TY - JOUR
T1 - Portraying double higgs at the large hadron collider
AU - Kim, Jeong Han
AU - Kim, Minho
AU - Kong, Kyoungchul
AU - Matchev, Konstantin T.
AU - Park, Myeonghun
N1 - Publisher Copyright:
© Copyright owned by the author(s) under the terms of the Creative Commons.
PY - 2019
Y1 - 2019
N2 - We examine the discovery potential for double Higgs production at the high luminosity LHC in the final state with two b-tagged jets, two leptons and missing transverse momentum. Although this dilepton final state has been considered a difficult channel due to the large backgrounds, we argue that it is possible to obtain a sizable signal significance, by adopting a deep learning framework making full use of the relevant kinematics along with the jet images from the Higgs decay. The proposed method can be easily generalized to the semi-leptonic channel of double Higgs production, as well as to other processes with similar final states.
AB - We examine the discovery potential for double Higgs production at the high luminosity LHC in the final state with two b-tagged jets, two leptons and missing transverse momentum. Although this dilepton final state has been considered a difficult channel due to the large backgrounds, we argue that it is possible to obtain a sizable signal significance, by adopting a deep learning framework making full use of the relevant kinematics along with the jet images from the Higgs decay. The proposed method can be easily generalized to the semi-leptonic channel of double Higgs production, as well as to other processes with similar final states.
UR - http://www.scopus.com/inward/record.url?scp=85101200331&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:85101200331
SN - 1824-8039
VL - 372
JO - Proceedings of Science
JF - Proceedings of Science
M1 - 013
T2 - 2019 Artificial Intelligence for Science, Industry and Society, AISIS 2019
Y2 - 21 October 2019 through 25 October 2019
ER -