6 papers
Tagging fully hadronic exotic decays of the vectorlike quark using a graph neural network
Jai Bardhan, Tanumoy Mandal, Subhadip Mitra +2
Following up on our earlier study in [J. Bardhan et al., Machine learning-enhanced search for a vectorlike singlet B quark decaying to a singlet scalar or pseudoscalar, Phys. Rev.…
TooLQit: Leptoquark Models and Limits
Arvind Bhaskar, Yash Chaurasia, Arijit Das +5
We introduce the leptoquark (LQ) toolkit, TooLQit, which includes leading-order FeynRules models for all types of LQs and a Python-based calculator, named CaLQ, to test if a set of…
HEP-JEPA: A foundation model for collider physics using joint embedding predictive architecture
Jai Bardhan, Radhikesh Agrawal, Abhiram Tilak +2
We present a transformer architecture-based foundation model for tasks at high-energy particle colliders such as the Large Hadron Collider. We train the model to classify jets usin…
Constructing sensible baselines for Integrated Gradients
Jai Bardhan, Cyrin Neeraj, Mihir Rawat +1
Machine learning methods have seen a meteoric rise in their applications in the scientific community. However, little effort has been put into understanding these "black box" model…
Loss function to optimise signal significance in particle physics
Jai Bardhan, Cyrin Neeraj, Subhadip Mitra +1
We construct a surrogate loss to directly optimise the significance metric used in particle physics. We evaluate our loss function for a simple event classification task using a li…
Unsupervised and lightly supervised learning in particle physics
Jai Bardhan, Tanumoy Mandal, Subhadip Mitra +2
We review the main applications of machine learning models that are not fully supervised in particle physics, i.e., clustering, anomaly detection, detector simulation, and unfoldin…