91 citations · 339 across the 6 of their papers we have counts for
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hep-ph2019★ 39 cited
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…
hep-ph2017★ 78 cited
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…
hep-ph2017★ 91 cited
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…