Publications (7)
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…
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…
Report of the Topical Group on Physics Beyond the Standard Model at Energy Frontier for Snowmass 2021
Tulika Bose, Antonio Boveia, Caterina Doglioni +318
This is the Snowmass2021 Energy Frontier (EF) Beyond the Standard Model (BSM) report. It combines the EF topical group reports of EF08 (Model-specific explorations), EF09 (More gen…
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…
ADFilter -- A Web Tool for New Physics Searches With Autoencoder-Based Anomaly Detection Using Deep Unsupervised Neural Networks
Sergei V. Chekanov, Wasikul Islam, Rui Zhang +1
A web-based tool called ADFilter was developed to process collision events using autoencoders based on a deep unsupervised neural network. The autoencoders are trained on a small f…
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…