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The use of the Audio Spectrogram Transformer (AST) model for gravitational-wave data analysis is investigated. The AST machine-learning model is a convolution-free classifier that captures long-range global dependencies through a purely attention-based mechanism. In this paper a model is applied to a simulated dataset of inspiral gravitational wave signals from binary neutron star coalescences, built from five distinct, cold equations of state (EOS) of nuclear matter. From the analysis of the mass dependence of the tidal deformability parameter for each EOS class it is shown that the AST model achieves a promising performance in correctly classifying the EOS purely from the gravitational wave signals, especially when the component masses of the binary system are in the range $[1,1.5]M_{\odot}$. Furthermore, the generalization ability of the model is investigated by using gravitational-wave signals from a new EOS not used during the training of the model, achieving fairly satisfactory results. Overall, the results, obtained using the simplified setup of noise-free waveforms, show that the AST model, once trained, might allow for the instantaneous inference of the cold nuclear matter EOS directly from the inspiral gravitational-wave signals produced in binary neutron star coalescences., Comment: 11 pages, 11 figures

We demonstrate the application of the YOLOv5 model, a general purpose convolution-based single-shot object detection model, in the task of detecting binary neutron star (BNS) coalescence events from gravitational-wave data of current generation interferometer detectors. We also present a thorough explanation of the synthetic data generation and preparation tasks based on approximant waveform models used for the model training, validation and testing steps. Using this approach, we achieve mean average precision ($\text{mAP}_{[0.50]}$) values of 0.945 for a single class validation dataset and as high as 0.978 for test datasets. Moreover, the trained model is successful in identifying the GW170817 event in the LIGO H1 detector data. The identification of this event is also possible for the LIGO L1 detector data with an additional pre-processing step, without the need of removing the large glitch in the final stages of the inspiral. The detection of the GW190425 event is less successful, which attests to performance degradation with the signal-to-noise ratio. Our study indicates that the YOLOv5 model is an interesting approach for first-stage detection alarm pipelines and, when integrated in more complex pipelines, for real-time inference of physical source parameters., Comment: 11 pages, 9 figures

A minimal extension of the Standard Model (SM) featuring two scalar leptoquarks, an SU(2) doublet with hypercharge 1/6 and a singlet with hypercharge 1/3, is proposed as an economical benchmark model for studies of an interplay between flavour physics and properties of the neutrino sector. The presence of such type of leptoquarks radiatively generates neutrino masses and offers a simultaneous explanation for the current B-physics anomalies involving $b \to c \ell \nu_\ell$ decays. The model can also accommodate both the muon magnetic moment and the recently reported $W$ mass anomalies, while complying with the most stringent lepton flavour violating observables., Comment: Accepted version

In this work, we continue our exploration of TeV-scale vector-like fermion signatures inspired by a Grand Unification scenario based on the trinification gauge group. A particular focus is given to pair-production topologies of vector-like quarks (VLQs) at the LHC, in a multi-jet plus a charged lepton and a missing energy signature. We employ Deep Learning methods and techniques based in evolutive algorithms that optimize hyper-parameters in the neural network construction, whose objective is to maximise the Asimov estimate for distinct VLQ masses. In this article, we consider the implications of an innovative approach by simultaneously combining detector images (also known as jet images) and tabular data containing kinematic information from the final states. With this technique we are able to exclude VLQs, that are specific for the considered model, to up a mass of 800 GeV in both the high-luminosity the Run-III phases of the LHC programme., Comment: 24 pages, 4 tables and 8 figures. Matches version to be published in EPJC

A chiral-symmetric technicolor model successfully reconciles the tension between electroweak precision tests and traditional technicolor models. Focusing on its simplest realization preserving the conventional Higgs mechanism, we study its primordial gravitational wave signatures originating from first order phase transitions in the early Universe. We found that abundant phase transition patterns arise from a physically viable parameter space. Besides, we have also found gravitational wave signals possibly visible by future experiments, such as LISA, BBO and u-DECIGO. Our results stress the importance of gravitational wave detectors in exploring new physics complementary to ground colliders in the multi-messenger astronomy era., Comment: 9 pages, 6 figures, 3 tables; accepted for publication in Phys. Lett. B

Current gravitational wave (GW) detections rely on the existence of libraries of theoretical waveforms. Consequently, finding new physics with GWs requires libraries of non-standard models, which are computationally demanding. We discuss how deep learning frameworks can be used to generate new waveforms "learned" from a simulation dataset obtained, say, from numerical relativity simulations. Concretely, we use the WaveGAN architecture of a generative adversarial network (GAN). As a proof of concept we provide this neural network (NN) with a sample of ($>500$) waveforms from the collisions of exotic compact objects (Proca stars), obtained from numerical relativity simulations. Dividing the sample into a training and a validation set, we show that after a sufficiently large number of training epochs the NN can produce from 12\% to 25\% of the synthetic waveforms with an overlapping match of at least 95\% with the ones from the validation set. We also demonstrate that a NN can be used to predict the overlapping match score, with 90\% of accuracy, of new synthetic samples. These are encouraging results for using GANs for data augmentation and interpolation in the context of GWs, to cover the full parameter space of, say, exotic compact binaries, without the need of intensive numerical relativity simulations., Comment: 20 pages, 13 figures, 4 tables

In this paper, we present several possible anomaly-free implementations of the Branco-Grimus-Lavoura (BGL) model with two Higgs doublets and one singlet scalar. The model also includes three generations of massive neutrinos that get their mass via a type-I seesaw mechanism. A particular anomaly-free realization, which we dub $\nu$BGL-1 scenario, is subjected to an extensive phenomenological analysis, from the perspective of flavour physics and collider phenomenology., Comment: 32 pages, 9 Figures and 7 tables. Matches version to be published in PRD