A Deep Neural Network for Simultaneous Estimation of b Jet Energy and Resolution
- PDF / 1,642,438 Bytes
- 20 Pages / 595.276 x 790.866 pts Page_size
- 44 Downloads / 170 Views
ORIGINAL ARTICLE
A Deep Neural Network for Simultaneous Estimation of b Jet Energy and Resolution CMS Collaboration193 Received: 24 December 2019 / Accepted: 20 June 2020 © The Author(s) 2020
Abstract We describe a method to obtain point and√dispersion estimates for the energies of jets arising from b quarks produced in proton–proton collisions at an energy of s = 13 TeV at the CERN LHC. The algorithm is trained on a large sample of simulated b jets and validated on data recorded by the CMS detector in 2017 corresponding to an integrated luminosity of 41 fb−1 . A multivariate regression algorithm based on a deep feed-forward neural network employs jet composition and shape information, and the properties of reconstructed secondary vertices associated with the jet. The results of the algorithm are used to improve the sensitivity of analyses that make use of b jets in the final state, such as the observation of Higgs boson decay to bb̄ . Keywords CMS · b jets · Higgs boson · Jet energy · Jet resolution · Deep learning
Introduction Following the discovery of the 125 GeV Higgs boson reported by the ATLAS and CMS Collaborations at the CERN LHC in 2012[1–3], a rich research program was established to probe this new particle. The program includes the measurement of all production and decay modes that are accessible at the LHC. The decay of the Higgs boson into a pair of vector bosons was established with a statistical significance higher than five standard deviations individually for photon, Z and W pairs using data collected at the √ LHC from 2011 to 2013 at center-of-mass energies of s = 7 and 8 TeV[4–9]. A few years later, the combination of CMS data sets collected at 8 and 13 TeV was used to report the observation of Higgs boson decay to a pair of 𝜏 leptons[10], followed by the observation of the associated production of a Higgs boson with a top quark–antiquark pair ( t̄t)[11, 12]. Higgs boson decay to a b quark–antiquark pair ( bb̄ ) was only recently announced by the CMS[13] and ATLAS[14] collaborations, despite it being the dominant decay mode. This is because of the challenges associated with separating the signal from the large background of bb̄ produced by quantum chromodynamics (QCD) processes. Good resolution of Deceased: A. M. Sirunyan, G. Vesztergombi, S. Guts, G. R. Snow Extended author information available on the last page of the article
the reconstructed invariant mass of Higgs boson candidates is necessary to have a more favorable signal-to-background ratio. This is achieved in CMS by the method described in this paper, based on a deep neural network (DNN) that estimates the energy of jets originating from b quarks (b jets). Similar algorithms, using neural networks, were previously used by the CDF Collaboration at the Tevatron [15, 16], and BDT-based energy regressions were used earlier by the CMS Collaboration to estimate the energy of b jets[17]. The approach described in this paper is to use a regression algorithm that is implemented in a feed-forward neural network with six hidden layers tra
Data Loading...
