Related papers: $\texttt{SpaceMath v.2.0}$ with Machine Learning. …
Nowadays, one needs to consider seriously the possibility that a large separation between the scale of new physics and the electroweak scale exists. Nevertheless, there are still observables in this scenario, in particular the Higgs mass,…
In this work, we use a recast of the Run II search for invisible Higgs decays within Vector Boson Fusion to constrain the parameter space of the Inert Doublet model, a two Higgs doublet model with a dark matter candidate. When including all…
Machine learning is applied to investigate the phase transition of two-dimensional complex plasmas. The Langevin dynamics simulation is employed to prepare particle suspensions in various thermodynamic states. Based on the resulted particle…
In this work, the standard model (SM) is extended with two right-handed (RH) neutrinos and two singlet neutral fermions to yield active neutrino masses via (2,2) inverse see-saw mechanism. We first validate the multi-dimensional model…
In this study, we explore the potential to probe the process $gg \to H_{\text{SM-like}} \to hh \to b\bar{b}\tau\tau$ at the Large Hadron Collider (LHC), within the framework of the Two Higgs Doublet Model (2HDM) Type-I. After performing a…
Many domains of high energy physics analysis are starting to explore machine learning techniques. Powerful methods can be used to identify and measure rare processes from previously insurmountable backgrounds. One of the most profound…
This study investigates the production of multi-top quark events final states containing up to four top quarks as a probe for new physics beyond the Standard Model (SM) within the framework of the Two Higgs Doublet Model (2HDM) Type-I.…
Traditional machine learning models, particularly neural networks, are rooted in finite-dimensional parameter spaces and nonlinear function approximations. This report explores an alternative formulation where learning tasks are expressed…
Searches for dark matter produced via scalar resonances in final states consisting of Standard Model (SM) particles and missing transverse momentum are of high relevance at the LHC. Motivated by dark-matter portal models, most existing…
We introduce a strategy to study the parameter space of the general, CP-conserving, two-Higgs-doublet Model (2HDM) with a softly broken Z_2-symmetry by means of a new "hybrid" basis. In this basis the input parameters are the measured…
After the LHC Run 1, the standard model (SM) of particle physics has been completed. Yet, despite its successes, the SM has shortcomings vis-\`{a}-vis cosmological and other observations. At the same time, while the LHC restarts for Run 2…
Recent innovations from machine learning allow for data unfolding, without binning and including correlations across many dimensions. We describe a set of known, upgraded, and new methods for ML-based unfolding. The performance of these…
Neutral long-lived particles (LLPs) are highly motivated by many BSM scenarios, such as theories of supersymmetry, baryogenesis, and neutral naturalness, and present both tremendous discovery opportunities and experimental challenges for…
Neural simulation-based inference is a powerful class of machine-learning-based methods for statistical inference that naturally handles high-dimensional parameter estimation without the need to bin data into low-dimensional summary…
Nuclear materials are often demanded to function for extended time in extreme environments, including high radiation fluxes and transmutation, high temperature and temperature gradients, stresses, and corrosive coolants. They also have a…
Machine-learning assisted jet substructure tagging techniques have the potential to significantly improve searches for new particles and Standard Model measurements in hadronic final states. Techniques with simple analytic forms are…
We construct a composite two-Higgs-doublet model (2HDM) within the context of dilaton effective field theory. This EFT describes the particle spectrum observed in lattice simulations of a near-conformal $SU(3)$ gauge field theory. A second…
The di-leptons and di-neutrinos observed in the final states of flavor-changing neutral b decays provide an ideal platform for probing physics beyond the standard model. Although the latest measurements of $R_{K^{(*)}}$ agree well with the…
Using very long baseline interferometry, the Event Horizon Telescope (EHT) collaboration has resolved the shadows of two supermassive black holes. Model comparison is traditionally performed in image space, where imaging algorithms…
We propose to interpret machine learning functions as physical observables, opening up the possibility to apply "standard" statistical-mechanical methods to outputs from neural networks. This includes histogram reweighting and finite-size…