10 papers
Two Flavon Froggatt-Nielsen Models with Genetic Algorithms
Miguel Crispim Romão, Stephen F. King
We present the first systematic and comprehensive scan of two-flavon Froggatt-Nielsen (FN) models, employing artificial intelligence techniques to explore the high-dimensional, mix…
BSMArt 2: simpler and faster parameter space scans
Fernando Abreu de Souza, Nuno Filipe Castro, Miguel Crispim Romão +3
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 im…
Machine Learning insights on the Z3 3HDM with Dark Matter
Fernando Abreu de Souza, Rafael Boto, Miguel Crispim Romão +2
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…
Dark Classification Matters: Searching for Primordial Black Holes with LSST
Miguel Crispim Romao, Djuna Croon, Benedict Crossey +1
We present projected constraints on the abundance of primordial black holes (PBHs) as a constituent of dark matter, based on microlensing observations from the upcoming Legacy Surv…
Symbolic Regression and Differentiable Fits in Beyond the Standard Model Physics
Shehu AbdusSalam, Steven Abel, Deaglan Bartlett +1
We demonstrate the efficacy of symbolic regression (SR) to probe models of particle physics Beyond the Standard Model (BSM), by considering the so-called Constrained Minimal Supers…
Unearthing large pseudoscalar Yukawa couplings with Machine Learning
Fernando Abreu de Souza, Rafael Boto, Miguel Crispim Romão +3
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 fram…