Related papers: Signal mixture estimation for degenerate heavy Hig…
In this work, we deep-learn light charged Higgs signal in top quark decays which poses difficulties due to strong W boson contamination. We construct Deep Neural Networks (DNN) with appropriate architecture and determine signal extraction…
The consecutive steps of cascade decay initiated by H to tau tau can be useful for the measurement of Higgs couplings and in particular of the Higgs boson parity. In the previous papers we have found, that multi-dimensional signatures of…
Higgs boson is a fundamental particle, and the classification of Higgs signals is a well-known problem in high energy physics. The identification of the Higgs signal is a challenging task because its signal has a resemblance to the…
The H to tau tau decays form the prime channel for the measurement of the Higgs boson state and tests of the CP invariance of Higgs boson couplings. A previous study has shown the viability of deep learning techniques for the measurement.…
The Higgs boson is thought to provide the interaction that imparts mass to the fundamental fermions, but while measurements at the Large Hadron Collider (LHC) are consistent with this hypothesis, current analysis techniques lack the…
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…
Deep neural networks are prone to overconfident predictions on outliers. Bayesian neural networks and deep ensembles have both been shown to mitigate this problem to some extent. In this work, we aim to combine the benefits of the two…
We conduct a detailed exploration of charged Higgs boson masses $M_{H^{\pm}}$ within the range of $100-190~GeV$. This investigation is grounded in the benchmark points that comply with experimental constraints, allowing us to systematically…
At the CERN Large Hadron Collider experiment, the non-resonant double Higgs production via vector-boson fusion represents a unique mean to probe the VVHH (V=Z, W$^{\pm}$) Higgs self-coupling at the current center of mass energies. Such a…
The consecutive steps of H to tau tau cascade can be useful for the measurement of Higgs couplings and parity. The analysis methos of ATLAS and CMS Collaborations was to fit a one-dimensional distribution of the phi* angle, phi*, which is…
We study mono-Higgs signatures emerging in an illustrative new physics scenario involving Standard Model Higgs boson decays to bottom quark pairs using Hybrid Deep Neural Networks. We use a Multi-Layer Perceptron to analyze the kinematic…
We propose a neural network training method capable of accounting for the effects of systematic variations of the data model in the training process and describe its extension towards neural network multiclass classification. The procedure…
We study the implications of the LHC Higgs signals on the Higgs mixing in the next-to-minimal supersymmetric standard model (NMSSM). The Higgs couplings can depart from their values in the standard model (SM) due to mixing effects. However…
The reconstruction of the invariant mass of $\tau$ lepton pairs is important for analyses containing Higgs and Z bosons decaying to $\tau^{+}\tau^{-}$, but highly challenging due to the neutrinos from the $\tau$ lepton decays, which cannot…
We investigate properties of a classifier applied to the measurements of the CP state of the Higgs boson in $H\rightarrow\tau\tau$ decays. The problem is framed as binary classifier applied to individual instances. Then the prior knowledge…
Determining the form of the Higgs potential is one of the most exciting challenges of modern particle physics. Higgs pair production directly probes the Higgs self-coupling and should be observed in the near future at the High-Luminosity…
Measuring the Higgs trilinear self-coupling $\lambda_{hhh}$ is experimentally demanding but fundamental for understanding the shape of the Higgs potential. We present a comprehensive analysis strategy for the HL-LHC using di-Higgs events in…
We explore the feasibility of measuring the CP properties of the Higgs boson coupling to $\tau$ leptons at the High Luminosity Large Hadron Collider (HL-LHC). Employing detailed Monte Carlo simulations, we analyze the reconstruction of the…
Advances in machine learning have led to an emergence of new paradigms in the analysis of large data which could assist traditional approaches in the search for new physics amongst the immense Standard Model backgrounds at the Large Hadron…
We consider the problem of uncertainty estimation in the context of (non-Bayesian) deep neural classification. In this context, all known methods are based on extracting uncertainty signals from a trained network optimized to solve the…