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Ghosh, P., Lopez-Fogliani, D. E., Mitsou, V. A., Muñoz, C., & Ruiz de Austri, R. (2015). Hunting physics beyond the standard model with unusual W-+/- and Z decays. Phys. Rev. D, 91(3), 035020–8pp.
Abstract: Nonstandard on-shell decays of W-+/- and Z bosons are possible within the framework of extended supersymmetric models, i.e., with singlet states and/or new couplings compared to the minimal supersymmetric standard model. These modes are typically encountered in regions of the parameter space with light singlet-like scalars, pseudoscalars, and neutralinos. In this letter we emphasize how these states can lead to novel signals at colliders from Z- or W-+/--boson decays with prompt or displaced multileptons/tau jets/jets/photons in the final states. These new modes would give distinct evidence of new physics even when direct searches remain unsuccessful. We discuss the possibilities of probing these new signals using the existing LHC run-I data set. We also address the same in the context of the LHC run-II, as well as for the future colliders. We exemplify our observations with the “mu from v” supersymmetric standard model, where three generations of right-handed neutrino superfields are used to solve shortcomings of the minimal supersymmetric standard model. We also extend our discussion for other variants of supersymmetric models that can accommodate similar signatures.
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Bertone, G., Cerdeño, D. G., Fornasa, M., Ruiz de Austri, R., & Trotta, R. (2010). Identification of dark matter particles with LHC and direct detection data. Phys. Rev. D, 82(5), 055008–7pp.
Abstract: Dark matter (DM) is currently searched for with a variety of detection strategies. Accelerator searches are particularly promising, but even if weakly interacting massive particles are found at the Large Hadron Collider (LHC), it will be difficult to prove that they constitute the bulk of the DM in the Universe Omega(DM). We show that a significantly better reconstruction of the DM properties can be obtained with a combined analysis of LHC and direct detection data, by making a simple Ansatz on the weakly interacting massive particles local density rho(0)((chi) over bar1), i.e., by assuming that the local density scales with the cosmological relic abundance, (rho(0)((chi) over bar1)/rho(DM)) = (Omega(0)((chi) over bar1)/Omega(DM)). We demonstrate this method in an explicit example in the context of a 24-parameter supersymmetric model, with a neutralino lightest supersymmetric particle in the stau coannihilation region. Our results show that future ton-scale direct detection experiments will allow to break degeneracies in the supersymmetric parameter space and achieve a significantly better reconstruction of the neutralino composition and its relic density than with LHC data alone.
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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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Bertone, G., Bozorgnia, N., Kim, J. S., Liem, S., McCabe, C., Otten, S., et al. (2018). Identifying WIMP dark matter from particle and astroparticle data. J. Cosmol. Astropart. Phys., 03(3), 026–42pp.
Abstract: One of the most promising strategies to identify the nature of dark matter consists in the search for new particles at accelerators and with so-called direct detection experiments. Working within the framework of simplified models, and making use of machine learning tools to speed up statistical inference, we address the question of what we can learn about dark matter from a detection at the LHC and a forthcoming direct detection experiment. We show that with a combination of accelerator and direct detection data, it is possible to identify newly discovered particles as dark matter, by reconstructing their relic density assuming they are weakly interacting massive particles (WIMPs) thermally produced in the early Universe, and demonstrating that it is consistent with the measured dark matter abundance. An inconsistency between these two quantities would instead point either towards additional physics in the dark sector, or towards a non-standard cosmology, with a thermal history substantially different from that of the standard cosmological model.
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Kpatcha, E., Lopez-Fogliani, D. E., Munoz, C., & Ruiz de Austri, R. (2020). Impact of Higgs physics on the parameter space of the μnu SSM. Eur. Phys. J. C, 80(4), 336–43pp.
Abstract: Given the increasing number of experimental data, together with the precise measurement of the properties of the Higgs boson at the LHC, the parameter space of supersymmetric models starts to be constrained. We carry out a detailed analysis of this issue in the framework of the μnu SSM. In this model, three families of right-handed neutrino superfields are present in order to solve the μproblem and simultaneously reproduce neutrino physics. The new couplings and sneutrino vacuum expectation values in the μnu SSM induce new mixing of states, and, in particular, the three right sneutrinos can be substantially mixed with the neutral Higgses. After diagonalization, the masses of the corresponding three singlet-like eigenstates can be smaller or larger than the mass of the Higgs, or even degenerated with it. We analyze whether these situations are still compatible with the experimental results. To address it we scan the parameter space of the Higgs sector of the model. In particular, we sample the μnu SSM using a powerful likelihood data-driven method, paying special attention to satisfy the constraints coming from Higgs sector measurements/limits (using HiggsBounds and HiggsSignals), as well as a class of flavor observables such as B and μdecays, while muon g-2 is briefly discussed. We find that large regions of the parameter space of the μnu SSM are viable, containing an interesting phenomenology that could be probed at the LHC.
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