Related papers: Exploring Scotogenic Parameter Spaces and Mapping …
Evolutionary search is well suited for large-scale biophysical brain modeling, where many parameters with nonlinear interactions and no tractable gradients need to be optimized. Standard evolutionary approaches achieve an excellent fit to…
Stochastic optimizers play a crucial role in the successful training of deep neural network models. To achieve optimal model performance, designers must carefully select both model and optimizer hyperparameters. However, this process is…
Efficiently solving multi-objective optimization problems for simulation optimization of important scientific and engineering applications such as materials design is becoming an increasingly important research topic. This is due largely to…
We consider the possibility that neutrino masses arise from the exchange of dark matter states. We examine in detail the phenomenology of fermionic dark matter in the singlet-triplet scotogenic reference model. We explore the case of…
Software quality estimation is a challenging and time-consuming activity, and models are crucial to face the complexity of such activity on modern software applications. In this context, software refactoring is a crucial activity within…
The precision era of multi-messenger astronomy, together with modern astrophysical, cosmological, and gravitational wave observations, increasingly points toward the existence of a ``dark" sector that cannot be explained within the…
Multimodality is one of the biggest difficulties for optimization as local optima are often preventing algorithms from making progress. This does not only challenge local strategies that can get stuck. It also hinders meta-heuristics like…
Novelty search (NS) refers to a class of exploration algorithms that seek to uncover diverse system behaviors through simulations or experiments. Such diversity is central to many AI-driven discovery and design tasks, including material and…
In this work, we investigate a neutrinophilic low-mass dark matter model mediated by a pseudoscalar particle. Since dark matter lacks Standard Model gauge charges, new interactions are required to connect it to the visible sector.…
Direct-detection searches for axions and hidden photons are playing an increasingly prominent role in the search for dark matter. In this work, we derive the properties of optimal electromagnetic searches for these candidates, subject to…
We study Dark Matter (DM) phenomenology with multiple DM species consisting of both scalar and vector DM particles. More specifically, we study the Hidden Gauged SU(3) model of Arcadi {\it et al}. Before proceeding to the Hidden Gauged…
We study the problem of selecting most informative subset of a large observation set to enable accurate estimation of unknown parameters. This problem arises in a variety of settings in machine learning and signal processing including…
I discuss recent progress in dark matter searches, focusing in particular on how rigorous modeling the dark matter distribution in the Galaxy and in its satellite galaxies improves our interpretation of the limits on the annihilation and…
We report on a novel methodology for extracting material parameters from spectroscopic optical data using a physics-based neural network. The proposed model integrates classical optimization frameworks with a multi-scale object detection…
The Scotogenic model extends the standard model with three singlet fermion $N_i$ and one inert doublet scalar $\eta$ to address the common origin of tiny neutrino mass and dark matter. For fermion dark matter $N_1$, a hierarchical Yukawa…
The performance of any Machine Learning (ML) algorithm is impacted by the choice of its hyperparameters. As training and evaluating a ML algorithm is usually expensive, the hyperparameter optimization (HPO) method needs to be…
In this paper, based on the limited memory techniques and subspace minimization conjugate gradient (SMCG) methods, a regularized limited memory subspace minimization conjugate gradient method is proposed, which contains two types of…
We review theoretical frameworks in which small neutrino masses arise radiatively through interactions with a dark sector that also accounts for cosmological dark matter (DM). A prototype is provided by scotogenic schemes, that extend the…
We explore a novel flavor structure in the interactions of dark matter with the Standard Model. We consider theories in which both the dark matter candidate, and the particles that mediate its interactions with the Standard Model fields,…
Hyperparameter optimization (HPO) is a necessary step to ensure the best possible performance of Machine Learning (ML) algorithms. Several methods have been developed to perform HPO; most of these are focused on optimizing one performance…