6 citations · 9 across the 20 of their papers we have counts for
20 papers
The logical set of Gaussian states under the stabilizer subsystem decomposition
Santiago Zamora, Kaustav Chatterjee, Ulrik Lund Andersen +4
Universal quantum computation requires non-Gaussianity in continuous-variable systems and non-stabilizerness in discrete-variable systems. Yet, mapping a Gaussian state into the Go…
Leveraging Industrial Foundation Models at the Edge of Particle Physics Detectors via Distillation Learning and Hardware Co-design
Gia Ancone, Qibin Liu, Liangyu Wu +1
Data acquisition (DAQ) systems at future particle physics experiments stand to benefit from the extremes of AI/ML development: large-scale foundation models can enhance the perform…
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…
A Lightweight Foundation Model for Collider Physics with Multi-Domain Adaptation
Liangyu Wu, Qibin Liu, Alexander Yue +1
We present a lightweight approach to foundation modeling (\textbf{NEXUS}) that leverages pre-trained learning from collider physics data towards out-of-domain tasks in other scient…
Classical Hardware Acceleration of Quantum Autoencoders for Real-Time Anomaly Detection in Collider Experiments
Ivan Ge, Sagar Addepalli, Abhilasha Dave +1
Quantum machine learning (QML) algorithms in high energy physics (HEP) can efficiently represent and leverage long-range, high-order correlations in high-dimensional collider data,…
Towards anomaly detection searches for new physics signatures including Higgs bosons with weakly supervised machine learning
Chi Lung Cheng, Julia Gonski, Runze Li +5
The Higgs boson, with its universal coupling to mass, provides a broadly applicable portal to sectors beyond the Standard Model and is therefore a natural anchor for anomaly detect…
Transformer-based machine learning using low-level calorimeter signals for collimated photon identification at collider experiments
Gabriel Matos, Lauren Larson, Abhilasha Dave +8
Electromagnetic calorimeters provide essential information for reconstructing and selecting both Standard Model (SM) and potential beyond the SM physics events at high-energy parti…
Machine Learning Enables Real-Time Waveform Decomposition for Dual-Readout Calorimetry
Liangyu Wu, Qibin Liu, Marco Toliman Lucchini +3
Dual-readout calorimeters achieve superior energy resolution by simultaneously measuring Cherenkov and scintillation signals for event-by-event electromagnetic fraction correction,…
SciFi: A Safe, Lightweight, User-Friendly, and Fully Autonomous Agentic AI Workflow for Scientific Applications
Qibin Liu, Julia Gonski
Recent advances in agentic AI have enabled increasingly autonomous workflows, but existing systems still face substantial challenges in achieving reliable deployment in real-world…
Agentic AI Detector Co-design and Optimization in Vertically Integrated Differentiable Full Simulations
Wonyong Chung, Qibin Liu, Liangyu Wu +1
We present the first implementation of AI agents into the design and optimization of detectors in high-energy physics experiments via a bi-level optimization framework that vertica…
Hardware-Aware Tensor Networks for Real-Time Quantum-Inspired Anomaly Detection at Particle Colliders
Sagar Addepalli, Prajita Bhattarai, Abhilasha Dave +1
Quantum machine learning offers the ability to capture complex correlations in high-dimensional feature spaces, crucial for the challenge of detecting beyond the Standard Model phy…
Ultra Fast Calorimeter Simulation with Generative Machine Learning on FPGAs
P. Alex May, Qibin Liu, Julia Gonski +1
Computationally expensive, high-accuracy detector simulations are a major bottleneck for many particle physics experiments such as those at the Large Hadron Collider (LHC) as well…
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…
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…
Convolutional Autoencoders for Data Compression and Anomaly Detection in Small Satellite Technologies
Dishanand Jayeprokash, Julia Gonski
Small satellite technologies have enhanced the potential and feasibility of geodesic missions, through simplification of design and decreased costs allowing for more frequent launc…
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…
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).…
Highlights from Long-Lived Particle Searches at ATLAS
Julia Gonski
The latest results of long-lived particle (LLP) searches from the ATLAS Experiment at the Large Hadron Collider are presented. Analyses are presented with a focus on detector subsy…