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Bhattacharya, S., Mondal, N., Roshan, R., & Vatsyayan, D. (2024). Leptogenesis, dark matter and gravitational waves from discrete symmetry breaking. J. Cosmol. Astropart. Phys., 06(6), 029–25pp.
Abstract: We analyse a model that connects the neutrino sector and the dark sector of the universe via a mediator 41., stabilised by a discrete Z4 symmetry that breaks to a remnant Z2 upon 41. acquiring a non -zero vacuum expectation value (v phi). The model accounts for the observed baryon asymmetry of the universe via additional contributions to the canonical Type -I leptogenesis. The Z4 symmetry breaking scale (v phi) in the model not only establishes a connection between the neutrino sector and the dark sector, but could also lead to gravitational wave signals that are within the reach of current and future experimental sensitivities.
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Villanueva-Domingo, P., Villaescusa-Navarro, F., Angles-Alcazar, D., Genel, S., Marinacci, F., Spergel, D. N., et al. (2022). Inferring Halo Masses with Graph Neural Networks. Astrophys. J., 935(1), 30–15pp.
Abstract: Understanding the halo-galaxy connection is fundamental in order to improve our knowledge on the nature and properties of dark matter. In this work, we build a model that infers the mass of a halo given the positions, velocities, stellar masses, and radii of the galaxies it hosts. In order to capture information from correlations among galaxy properties and their phase space, we use Graph Neural Networks (GNNs), which are designed to work with irregular and sparse data. We train our models on galaxies from more than 2000 state-of-the-art simulations from the Cosmology and Astrophysics with MachinE Learning Simulations project. Our model, which accounts for cosmological and astrophysical uncertainties, is able to constrain the masses of the halos with a similar to 0.2 dex accuracy. Furthermore, a GNN trained on a suite of simulations is able to preserve part of its accuracy when tested on simulations run with a different code that utilizes a distinct subgrid physics model, showing the robustness of our method. The PyTorch Geometric implementation of the GNN is publicly available on GitHub (https://github.com/PabloVD/HaloGraphNet).
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Herrero-Garcia, J., Landini, G., & Vatsyayan, D. (2023). Asymmetries in extended dark sectors: a cogenesis scenario. J. High Energy Phys., 05(5), 049–41pp.
Abstract: The observed dark matter relic abundance may be explained by different mechanisms, such as thermal freeze-out/freeze-in, with one or more symmetric/asymmetric components. In this work we investigate the role played by asymmetries in determining the yield and nature of dark matter in non-minimal scenarios with more than one dark matter particle. In particular, we show that the energy density of a particle may come from an asymmetry, even if the particle is asymptotically symmetric by nature. To illustrate the different effects of asymmetries, we adopt a model with two dark matter components. We embed it in a multi-component cogenesis scenario that is also able to reproduce neutrino masses and the baryon asymmetry. In some cases, the model predicts an interesting monochromatic neutrino line that may be searched for at neutrino telescopes.
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Jordan, D., Algora, A., & Tain, J. L. (2016). An event generator for simulations of complex beta-decay experiments. Nucl. Instrum. Methods Phys. Res. A, 828, 52–57.
Abstract: This article describes a Monte Carlo event generator for the design, optimization and performance characterization of beta decay spectroscopy experimental set-ups. The event generator has been developed within the Geant4 simulation architecture and provides new features and greater flexibility in comparison with the current available decay generator.
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KM3NeT Collaboration(Adrian-Martinez, S. et al), Barrios-Marti, J., Calvo Diaz-Aldagalan, D., Hernandez-Rey, J. J., Real, D., Zornoza, J. D., et al. (2016). The prototype detection unit of the KM3NeT detector. Eur. Phys. J. C, 76(2), 54–12pp.
Abstract: A prototype detection unit of the KM3NeT deep-sea neutrino telescope has been installed at 3500m depth 80 km offshore the Italian coast. KM3NeT in its final configuration will contain several hundreds of detection units. Each detection unit is a mechanical structure anchored to the sea floor, held vertical by a submerged buoy and supporting optical modules for the detection of Cherenkov light emitted by charged secondary particles emerging from neutrino interactions. This prototype string implements three optical modules with 31 photomultiplier tubes each. These optical modules were developed by the KM3NeT Collaboration to enhance the detection capability of neutrino interactions. The prototype detection unit was operated since its deployment in May 2014 until its decommissioning in July 2015. Reconstruction of the particle trajectories from the data requires a nanosecond accuracy in the time calibration. A procedure for relative time calibration of the photomultiplier tubes contained in each optical module is described. This procedure is based on the measured coincidences produced in the sea by the K background light and can easily be expanded to a detector with several thousands of optical modules. The time offsets between the different optical modules are obtained using LED nanobeacons mounted inside them. A set of data corresponding to 600 h of livetime was analysed. The results show good agreement with Monte Carlo simulations of the expected optical background and the signal from atmospheric muons. An almost background-free sample of muons was selected by filtering the time correlated signals on all the three optical modules. The zenith angle of the selected muons was reconstructed with a precision of about 3 degrees.
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