4 papers
Quantifying vacuum-like jets in heavy-ion collisions: a Machine Learning study
Miguel Crispim Romão, João Arruda Gonçalves, José Guilherme Milhano
The modification of jets by interaction with the Quark Gluon Plasma has been extensively established through the comparison of observables computed for samples of jets produced in…
Exploring Scotogenic Parameter Spaces and Mapping Uncharted Dark Matter Phenomenology with Multi-Objective Search Algorithms
Fernando Abreu de Souza, Nuno Filipe Castro, Miguel Crispim Romão +1
We present a novel artificial intelligence approach to explore beyond Standard Model parameter spaces by leveraging a multi-objective optimisation algorithm. We apply this methodol…
Combining Evolutionary Strategies and Novelty Detection to go Beyond the Alignment Limit of the 3HDM
Jorge Crispim Romão, Miguel Crispim Romão
We present a novel Artificial Intelligence approach for Beyond the Standard Model parameter space scans by augmenting an Evolutionary Strategy with Novelty Detection. Our approach…
Microlensing signatures of extended dark objects using machine learning
Miguel Crispim Romão, Djuna Croon
This paper presents a machine learning-based method for the detection of the unique gravitational microlensing signatures of extended dark objects, such as boson stars, axion minic…