Kim, J. S., Lopez-Fogliani, D. E., Perez, A. D., & Ruiz de Austri, R. (2023). Right-handed sneutrino and gravitino multicomponent dark matter in light of neutrino detectors. J. Cosmol. Astropart. Phys., 04(4), 050–32pp.
Abstract: We investigate the possibility that right-handed (RH) sneutrinos and gravitinos can coexist and explain the dark matter (DM) problem. We compare extensions of the minimal supersymmetric standard model (MSSM) and the next-to-MSSM (NMSSM) adding RH neutrinos superfields, with special emphasis on the latter. If the gravitino is the lightest supersymmetric particle (LSP) and the RH sneutrino the next-to-LSP (NLSP), the heavier particle decays to the former plus left-handed (LH) neutrinos through the mixing between the scalar partners of the LH and RH neutrinos. However, the interaction is suppressed by the Planck mass, and if the LH-RH sneutrino mixing parameter is small, << O(10-2), a long-lived RH sneutrino NLSP is possible even surpassing the age of the Universe. As a byproduct, the NLSP to LSP decay produces monochromatic neutrinos in the ballpark of current and planned neutrino telescopes like Super-Kamiokande, IceCube and Antares that we use to set constraints and show prospects of detection. In the NMSSM+RHN, assuming a gluino mass parameter M3 = 3 TeV we found the following lower limits for the gravitino mass m3/2 >= 1-600 GeV and the reheating temperature TR >= 105-3 x 107 GeV, for m nu similar to R similar to 10-800 GeV. If we take M3 = 10 TeV, then the limits on TR are relaxed by one order of magnitude.
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Cabrera, M. E., Casas, J. A., Delgado, A., Robles, S., & Ruiz de Austri, R. (2016). Naturalness of MSSM dark matter. J. High Energy Phys., 08(8), 058–30pp.
Abstract: There exists a vast literature examining the electroweak (EW) fine-tuning problem in supersymmetric scenarios, but little concerned with the dark matter (DM) one, which should be combined with the former. In this paper, we study this problem in an, as much as possible, exhaustive and rigorous way. We have considered the MSSM framework, assuming that the LSP is the lightest neutralino, chi(0)(1), and exploring the various possibilities for the mass and composition of chi(0)(1), as well as different mechanisms for annihilation of the DM particles in the early Universe (well-tempered neutralinos, funnels and co-annihilation scenarios). We also present a discussion about the statistical meaning of the fine-tuning and how it should be computed for the DM abundance, and combined with the EW fine-tuning. The results are very robust and model-independent and favour some scenarios (like the h-funnel when M-chi 10 is not too close to m(h)/2) with respect to others (such as the pure wino case). These features should be taken into account when one explores “natural SUSY” scenarios and their possible signatures at the LHC and in DM detection experiments.
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Caron, S., Gomez-Vargas, G. A., Hendriks, L., & Ruiz de Austri, R. (2018). Analyzing gamma rays of the Galactic Center with deep learning. J. Cosmol. Astropart. Phys., 05(5), 058–24pp.
Abstract: We present the application of convolutional neural networks to a particular problem in gamma ray astronomy. Explicitly, we use this method to investigate the origin of an excess emission of GeV gamma rays in the direction of the Galactic Center, reported by several groups by analyzing Fermi-LAT data. Interpretations of this excess include gamma rays created by the annihilation of dark matter particles and gamma rays originating from a collection of unresolved point sources, such as millisecond pulsars. We train and test convolutional neural networks with simulated Fermi-LAT images based on point and diffuse emission models of the Galactic Center tuned to measured gamma ray data. Our new method allows precise measurements of the contribution and properties of an unresolved population of gamma ray point sources in the interstellar diffuse emission model. The current model predicts the fraction of unresolved point sources with an error of up to 10% and this is expected to decrease with future work.
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Gomez, M. E., Lola, S., Ruiz de Austri, R., & Shafi, Q. (2018). Dark matter, sparticle spectroscopy and muon (g-2) in SU(4)(c) x SU(2)(L) x SU(2)(R). J. High Energy Phys., 10(10), 062–24pp.
Abstract: We explore the sparticle mass spectra including LSP dark matter within the framework of supersymmetric SU(4)(c) x SU(2)(L) x SU(2)(R) (422) models, taking into account the constraints from extensive LHC and cold dark matter searches. The soft supersymmetry-breaking parameters at M-GUT can be non-universal, but consistent with the 422 symmetry. We identify a variety of coannihilation scenarios compatible with LSP dark matter, and study the implications for future supersymmetry searches and the ongoing muon g-2 experiment.
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Panes, B., Eckner, C., Hendriks, L., Caron, S., Dijkstra, K., Johannesson, G., et al. (2021). Identification of point sources in gamma rays using U-shaped convolutional neural networks and a data challenge. Astron. Astrophys., 656, A62–18pp.
Abstract: Context. At GeV energies, the sky is dominated by the interstellar emission from the Galaxy. With limited statistics and spatial resolution, accurately separating point sources is therefore challenging. Aims. Here we present the first application of deep learning based algorithms to automatically detect and classify point sources from gamma-ray data. For concreteness we refer to this approach as AutoSourceID. Methods. To detect point sources, we utilized U-shaped convolutional networks for image segmentation and k-means for source clustering and localization. We also explored the Centroid-Net algorithm, which is designed to find and count objects. Using two algorithms allows for a cross check of the results, while a combination of their results can be used to improve performance. The training data are based on 9.5 years of exposure from The Fermi Large Area Telescope (Fermi-LAT) and we used source properties of active galactic nuclei (AGNs) and pulsars (PSRs) from the fourth Fermi-LAT source catalog in addition to several models of background interstellar emission. The results of the localization algorithm are fed into a classification neural network that is trained to separate the three general source classes (AGNs, PSRs, and FAKE sources). Results. We compared our localization algorithms qualitatively with traditional methods and find them to have similar detection thresholds. We also demonstrate the robustness of our source localization algorithms to modifications in the interstellar emission models, which presents a clear advantage over traditional methods. The classification network is able to discriminate between the three classes with typical accuracy of similar to 70%, as long as balanced data sets are used in classification training. We published online our training data sets and analysis scripts and invite the community to join the data challenge aimed to improve the localization and classification of gamma-ray point sources.
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