6 citations · 10 across the 5 of their papers we have counts for
5 papers
Contrastive Learning for Interpretable Anomaly Detection at Collider Experiments
Haoyi Jia, Sagar Addepalli, Julia Gonski
Generic event-level anomaly detection for collider physics has two recurring problems: anomaly scores are hard to interpret, and they correlate strongly with energy scale and objec…
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…
Analysis of Hardware Synthesis Strategies for Machine Learning in Collider Trigger and Data Acquisition
Haoyi Jia, Abhilasha Dave, Julia Gonski +1
To fully exploit the physics potential of current and future high energy particle colliders, machine learning (ML) can be implemented in detector electronics for intelligent data p…
Variational Autoencoders for At-Source Data Reduction and Anomaly Detection in High Energy Particle Detectors
Alexander Yue, Haoyi Jia, Julia Gonski
Detectors in next-generation high-energy physics experiments face several daunting requirements, such as high data rates, damaging radiation exposure, and stringent constraints on…
Embedded FPGA Developments in 130nm and 28nm CMOS for Machine Learning in Particle Detector Readout
Julia Gonski, Aseem Gupta, Haoyi Jia +5
Embedded field programmable gate array (eFPGA) technology allows the implementation of reconfigurable logic within the design of an application-specific integrated circuit (ASIC).…