Exploring New Physics with Deep Learning in the Era of Collider and Gravitational Wave Experiments

Dear Colleagues,

Despite the tremendous success of the Standard Model (SM), it is unquestionable that there is overwhelming phenomenological evidence that strongly suggests the need for a more complete theory. The discovery of neutrino flavor oscillations implies a quantum superposition of mass eigenstates, revealing a caveat of the SM where neutrinos are purely massless. Therefore, any complete, anomaly free, beyond the SM (BSM) framework needs to account for, at least, three right-handed neutrino families. The existence of DM, from galaxy rotation curves and anisotropies in the Cosmic Microwave Background, is becoming increasingly favored by a particle physics explanation that the SM cannot offer. Furthermore, unprecedentedly high energy proton collisions in the CMS and ATLAS experiments at the CERN Large Hadron Collider (LHC) marked a milestone in particle physics—the discovery of the first fundamental scalar particle in Nature—the Higgs boson. While the properties of the SM are remarkably well measured, a common explanation for its flavor and gauge structures is lacking. Last but not least, the recent observation of gravitational waves (GW) opened up a new channel for particle physics. Models with extended scalar sectors feature the possibility of first-order phase transitions capable of generating a stochastic background of GW which, if detected, can become a gravitational portal and a probe for BSM physics, in combination with collider experiments.

Artificial Neural Network (ANN) and, by extension, Deep Learning (DL) algorithms are now becoming a crucial component of the backbone of the particle physics analysis framework. DL and evolutionary algorithms can be applied to analyze and constrain BSM models and search for hints of new physics. A remarkable feature exhibited by these algorithms is the ability of identifying patterns in hyper-dimensional spaces and finding patterns often not noticeable by humans. Furthermore, they are capable of predicting with a high degree of confidence whether a given event comes from BSM interactions.

In this Special Issue, we will address outstanding questions such as I) what is the origin of the SM structure, II) which new physics and which smoking-gun signatures are expected, III) what are the Early Universe implications, and IV) how can state-of-the-art Artificial Intelligence techniques be useful to address these challenges?

Dr. António Pestana Morais
Dr. Roman Pasechnik
Dr. Felipe Ferreira de Freitas
Guest Editor

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• Beyond the Standard Model
• Gravitational Waves
• Dark Matter
• Collider Phenomenology
• Deep Learning