Related papers: Exploring Scotogenic Parameter Spaces and Mapping …
Multi-objective optimization is a crucial matter in computer systems design space exploration because real-world applications often rely on a trade-off between several objectives. Derivatives are usually not available or impractical to…
For general multi-objective optimization problems, we propose a novel performance metric called domination measure to measure the quality of a solution, which can be intuitively interpreted as the size of the portion of the solution space…
A wide range of techniques have been developed to search for particle dark matter, including direct detection, indirect detection, and collider searches. The prospects for the detection of neutralino dark matter is quite promising for each…
The observable cosmos exhibits sizable baryon asymmetry, small active neutrino masses, and the presence of dark matter (DM). To address these phenomena together, we propose a two component DM scenario in an extension of Scotogenic model,…
Data analysis in science, e.g., high-energy particle physics, is often subject to an intractable likelihood if the observables and observations span a high-dimensional input space. Typically the problem is solved by reducing the…
Neutrino masses and dark matter (DM) might have a common origin. The scotogenic model can be considered the proto-type model realizing this idea, but many other variants exist. In this paper we explore the phenomemology of a particular DM…
The search for relativistic scattering signals of cosmogenic light dark matter at terrestrial detectors has received increasing attention as an alternative approach to probe dark-sector physics. Large-volume neutrino experiments are well…
In real life, mostly problems are dynamic. Many algorithms have been proposed to handle the static problems, but these algorithms do not handle or poorly handle the dynamic environment problems. Although, many algorithms have been proposed…
A scotogenic model can radiatively generate the observed neutrino mass, provide a dark matter candidate, and lead to rare lepton flavor-violating processes. We aim to extend the model to establish a potential connection to the quark…
We perform a feasibility study to probe dark matter (DM) production at the LHC within a global $U(1)_L$ scotogenic model. The study is conducted using the Markov Chain Monte Carlo numerical method, considering the viable parameter space of…
We study the possibility of probing the radiative Dirac seesaw model with dark sector particles going inside the loop, popularly referred to as the Dirac scotogenic model via measurements of effective relativistic degrees of freedom ${\rm…
Markov decision processes (MDPs) are a popular model for performance analysis and optimization of stochastic systems. The parameters of stochastic behavior of MDPs are estimates from empirical observations of a system; their values are not…
We explore a fermionic dark matter model with a possible extension of Standard Model (SM) of particle physics into two Higgs doublet model. Higgs doublets couple to the singlet fermionic dark matter (FDM) through a non renormalisable…
This work investigates the allowed parameter spaces of a simplified dark matter (DM) model characterized by a spin-0 mediator with masses in the low to intermediate range ($ < $ 10 GeV). We systematically divide the parameter space into…
The recently introduced odd-one-out anomaly detection task involves identifying the odd-looking instances within a multi-object scene. This problem presents several challenges for modern deep learning models, demanding spatial reasoning…
We propose a novel and minimal framework where a light scalar field can give rise to dark matter (DM) self-interactions, while enhancing the CP symmetry required for successful baryon asymmetry of the Universe via leptogenesis route. For…
We consider renormalizable theories such that the scattering of dark matter off leptons arises at tree level, but scattering off nuclei only arises at loop. In this framework, the various dark matter candidates can be classified by their…
Gradient descent algorithms perform well in convex optimization but can get tied for finding local minima in non-convex optimization. A robust method that combines a spectral approach with nonmonotone line search strategy for solving…
The search for Dark Matter (DM) has great potential to reveal physics beyond the Standard Model. As such, searches for evidence of DM particles are being carried out using a wide range of techniques, such as direct searches for DM…
Recent results from ATLAS gives a Higgs mass of 125.5 GeV, further constrain already highly constrained supersymmetric models such as pMSSM or CMSSM/mSUGRA. Finding potentially discoverable and non-excluded regions of model parameter space…