91 citations · 339 across the 6 of their papers we have counts for
6 papers
Automating the Construction of Jet Observables with Machine Learning
Kaustuv Datta, Andrew Larkoski, Benjamin Nachman
Machine-learning assisted jet substructure tagging techniques have the potential to significantly improve searches for new particles and Standard Model measurements in hadronic fin…
LHC analysis-specific datasets with Generative Adversarial Networks
Bobak Hashemi, Nick Amin, Kaustuv Datta +2
Using generative adversarial networks (GANs), we investigate the possibility of creating large amounts of analysis-specific simulated LHC events at limited computing cost. This kin…
Unfolding with Generative Adversarial Networks
Kaustuv Datta, Deepak Kar, Debarati Roy
Correcting measured detector-level distributions to particle-level is essential to make data usable outside the experimental collaborations. The term unfolding is used to describe…
Novel Jet Observables from Machine Learning
Kaustuv Datta, Andrew J. Larkoski
Previous studies have demonstrated the utility and applicability of machine learning techniques to jet physics. In this paper, we construct new observables for the discrimination o…
How Much Information is in a Jet?
Kaustuv Datta, Andrew Larkoski
Machine learning techniques are increasingly being applied toward data analyses at the Large Hadron Collider, especially with applications for discrimination of jets with different…
Beam energy dependence of pseudorapidity distributions of charged particles produced in heavy-ion collisions at RHIC and LHC energies
Sumit Basu, Tapan K. Nayak, Kaustuv Datta
Heavy-ion collisions at the Relativistic Heavy Ion Collider at Brookhaven National Laboratory and the Large Hadron Collider at CERN probe matter at extreme conditions of temperatur…