arXiv subjects

Fernando Abreu de Souza

Publications and source records attributed to Fernando Abreu de Souza.

7 recordsLinked to original sources

arXiv2026

Machine Learning insights on the Z3 3HDM with Dark Matter

We study a 3-Higgs Doublet Model (3HDM) with an imposed Z3 symmetry, allowing for two Inert scalar doublets and one active Higgs doublet. The WIMP dark matter candidates correspond to two mass-degenerate states, H1 and A1, which possess opposite CP quantum numbers and can reproduce the correct relic density simultaneously with all theoretical and experimental constraints. We use state-of-the-art machine learning algorithms to probe the parameter space of the model by employing an Evolutionary Strategy augmented with Novelty Reward. We consider the multiple solutions tothe condition that guarantees the vertex Z Hi Ai ~ cos(2θ) to vanish, closing a channel that would otherwise be at odds with current direct detection bounds. For scenarios with θ = π/4, we find viable dark matter candidates within two separate mass regimes, ranging from 50 GeV < mDM < mW and 386 < mDM < 1000 GeV. The other possible solution considered only finds viable solutions in the low DM mass region. Moreover, we find it is possible to fulfill all the existing constraints while still obtaining values for the dark matter-higgs coupling of order ~ O(0.1). Exploring the model proved to be a challenging task, as there are regions which seem easy to explore when projected on a 2D plane, yet may be completely disconnected on the hypersurface supporting the valid points, given its non-convex and multi-dimensional nature. We consider new methods of prototype selection to seed new exploration runs, which allow for efficient global scans over the parameter space.

hep-ph↗

arXiv2026

BSMArt 2: simpler and faster parameter space scans

We present version 2 of BSMArt, a powerful yet lightweight scanning tool designed to simplify the exploration of parameter spaces of new physics models. Aside from architectural improvements, simpler installation and expanded documentation with examples, the new version includes additional tools and new machine learning and Monte Carlo scanning algorithms: Affine MC, Contour Finding, MLScanner, DLScanner, MLS, and CMA-ES. We showcase two variants of CMA-ES scans with physics applications relevant for soft lepton excesses at the LHC. We demonstrate that it is easily possible to find diverse and interesting parameter points for future testing.

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arXiv2025

Exploring Scotogenic Parameter Spaces and Mapping Uncharted Dark Matter Phenomenology with Multi-Objective Search Algorithms

We present a novel artificial intelligence approach to explore beyond Standard Model parameter spaces by leveraging a multi-objective optimisation algorithm. We apply this methodology to a non-minimal scotogenic model which is constrained by Higgs mass, anomalous magnetic moment of the muon, dark matter relic density, dark matter direct detection, neutrino masses and mixing, and lepton flavour violating processes. Our results successfully expand on the phenomenological realisations presented in previous work. We compare between multi- and single-objective algorithms and we observe more phenomenologically diverse solutions and an improved search capacity coming from the former. We use novelty detection to further explore sparsely populated regions of phenomenological interest. These results suggest a powerful search strategy that combines the global exploration of multi-objective optimisation with the exploitation of single-objective optimisation.

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arXiv2025

Unearthing large pseudoscalar Yukawa couplings with Machine Learning

With the Large Hadron Collider's Run 3 in progress, the 125 GeV Higgs boson couplings are being examined in greater detail, while searching for additional scalars. Multi-Higgs frameworks allow Higgs couplings to significantly deviate from Standard Model values, enabling indirect probes of extra scalars. We consider the possibility of large pseudoscalar Yukawa couplings in the softly-broken Z2xZ2' three-Higgs doublet model with CP violating coefficients. To explore the parameter space of the model, we employ a Machine Learning algorithm that significantly enhances sampling efficiency. Using it, we find new regions of parameter space and observable consequences, not found with previous techniques. This method leverages an Evolutionary Strategy to quickly converge towards valid regions with an additional Novelty Reward mechanism. We use this model as a prototype to illustrate the potential of the new techniques, applicable to any Physics Beyond the Standard Model scenario.

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arXiv2025

Sensitivity to New Physics Phenomena in Anomaly Detection: A Study of Untunable Hyperparameters

The search for physics beyond the Standard Model (BSM) at collider experiments requires model-independent strategies to avoid missing possible discoveries of unexpected signals. Anomaly detection (AD) techniques offer a promising approach by identifying deviations from the Standard Model (SM) and have been extensively studied. The sensitivity of these methods to untunable hyperparameters has not been systematically compared, however. This study addresses it by investigating four semi-supervised AD methods -- Auto-Encoders, Deep Support Vector Data Description, Histogram-based Outlier Score, and Isolation Forest -- trained on simulated SM background events. In this paper, we study the sensitivity of these methods to BSM benchmark signals as a function of these untunable hyperparameters. Such a study is complemented by a proposal of a non-parametric permutation test using signal-agnostic statistics, which can provide a robust statistical assessment.

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arXiv2022

Exploring Parameter Spaces with Artificial Intelligence and Machine Learning Black-Box Optimisation Algorithms

Constraining Beyond the Standard Model theories usually involves scanning highly multi-dimensional parameter spaces and check observable predictions against experimental bounds and theoretical constraints. Such task is often timely and computationally expensive, especially when the model is severely constrained and thus leading to very low random sampling efficiency. In this work we tackled this challenge using Artificial Intelligence and Machine Learning search algorithms used for Black-Box optimisation problems. Using the cMSSM and the pMSSM parameter spaces, we consider both the Higgs mass and the Dark Matter Relic Density constraints to study their sampling efficiency and parameter space coverage. We find our methodology to produce orders of magnitude improvement of sampling efficiency whilst reasonably covering the parameter space.

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arXiv2019

A Random Clockwork of Flavor

We propose a simple clockwork model of flavor which successfully generates the Standard Model flavor hierarchies from random order-one couplings. With very few parameters we achieve distributions of models in excellent agreement with observation. We explain in some detail the interpretation of our mechanism as random localization of zero modes in theory space. The scale of the vectorlike fermions is mostly constrained by lepton flavor violation with secondary constraints arising from rare meson decays.

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