Related papers: $\texttt{SpaceMath v.2.0}$ with Machine Learning. …

We present up-to-date constraints on a generic Higgs parameter space. An accurate assessment of these exclusions must take into account statistical, and potentially signal, fluctuations in the data currently taken at the LHC. For this, we…

Searches for the neutral Higgs bosons h0 and A0, are used to obtain limits on the Type II Two Higgs Doublet Model (2HDM(II)) with no CP-violation in the Higgs sector and no additional particles besides the five Higgs bosons. The analysis…

We present SUSY_FLAVOR version 2.5 - a Fortran 77 program that calculates low-energy flavor observables in the general $R$-parity conserving MSSM. For a set of MSSM parameters as input, the code gives predictions for: 1. Electric dipole…

The last few years have seen the emergence of a wide array of novel techniques for analyzing high-precision data from upcoming galaxy surveys, which aim to extend the statistical analysis of galaxy clustering data beyond the linear regime…

Much attention has recently been devoted to data-based computing of evolution of physical systems. In such approaches, information about data points from past trajectories in phase space is used to reconstruct the equations of motion and to…

The exploration of planetary bodies in our Solar system and beyond relies on the processing and interpretation of large, spatio-temporally inconsistent, and heterogeneous datasets. Recent advances in machine learning (ML) provide…

The next generation of particle physics experiments will face a new era of challenges in data acquisition, due to unprecedented data rates and volumes along with extreme environments and operational constraints. Harnessing this data for…

We examined the influence of additional scalar doublet on the parameter space of the Standard Model supplemented with a generation of new vector like leptons. In particular we identified the viable regions of parameter space by inspecting…

The inert 2-Higgs Doublet Model (i2HDM) is a well-motivated minimal consistent Dark Matter (DM) model, but it is rather challenging to test at the Large Hadron Collider (LHC) in the parameter space allowed by relic density and DM direct…

A comprehensive phenomenological analysis of a two Higgs doublet model, with flavor changing scalar currents at the tree level, called model III, is presented. Constraints from existing experimental information especially on (Delta F)=2…

The Higgs sector of models beyond the standard model requires special attention and study, since through them, a natural explanation can be offered to current questions such as the big differences in the values of the masses of the quarks…

The Type-II Seesaw Model provides an attractive scenario to account for Majorana-neutrino masses. Its extended Higgs sector, if sufficiently light, can have a rich and distinctive phenomenology at the LHC while yielding automatically an…

A comprehensive extension of the ordinary 2-Higgs Doublet Model (2HDM), supplemented by Vector-Like Quarks (VLQs), in the "alignment limit" is presented. In such a scenario, we study the possibility that Large Hadron Collider (LHC) searches…

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…

In this paper we present MLaut (Machine Learning AUtomation Toolbox) for the python data science ecosystem. MLaut automates large-scale evaluation and benchmarking of machine learning algorithms on a large number of datasets. MLaut provides…

We introduce version 2.0 of $Z'$-explorer, a software tool which provides a simple, fast and user-friendly test of models with an extra $U(1)$ gauge boson ($Z'$) against experimental LHC results. The main novelty of the second version is…

This paper introduces a deep learning system based on a quantum neural network for the binary classification of points of a specific geometric pattern (Two-Moons Classification problem) on a plane. We believe that the use of hybrid deep…

Most of the existing approaches focus on specific visual tasks while ignoring the relations between them. Estimating task relation sheds light on the learning of high-order semantic concepts, e.g., transfer learning. How to reveal the…

Machine learning (ML) is often viewed as a black-box regression technique that is unable to provide considerable scientific insight. ML models are universal function approximators and - if used correctly - can provide scientific information…

Machine Learning is a powerful tool to reveal and exploit correlations in a multi-dimensional parameter space. Making predictions from such correlations is a highly non-trivial task, in particular when the details of the underlying dynamics…