (DUNE) is a next-generation neutrino experiment with a rich physics program that includes searches for the hypothetical phenomenon of proto…
Towards mono-energetic virtual beam cross-section measurements: A feasibility study of -Ar interaction analysis with DUNE-PRISM
DUNE Collaboration, S. Abbaslu, A. Abed Abud +1321
Neutrino-nucleus cross-section measurements are critical for future neutrino oscillation analyses. However, our models to describe them require further refinement, and a deeper und…
Real-time Anomaly Detection for Liquid Argon Time Projection Chambers
Seokju Chung, Jack Cleeve, Akshay Malige +4
We present a real-time anomaly detection framework for liquid argon time projection chambers (LArTPCs), targeting applications in particle physics experiments such as the Short Bas…
Operation of a Modular 3D-Pixelated Liquid Argon Time-Projection Chamber in a Neutrino Beam
DUNE Collaboration, S. Abbaslu, A. Abed Abud +1318
The 2x2 Demonstrator, a prototype for the Deep Underground Neutrino Experiment (DUNE) liquid argon (LAr) Near Detector, was exposed to the Neutrinos from the Main Injector (NuMI) n…
Spatial and Temporal Evaluations of the Liquid Argon Purity in ProtoDUNE-SP
DUNE Collaboration, S. Abbaslu, A. Abed Abud +1320
Liquid argon time projection chambers (LArTPCs) rely on highly pure argon to ensure that ionization electrons produced by charged particles reach readout arrays. ProtoDUNE Single-P…
Differentiating a HEP Analysis Pipeline within the Scikit-HEP Software Ecosystem
Mohamed Aly, Lino Gerlach
A first differentiable analysis pipeline is presented for an example high-energy physics (HEP) use case with publicly available collision data from the Compact Muon Solenoid detect…
European Contributions to Fermilab Accelerator Upgrades and Facilities for the DUNE Experiment
DUNE Collaboration, A. Abed Abud, R. Acciarri +1340
The Proton Improvement Plan (PIP-II) to the FNAL accelerator chain and the Long-Baseline Neutrino Facility (LBNF) will provide the world's most intense neutrino beam to the Deep Un…
DUNE Software and Computing Research and Development
DUNE Collaboration, A. Abed Abud, R. Acciarri +1340
The international collaboration designing and constructing the Deep Underground Neutrino Experiment (DUNE) at the Long-Baseline Neutrino Facility (LBNF) has developed a two-phase s…
The DUNE Phase II Detectors
DUNE Collaboration, A. Abed Abud, R. Acciarri +1340
The international collaboration designing and constructing the Deep Underground Neutrino Experiment (DUNE) at the Long-Baseline Neutrino Facility (LBNF) has developed a two-phase s…
The DUNE Science Program
DUNE Collaboration, A. Abed Abud, R. Acciarri +1340
The international collaboration designing and constructing the Deep Underground Neutrino Experiment (DUNE) at the Long-Baseline Neutrino Facility (LBNF) has developed a two-phase s…
Neutrino Interaction Vertex Reconstruction in DUNE with Pandora Deep Learning
DUNE Collaboration, A. Abed Abud, R. Acciarri +1331
The Pandora Software Development Kit and algorithm libraries perform reconstruction of neutrino interactions in liquid argon time projection chamber detectors. Pandora is the prima…
PyHEP.dev 2024 Workshop Summary Report, August 26-30 2024, Aachen, Germany
Azzah Alshehri, Jan Bürger, Saransh Chopra +28
The second PyHEP.dev workshop, part of the "Python in HEP Developers" series organized by the HEP Software Foundation (HSF), took place in Aachen, Germany, from August 26 to 30, 20…
The track-length extension fitting algorithm for energy measurement of interacting particles in liquid argon TPCs and its performance with ProtoDUNE-SP data
DUNE Collaboration, A. Abed Abud, B. Abi +1366
This paper introduces a novel track-length extension fitting algorithm for measuring the kinetic energies of inelastically interacting particles in liquid argon time projection cha…