83 citations · 134 across the 6 of their papers we have counts for
6 papers
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…
Sensitivity to New Physics Phenomena in Anomaly Detection: A Study of Untunable Hyperparameters
Fernando Abreu de Souza, Maura Barros, Nuno Filipe Castro +3
The search for physics beyond the Standard Model (BSM) at collider experiments requires model-independent strategies to avoid missing possible discoveries of unexpected signals. An…
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…
Precision calibration of calorimeter signals in the ATLAS experiment using an uncertainty-aware neural network
ATLAS Collaboration
The ATLAS experiment at the Large Hadron Collider explores the use of modern neural networks for a multi-dimensional calibration of its calorimeter signal defined by clusters of to…
Finding New Physics without learning about it: Anomaly Detection as a tool for Searches at Colliders
M. Crispim Romao, N. F. Castro, R. Pedro
In this paper we propose a new strategy, based on anomaly detection methods, to search for new physics phenomena at colliders independently of the details of such new events. For t…
Transferability of Deep Learning Models in Searches for New Physics at Colliders
M. Crispim Romao, N. F. Castro, R. Pedro +1
In this work we assess the transferability of deep learning models to detect beyond the standard model signals. For this we trained Deep Neural Networks on three different signal m…