Related papers: On the efficiency of parameter space exploration: …

In these proceedings, we present a study of a combined singlet--doublet fermion and triplet scalar model for dark matter (DM). Together, these models form a simple extension of the Standard Model (SM) that can account for DM and explain the…

Modern Machine Learning (ML) and Deep Neural Networks (DNNs) often operate on high-dimensional data and rely on overparameterized models, where classical low-dimensional intuitions break down. In particular, the proportional regime where…

The Pseudo-Marginal (PM) algorithm is a popular Markov chain Monte Carlo (MCMC) method used to sample from a target distribution when its density is inaccessible, but can be estimated with a non-negative unbiased estimator. Its performance…

Efficient sampling of many-dimensional and multimodal density functions is a task of great interest in many research fields. We describe an algorithm that allows parallelizing inherently serial Markov chain Monte Carlo (MCMC) sampling by…

We analyze two different scenarios for the late Universe dynamics, resulting into Hubble parameters deviating from the $\Lambda$CDM, mainly for the presence of an additional free parameter, which is the dark energy parameter. The first…

We simultaneously investigate source, detector and matter non-standard neutrino interactions at the proposed DUNE experiment. Our analysis is performed using a Markov Chain Monte Carlo exploring the full parameter space. We find that the…

Bayesian statistics and Markov Chain Monte Carlo (MCMC) algorithms have found their place in the field of Cosmology. They have become important mathematical and numerical tools, especially in parameter estimation and model comparison. In…

We present a minimal sub-GeV thermal Dark Matter (DM) model where the DM primarily interacts with neutrinos and participates in neutrino mass generation through quantum loop corrections at one-loop level. We discuss the challenges in…

In this paper, we introduce a scanner package enhanced by deep learning (DL) techniques. The proposed package addresses two significant challenges associated with previously developed DL-based methods: slow convergence in high-dimensional…

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…

The scotogenic model of radiative neutrino mass with $Z_2$ or $U(1)_D$ dark matter is shown to accommodate $\Delta(27)$ symmetry naturally. The resulting neutrino mass matrix is identical to either of two forms, one proposed in 2006, the…

Monte Carlo (MC) algorithms are commonly employed to explore high-dimensional parameter spaces constrained by data. All the statistical information obtained in the output of these analyses is contained in the Markov chains, which one needs…

Map Space Exploration is the problem of finding optimized mappings of a Deep Neural Network (DNN) model on an accelerator. It is known to be extremely computationally expensive, and there has been active research looking at both heuristics…

We consider some implications of X-ray lines from certain astronomical objects as potential effects of dark matter decay in the context of the scotogenic model, where neutrinos acquire mass radiatively via one-loop interactions with dark…

Performance optimization of deep learning models is conducted either manually or through automatic architecture search, or a combination of both. On the other hand, their performance strongly depends on the target hardware and how…

In this paper we investigate a new phenomenological parameterization for unified dark matter and dark energy based on the polynomial expansion of the barotropic equation of state parameter $w$. Our parameterization provides well-behaving…

Deep neural networks (DNNs) are often coupled with physics-based models or data-driven surrogate models to perform fault detection and health monitoring of systems in the low data regime. These models serve as digital twins to generate…

We use Deep Convolutional Neural Networks (DCNNs) for image segmentation problems. DCNNs can well extract the features from natural images. However, the classification functions in the existing network architecture of CNNs are simple and…

Training energy-based probabilistic models is confronted with apparently intractable sums, whose Monte Carlo estimation requires sampling from the estimated probability distribution in the inner loop of training. This can be approximately…

Propagation of charged cosmic-rays in the Galaxy depends on the transport parameters, whose number can be large depending on the propagation model under scrutiny. A standard approach for determining these parameters is a manual scan,…