Related papers: $\texttt{SpaceMath v.2.0}$ with Machine Learning. …
Discarding the prejudice about fine tuning, we propose a novel and efficient approach to identify relevant regions of fundamental parameter space in supersymmetric models with some amount of fine tuning. The essential idea is the mapping of…
The search for evidence of extended electroweak symmetry breaking has entered a new phase with the discovery of a Standard Model (SM)-like Higgs at the LHC. The measurement of Higgs couplings and direct searches for additional scalars…
The 2HDM+S is a singlet extension of the Two-Higgs-Doublet Model (2HDM), which offers rich collider phenomenology. In this paper, we parametrize the 2HDM+S with the Higgs masses and mixing angles, which provide a model-independent framework…
We present a parameter-free variant of the halo model that significantly improves the precision of matter clustering predictions, particularly in the challenging 1-halo to 2-halo transition regime, where standard halo models often fail.…
After the observation in 2012 of a new scalar particle closely resembling the Higgs boson of the Standard Model of particle physics, there is a general consensus that there must be Physics Beyond the Standard Model, with present experiments…
Employing effective field theory techniques, we advance computations of thermal parameters that enter predictions for the gravitational wave spectra from first-order electroweak phase transitions. Working with the real-singlet-extended…
Based on recent advancements in using machine learning for classical density functional theory for systems with one-dimensional, planar inhomogeneities, we propose a machine learning model for application in two dimensions (2D) akin to…
The search for evidence of beyond Standard Model Higgs bosons is an integral part of the Higgs boson studies at the LHC. This article reviews recent beyond Standard Model Higgs boson searches using Run I LHC proton-proton collision data…
Investigation of well-motivated parameter space in the theories of Beyond the Standard Model (BSM) plays an important role in new physics discoveries. However, a large-scale exploration of models with multi-parameter or equivalent solutions…
We advocate for a new paradigm of cosmological likelihood-based inference, leveraging recent developments in machine learning and its underlying technology, to accelerate Bayesian inference in high-dimensional settings. Specifically, we…
This paper addresses the question of observability of neutral Higgs bosons through the leptonic decay in a two Higgs doublet model (2HDM). Both scalar and pseudoscalar Higgs bosons ($H,~A$) are considered. The model is set to type IV to…
High-dimensional tensor models are notoriously computationally expensive to train. We present a meta-learning algorithm, MMT, that can significantly speed up the process for spatial tensor models. MMT leverages the property that spatial…
We applied machine learning to the entire data history of ESO's High Accuracy Radial Velocity Planet Searcher (HARPS) instrument. Our primary goal was to recover the physical properties of the observed objects, with a secondary emphasis on…
Study of the production of pairs of top quarks in association with a Higgs boson is one of the primary goals of the Large Hadron Collider over the next decade, as measurements of this process may help us to understand whether the uniquely…
We develop a machine learning method for mapping data originating from both Standard Model processes and various theories beyond the Standard Model into a unified representation (latent) space while conserving information about the…
FlexibleSUSY is a framework for the automated computation of physical quantities (observables) in models beyond the Standard Model (BSM). This paper describes an extension of FlexibleSUSY which allows to define and add new observables that…
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…
It is possible to ameliorate the Higgs vacuum stability problem by switching over to two Higgs doublet models (2HDM), ensuring a stable electroweak vacuum up to the Planck scale, even though the top quark mass may be on the high side.…
Two-Higgs-Doublet Model Evolver (2HDME) is a C++ program that provides the functionality to perform fast renormalization group equation running of the general, potentially CP-violating, 2 Higgs Doublet Model at 2-loop order. Simple…
We deploy an advanced Machine Learning (ML) environment, leveraging a multi-scale cross-attention encoder for event classification, towards the identification of the $gg\to H\to hh\to b\bar b b\bar b$ process at the High Luminosity Large…