5 papers
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…
Compact Representation of Particle-Collision Events for Physics-Informed Machine Learning
Wasikul Islam, Sergei Chekanov
We introduce a compact, physics-driven event representation, RMM-C46, designed to compress the high-dimensional rapidity mass matrix (RMM) into a low-dimensional, interpretable fea…
New Physics Searches at the LHC through Event-based Anomaly Detection and Development of ADFilter Web-tool
Wasikul Islam, Sergei Chekanov, Nicholas Luongo
This work presents advancements in model-agnostic searches for new physics at the Large Hadron Collider (LHC) through the application of event-based anomaly detection techniques ut…
Enhancing Sensitivity for Di-Higgs Boson Searches Using Anomaly Detection and Supervised Machine Learning Techniques
Sergei V. Chekanov, Wasikul Islam, Nicholas Luongo
This paper explores different strategies for enhancing sensitivity to new heavy resonances that decay into two or more Higgs bosons. This is achieved using two neural network archi…
WIPUNet: A Physics-inspired Network with Weighted Inductive Biases for Image Denoising
Wasikul Islam
In high-energy particle physics, collider measurements are contaminated by "pileup", overlapping soft interactions that obscure the hard-scatter signal of interest. Dedicated subtr…