相关论文: Exploring Scotogenic Parameter Spaces and Mapping …

The paper presents a comprehensive performance evaluation of some heuristic search algorithms in the context of autonomous systems and robotics. The objective of the study is to evaluate and compare the performance of different search…

We consider the standard model (SM) extended by a gauge singlet fermion as cold dark matter (SFCDM) and a gauge singlet scalar (singlet Higgs) as a mediator. The parameter space of the SM is enlarged by seven new ones. We obtain the total…

Multi-dimensional parameter spaces are commonly encountered in physics theories that go beyond the Standard Model. However, they often possess complicated posterior geometries that are expensive to traverse using techniques traditional to…

We propose a scotogenic extension of the Standard Model which can provide a scalar Dark Matter candidate in the new, theoretically previously unaddressed, intermediate region ($200\leq M_{DM}\leq 550$ GeV) and also generate light Dirac…

Many important physical processes have dynamics that are too complex to completely model analytically. Optimisation of such processes often relies on intuition, trial-and-error, or the construction of empirical models. Machine learning…

Many models containing particles which are candidates for dark matter, assume the standard model particles and the dark matter candidates are mediated by a spin-0 particle. At the LHC, one can use these models for dark matter searches. One…

We describe and analyze algorithms for shape-constrained symbolic regression, which allows the inclusion of prior knowledge about the shape of the regression function. This is relevant in many areas of engineering -- in particular whenever…

This paper is concerned with a recently developed paradigm for population-based optimization, termed particle filter optimization (PFO). This paradigm is attractive in terms of coherence in theory and easiness in mathematical analysis and…

We studied Dark Matter (DM) phenomenology with multiple DM species consisting of both scalar and vector DM particles in the Hidden Gauged SU(3) model of Arcadi et al. Because of the large parameter space in the Hidden Gauged SU(3) model we…

In this work, we consider methods for solving large-scale optimization problems with a possibly nonsmooth objective function. The key idea is to first specify a class of optimization algorithms using a generic iterative scheme involving…

The study of online algorithms with machine-learned predictions has gained considerable prominence in recent years. One of the common objectives in the design and analysis of such algorithms is to attain (Pareto) optimal tradeoffs between…

Many systems require optimisation over multiple objectives, where objectives are characteristics of the system such as energy consumed or increase in time to perform the work. Optimisation is performed by selecting the `best' set of input…

Automated per-instance algorithm selection and configuration have shown promising performances for a number of classic optimization problems, including satisfiability, AI planning, and TSP. The techniques often rely on a set of features…

We investigate the emergence of approximate neutrino texture structures in the minimal scotogenic model through large-scale Casas--Ibarra parameter scans subject to lepton flavor violation and dark matter constraints. We demonstrate that…

Forecasting the signal discrimination power of dark matter (DM) searches is commonly limited to a set of arbitrary benchmark points. We introduce new methods for benchmark-free forecasting that instead allow an exhaustive exploration and…

The vast majority of work in self-supervised learning, both theoretical and empirical (though mostly the latter), have largely focused on recovering good features for downstream tasks, with the definition of "good" often being intricately…

This paper proposes a general optimization strategy, which combines results from different optimization or parameter estimation methods to overcome shortcomings of a single method. Shotgun optimization is developed as a framework which…

We study the phenomenology of a model that addresses the neutrino mass, dark matter, and generation of the electroweak scale in a single framework. Electroweak symmetry breaking is realized via the Coleman-Weinberg mechanism in a…

Identifying the most representative subset for a close-to-submodular objective while satisfying the predefined partition constraint is a fundamental task with numerous applications in machine learning. However, the existing distorted…

The search for dark matter is one of the main science drivers of the particle and astroparticle physics communities. Determining the nature of dark matter will require a broad approach, with a range of experiments pursuing different…