Throughout different scientific disciplines, there is a need for machine learning models that leverage domain knowledge through inductive bias and data representations to maximize their potential. In addition, efficient machine learning implementations optimized for inference in hardware are critical for low-latency, high-throughput, or limited-resource scientific applications. In this...
This talk will provide an overview of HAC’s achievements over the past year and briefly introduce some ongoing projects.
Update/overview presentation on NeuroAI and Neuroscience developments and achievements in the past year.
We present an overview of current and planned High Energy Physics research activities in A3D3, driven by real-time machine learning. We report the first deployment of ML-based anomaly detection at the Level-1 trigger in both CMS and ATLAS, realized through the AXOL1TL (“Anomaly eXtraction L1 Trigger Lightweight”) and GELATO (“Generic Event-Level Anomalous Trigger Option”) algorithms,...
Machine learning is a critical tool for analysis and decision making across a wide range of scientific domains, from particle physics to materials science. However, the deployment of neural networks in resource constrained environments, such as the Level-1 Trigger and edge devices, remains a significant challenge. This often requires specialized expertise in both neural architecture design and...
The absence of beyond-Standard-Model physics discoveries at the LHC suggests that new physics may evade conventional trigger strategies. The existing ATLAS triggers are required to control data collection rates with high energy thresholds and target signal topologies specific to only certain models. Unsupervised machine learning enables the use of anomaly detection, presenting a unique...
To advance the search for 0𝜈𝛽𝛽 decay, the LEGEND-1000 experiment will require scaling-up from its predecessor, LEGEND-200, the cryostat in particular containing a copper reentrant tube (RT) in order to create separate volumes containing underground versus atmospheric argon. As the thinnest part of the RT will only be ~3 mm thick, small-scale pressure and strain testing is underway to confirm...
The Higgs boson's self-coupling has a significant impact on the production rate of multiple Higgs bosons. Measuring the self-coupling at the CERN LHC is crucial because any deviations from our expectations could potentially lead to new discoveries of physics beyond the standard model of particle physics. Most events are fully hadronic, meaning every Higgs boson decays to a bottom...
As deep learning methods and particularly Large Language Models have shown huge promise in a variety of applications, we apply a model inspired by BERT (Bidirectional Encoder Representations from Transformers), developed by Google and utilizing the multi-headed attention mechanism, to a high energy physics problem. We focus on the process of top quark-antiquark decay reconstruction and...
Fast and accurate parameter estimation of gravitational wave (GW) signals is crucial in multi-messenger astrophysics. These signals are the first to arrive, requiring prompt analysis of the merger properties. However, extracting these parameters from observed binary mergers from GW detectors remains a computational bottleneck. Current approaches, such as Markov-Chain Monte Carlo (MCMC) methods...
We present an overview of current and planned High Energy Physics research activities in A3D3, driven by real-time machine learning. We report the first deployment of ML-based anomaly detection at the Level-1 trigger in both CMS and ATLAS, realized through the AXOL1TL (“Anomaly eXtraction L1 Trigger Lightweight”) and GELATO (“Generic Event-Level Anomalous Trigger Option”) algorithms,...
Throughout different scientific disciplines, there is a need for machine learning models that leverage domain knowledge through inductive bias and data representations to maximize their potential. In addition, efficient machine learning implementations optimized for inference in hardware are critical for low-latency, high-throughput, or limited-resource scientific applications. In this...
