14 papers
Learning to Trigger: Reinforcement Learning at the Large Hadron Collider
Zixin Ding, Shaghayegh Emami, Giovanna Salvi +7
High-throughput scientific facilities such as the Large Hadron Collider depend on real-time event filtering (\textit{triggering}) under tight constraints on bandwidth, latency, and…
Patch Hierarchical Attention Transformer for Efficient Particle Jet Tagging
Aaron Wang, Zihan Zhao, Alan Xia +5
Real-time jet tagging is critical for identifying short-lived particle decays in the high-throughput detectors of the Large Hadron Collider, where real-time trigger systems respons…
Spatially Aware Linear Transformer (SAL-T) for Particle Jet Tagging
Aaron Wang, Zihan Zhao, Subash Katel +6
Transformers are very effective in capturing both global and local correlations within high-energy particle collisions, but they present deployment challenges in high-data-throughp…
On-chip probabilistic inference for charged-particle tracking at the sensor edge
Arghya Ranjan Das, David Jiang, Rachel Kovach-Fuentes +32
Modern scientific instruments operate under increasingly extreme constraints on bandwidth, latency, and power. Inference at the sensor edge determines experimental data collection…
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…
Poisson Log-Normal Process for Count Data Prediction
Anushka Saha, Abhijith Gandrakota, Alexandre V. Morozov
Modeling count data is important in physics and other scientific disciplines, where measurements often involve discrete, non-negative quantities such as photon or neutrino detectio…