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Bernal, N., Donini, A., Folgado, M. G., & Rius, N. (2021). FIMP Dark Matter in Clockwork/Linear Dilaton extra-dimensions. J. High Energy Phys., 04(4), 061–29pp.
Abstract: We study the possibility that Dark Matter (DM) is made of Feebly Interacting Massive Particles (FIMP) interacting just gravitationally with the Standard Model particles in the framework of a Clockwork/Linear Dilaton (CW/LD) model. We restrict here to the case in which the DM particles are scalar fields. This paper extends our previous study of FIMP's in Randall-Sundrum (RS) warped extra-dimensions. As it was the case in the RS scenario, also in the CW/LD model we find a significant region of the parameter space in which the observed DM relic abundance can be reproduced with scalar DM mass in the MeV range, with a reheating temperature varying from 10 GeV to 10(9) GeV. We comment on the similarities of the results in both extra-dimensional models.
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Escrihuela, F. J., Flores, L. J., Miranda, O. G., & Rendon, J. (2021). Global constraints on neutral-current generalized neutrino interactions. J. High Energy Phys., 07(7), 061–26pp.
Abstract: We study generalized neutrino interactions (GNI) for several neutrino processes, including neutrinos from electron-positron collisions, neutrino-electron scattering, and neutrino deep inelastic scattering. We constrain scalar, pseudoscalar, and tensor new physics effective couplings, based on the standard model effective field theory at low energies. We have performed a global analysis for the different effective couplings. We also present the different individual constraints for each effective parameter (scalar, pseudoscalar, and tensor). Being a global analysis, we show robust results for the restrictions on the different GNI parameters and improve some of these bounds.
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Villaescusa-Navarro, F. et al, & Villanueva-Domingo, P. (2022). The CAMELS Multifield Data Set: Learning the Universe's Fundamental Parameters with Artificial Intelligence. Astrophys. J. Suppl. Ser., 259(2), 61–14pp.
Abstract: We present the Cosmology and Astrophysics with Machine Learning Simulations (CAMELS) Multifield Data set (CMD), a collection of hundreds of thousands of 2D maps and 3D grids containing many different properties of cosmic gas, dark matter, and stars from more than 2000 distinct simulated universes at several cosmic times. The 2D maps and 3D grids represent cosmic regions that span similar to 100 million light-years and have been generated from thousands of state-of-the-art hydrodynamic and gravity-only N-body simulations from the CAMELS project. Designed to train machine-learning models, CMD is the largest data set of its kind containing more than 70 TB of data. In this paper we describe CMD in detail and outline a few of its applications. We focus our attention on one such task, parameter inference, formulating the problems we face as a challenge to the community. We release all data and provide further technical details at https://camels-multifield-dataset.readthedocs.io.
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NEXT Collaboration(Renner, J. et al), Alvarez, V., Carcel, S., Cervera-Villanueva, A., Diaz, J., Ferrario, P., et al. (2015). Ionization and scintillation of nuclear recoils in gaseous xenon. Nucl. Instrum. Methods Phys. Res. A, 793, 62–74.
Abstract: Ionization and scintillation produced by nuclear recoils in gaseous xenon at approximately 14 bar have been simultaneously observed in an electroluminescent time projection chamber. Neutrons from radioisotope a-Be neutron sources were used to induce xenon nuclear recoils, and the observed recoil spectra were compared to a detailed Monte Carlo employing estimated ionization and scintillation yields for nuclear recoils. The ability to discriminate between electronic and nuclear recoils using the ratio of ionization to primary scintillation is demonstrated. These results encourage further investigation on the use of xenon in the gas phase as a detector medium in dark matter direct detection experiments.
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ANTARES Collaboration(Adrian-Martinez, S. et al), Barrios-Marti, J., Hernandez-Rey, J. J., Lambard, G., Mangano, S., Sanchez-Losa, A., et al. (2016). Optical and X-ray early follow-up of ANTARES neutrino alerts. J. Cosmol. Astropart. Phys., 02(2), 062–29pp.
