Speaker
Mr
Vishal Singh Ngairangbam
(Physical Research Laboratory)
Description
We show that using the full tower information in the form of an image,
a Convolutional Neural Network(CNN) can efficiently recognise Vector boson fusion(VBF)
signal from non VBF backgrounds at the Large Hadron Collider(LHC). As a concrete example, we compare with existing state-of-the-art techniques currently in use, we show that deep-learning algorithms like a CNN can significantly improve the bounds on the invisible branching ratio of the recently discovered Higgs boson. This can help constrain many beyond the Standard Model(BSM) theories, which relies on the Higgs decaying to any new stable (or semi stable) particles which do not interact with the known Standard Model particles.
Authors
Akanksaa Bhardwaj
(University of Glasgow)
Aruna Nayak
(Institute of Physics, Bhubaneswar )
Partha Konar
(Physical Research Laboratory, Ahmedabad, Gujarat-380 009, INDIA)
Mr
Vishal Singh Ngairangbam
(Physical Research Laboratory)
