20 citations · 50 across the 18 of their papers we have counts for
18 papers
FAIR Universe Weak Lensing ML Uncertainty Challenge: Handling Uncertainties and Distribution Shifts for Precision Cosmology
Biwei Dai, Po-Wen Chang, Wahid Bhimji +15
Weak gravitational lensing, the correlated distortion of background galaxy shapes by foreground structures, is a powerful probe of the matter distribution in our universe and allow…
Fair Universe Higgs Uncertainty Challenge
Ragansu Chakkappai, Wahid Bhimji, Paolo Calafiura +16
This competition in high-energy physics (HEP) and machine learning was the first to strongly emphasise uncertainties in cross-section measurement. Partici…
Serving LLMs in HPC Clusters: A Comparative Study of Qualcomm Cloud AI 100 Ultra and NVIDIA Data Center GPUs
Mohammad Firas Sada, John J. Graham, Elham E Khoda +7
This study presents a benchmarking analysis of the Qualcomm Cloud AI 100 Ultra (QAic) accelerator for large language model (LLM) inference, evaluating its energy efficiency (throug…
The National Research Platform: Stretched, Multi-Tenant, Scientific Kubernetes Cluster
Derek Weitzel, Ashton Graves, Sam Albin +9
The National Research Platform (NRP) represents a distributed, multi-tenant Kubernetes-based cyberinfrastructure designed to facilitate collaborative scientific computing. Spanning…
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…
FAIR Universe HiggsML Uncertainty Dataset and Competition
Lisa Benato, Wahid Bhimji, Paolo Calafiura +26
The FAIR Universe HiggsML Uncertainty Challenge focused on measuring the physical properties of elementary particles with imperfect simulators. Participants were required to comput…
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…
Graph Neural Network-based Tracking as a Service
Haoran Zhao, Andrew Naylor, Shih-Chieh Hsu +8
Recent studies have shown promising results for track finding in dense environments using Graph Neural Network (GNN)-based algorithms. However, GNN-based track finding is computati…
FPGA Deployment of LFADS for Real-time Neuroscience Experiments
Xiaohan Liu, ChiJui Chen, YanLun Huang +6
Large-scale recordings of neural activity are providing new opportunities to study neural population dynamics. A powerful method for analyzing such high-dimensional measurements is…
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…
Applications of Deep Learning to physics workflows
Manan Agarwal, Jay Alameda, Jeroen Audenaert +65
Modern large-scale physics experiments create datasets with sizes and streaming rates that can exceed those from industry leaders such as Google Cloud and Netflix. Fully processing…
Report of the Topical Group on Electroweak Precision Physics and Constraining New Physics for Snowmass 2021
Alberto Belloni, Ayres Freitas, Junping Tian +65
The precise measurement of physics observables and the test of their consistency within the standard model (SM) are an invaluable approach, complemented by direct searches for new…
Report of the Topical Group on Higgs Physics for Snowmass 2021: The Case for Precision Higgs Physics
Sally Dawson, Patrick Meade, Isobel Ojalvo +78
A future Higgs Factory will provide improved precision on measurements of Higgs couplings beyond those obtained by the LHC, and will enable a broad range of investigations across t…
Data Science and Machine Learning in Education
Gabriele Benelli, Thomas Y. Chen, Javier Duarte +20
The growing role of data science (DS) and machine learning (ML) in high-energy physics (HEP) is well established and pertinent given the complex detectors, large data, sets and sop…
Ultra-low latency recurrent neural network inference on FPGAs for physics applications with hls4ml
Elham E Khoda, Dylan Rankin, Rafael Teixeira de Lima +10
Recurrent neural networks have been shown to be effective architectures for many tasks in high energy physics, and thus have been widely adopted. Their use in low-latency environme…
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…
Study of Electroweak Phase Transition in Exotic Higgs Decays at the CEPC
Zhen Wang, Xuliang Zhu, Elham E Khoda +7
A strong first-order electroweak phase transition (EWPT) can be induced by light new physics weakly coupled to the Higgs. This study focuses on a scenario in which the first-order…
Anomalous production of massive gauge boson pairs at muon colliders
Brad Abbott, Aram Apyan, Bianca Azartash-Namin +11
The prospects of searches for anomalous production of hadronically decaying weak boson pairs at proposed high-energy muon colliders are reported. Muon-muon collision events are sim…