Machine learning classification: case of Higgs boson CP state in H to tau tau decay at LHC

Machine Learning (ML) techniques are rapidly finding a place among the methods of High Energy Physics data analysis. Different approaches are explored concerning how much effort should be put into building high-level variables based on physics insight into the problem, and when it is enough to rely on low-level ones, allowing ML methods to find patterns without explicit physics model. In this paper we continue the discussion of previous publications on the CP state of the Higgs boson measurement of the H to tau tau decay channel with the consecutive tau^pm to rho^pm nu; rho^pm to pi^pm pi^0 and tau^pm to a_1^pm nu; a_1^pm to rho^0 pi^pm to 3 pi^pm cascade decays. The discrimination of the Higgs boson CP state is studied as a binary classification problem between CP-even (scalar) and CP-odd (pseudoscalar), using Deep Neural Network (DNN). Improvements on the classification from the constraints on directly non-measurable outgoing neutrinos are discussed. We find, that once added, they enhance the sensitivity sizably, even if only imperfect information is provided. In addition to DNN we also evaluate and compare other ML methods: Boosted Trees (BT), Random Forest (RF) and Support Vector Machine (SVN).Comment: 1+20 pages, 9 figures, 6 tables, extended content and improved readabilit

Resonant Di-Higgs Production at Gravitational Wave Benchmarks: A Collider Study using Machine Learning

We perform a complementarity study of gravitational waves and colliders in the context of electroweak phase transitions choosing as our template the xSM model, which consists of the Standard Model augmented by a real scalar. We carefully analyze the gravitational wave signal at benchmark points compatible with a first order phase transition, taking into account subtle issues pertaining to the bubble wall velocity and the hydrodynamics of the plasma. In particular, we comment on the tension between requiring bubble wall velocities small enough to produce a net baryon number through the sphaleron process, and large enough to obtain appreciable gravitational wave production. For the most promising benchmark models, we study resonant di-Higgs production at the high-luminosity LHC using machine learning tools: a Gaussian process algorithm to jointly search for optimum cut thresholds and tuning hyperparameters, and a boosted decision trees algorithm to discriminate signal and background. The multivariate analysis on the collider side is able either to discover or provide strong statistical evidence of the benchmark points, opening the possibility for complementary searches for electroweak phase transitions in collider and gravitational wave experiments.Comment: 22 pages, 6 figures, 3 tables. Version published in JHE

Searches for Low Mass Higgs Boson at the Tevatron

We present the result of the searches for a low mass Standard Model Higgs boson performed at the Tevatron ppbar collider (sqrt(s) =1.96 TeV) by the CDF and D0 experiments with an integrated luminosity of up to 8.5 fb^-1. Individual searches are discussed and classified according to their sensitivity. Primary channels rely on the associate production with a vector boson (WH or ZH) and the H->bbbar decay channel (favored for M_H<135 GeV/c^2). Event selection is based on the leptonic decay of the vector boson and the identification of b-hadron enriched jets. Each individual channel is sensitive, for M_H=115 GeV/c^2, to less than 5 times the SM expected cross section and the most sensitive channels can exclude a production cross section of 2.3 x sigma_H SM. Secondary channels rely on a variety of final states. Although they are from 2 to 5 times less sensitive than any primary channel, they contribute to the Tevatron combination and, in some cases, they pose strong constrains on exotic Higgs boson models.Comment: Presented at the 2011 Hadron Collider Physics symposium (HCP-2011), Paris, France, November 14-18 2011, 4 pages, 5 figur