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KM3NeT Collaboration(Adrian-Martinez, S. et al), Barrios-Marti, J., Calvo Diaz-Aldagalan, D., Hernandez-Rey, J. J., Illuminati, G., Lotze, M., et al. (2016). Letter of intent for KM3NeT 2.0. J. Phys. G, 43(8), 084001–130pp.
Abstract: The main objectives of the KM3NeT Collaboration are (i) the discovery and subsequent observation of high-energy neutrino sources in the Universe and (ii) the determination of the mass hierarchy of neutrinos. These objectives are strongly motivated by two recent important discoveries, namely: (1) the high-energy astrophysical neutrino signal reported by IceCube and (2) the sizable contribution of electron neutrinos to the third neutrino mass eigenstate as reported by Daya Bay, Reno and others. To meet these objectives, the KM3NeT Collaboration plans to build a new Research Infrastructure consisting of a network of deep-sea neutrino telescopes in the Mediterranean Sea. A phased and distributed implementation is pursued which maximises the access to regional funds, the availability of human resources and the synergistic opportunities for the Earth and sea sciences community. Three suitable deep-sea sites are selected, namely off-shore Toulon (France), Capo Passero (Sicily, Italy) and Pylos (Peloponnese, Greece). The infrastructure will consist of three so-called building blocks. A building block comprises 115 strings, each string comprises 18 optical modules and each optical module comprises 31 photo-multiplier tubes. Each building block thus constitutes a three-dimensional array of photo sensors that can be used to detect the Cherenkov light produced by relativistic particles emerging from neutrino interactions. Two building blocks will be sparsely configured to fully explore the IceCube signal with similar instrumented volume, different methodology, improved resolution and complementary field of view, including the galactic plane. One building block will be densely configured to precisely measure atmospheric neutrino oscillations.
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Caballero, L., Albiol, F., Corbi Bellot, A., Domingo-Pardo, C., Leganes Nieto, J. L., Agramunt Ros, J., et al. (2018). Gamma-ray imaging system for real-time measurements in nuclear waste characterisation. J. Instrum., 13, P03016–23pp.
Abstract: Acompact, portable and large field-of-viewgamma camera that is able to identify, locate and quantify gamma-ray emitting radioisotopes in real-time has been developed. The device delivers spectroscopic and imaging capabilities that enable its use it in a variety of nuclear waste characterisation scenarios, such as radioactivity monitoring in nuclear power plants and more specifically for the decommissioning of nuclear facilities. The technical development of this apparatus and some examples of its application in field measurements are reported in this article. The performance of the presented gamma-camera is also benchmarked against other conventional techniques.
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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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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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Horvat, S., Magas, V. K., Strottman, D. D., & Csernai, L. P. (2010). Entropy development in ideal relativistic fluid dynamics with the Bag Model equation of state. Phys. Lett. B, 692(4), 277–280.
Abstract: We consider an idealized situation where the Quark-Gluon Plasma (QGP) is described by a perfect, (3 + 1)-dimensional fluid dynamic model starting from an initial state and expanding until a final state where freeze-out and/or hadronization takes place. We study the entropy production with attention to effects of (i) numerical viscosity, (ii) late stages of flow where the Bag Constant and the partonic pressure are becoming similar, (iii) and the consequences of final freeze-out and constituent quark matter formation.
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