Abstract: High-energy neutrinos could be produced in the interaction of charged cosmic rays with matter or radiation surrounding astrophysical sources. Even with the recent detection of extraterrestrial high-energy neutrinos by the IceCube experiment, no astrophysical neutrino source has yet been discovered. Transient sources, such as gamma-ray bursts, core-collapse supernovae, or active galactic nuclei are promising candidates. Multi-messenger programs offer a unique opportunity to detect these transient sources. By combining the information provided by the ANTARES neutrino telescope with information coming from other observatories, the probability of detecting a source is enhanced, allowing the possibility of identifying a neutrino progenitor from a single detected event. A method based on optical and X-ray follow-ups of high-energy neutrino alerts has been developed within the ANTARES collaboration. This method does not require any assumptions on the relation between neutrino and photon spectra other than time-correlation. This program, denoted as TAToO, triggers a network of robotic optical telescopes (TAROT and ROTSE) and the Swift-XRT with a delay of only a few seconds after a neutrino detection, and is therefore well-suited to search for fast transient sources. To identify an optical or Xray counterpart to a neutrino signal, the images provided by the follow-up observations are analysed with dedicated pipelines. A total of 42 alerts with optical and 7 alerts with Xray images taken with a maximum delay of 24 hours after the neutrino trigger have been analysed. No optical or X-ray counterparts associated to the neutrino triggers have been found, and upper limits on transient source magnitudes have been derived. The probability to reject the gamma-ray burst origin hypothesis has been computed for each alert.
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Chala, M., Krause, C., & Nardini, G. (2018). Signals of the electroweak phase transition at colliders and gravitational wave observatories. J. High Energy Phys., 07(7), 062–29pp.
Abstract: If the electroweak phase transition (EWPT) is of strongly first order due to higher dimensional operators, the scale of new physics generating them is at the TeV scale or below. In this case the effective-field theory (EFT) neglecting operators of dimension higher than six may overlook terms that are relevant for the EWPT analysis. In this article we study the EWPT in the EFT to dimension eight. We estimate the reach of the future gravitational wave observatory LISA for probing the region in which the EWPT is strongly first order and compare it with the capabilities of the Higgs measurements via double-Higgs production at current and future colliders. We also match different UV models to the previously mentioned dimension-eight EFT and demonstrate that, from the top-down point of view, the double-Higgs production is not the best signal to explore these scenarios.
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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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Vento, V., & Traini, M. (2020). Scattering of charged particles off monopole-anti-monopole pairs. Eur. Phys. J. C, 80(1), 62–10pp.
Abstract: The Large Hadron Collider is reaching energies never achieved before allowing the search for exotic particles in the TeV mass range. In a continuing effort to find monopoles we discuss the effect of the magnetic dipole field created by a pair of monopole-anti-monopole or monopolium on the successive bunches of charged particles in the beam at LHC.
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ATLAS Collaboration(Aad, G. et al), Aparisi Pozo, J. A., Bailey, A. J., Cabrera Urban, S., Castillo, F. L., Castillo Gimenez, V., et al. (2020). Search for new phenomena in final states with large jet multiplicities and missing transverse momentum using root s=13 TeV proton-proton collisions recorded by ATLAS in Run 2 of the LHC. J. High Energy Phys., 10(10), 062–53pp.
Abstract: Results of a search for new particles decaying into eight or more jets and moderate missing transverse momentum are presented. The analysis uses 139 fb(-1) of proton-proton collision data at <mml:msqrt>s</mml:msqrt> = 13 TeV collected by the ATLAS experiment at the Large Hadron Collider between 2015 and 2018. The selection rejects events containing isolated electrons or muons, and makes requirements according to the number of b-tagged jets and the scalar sum of masses of large-radius jets. The search extends previous analyses both in using a larger dataset and by employing improved jet and missing transverse momentum reconstruction methods which more cleanly separate signal from background processes. No evidence for physics beyond the Standard Model is found. The results are interpreted in the context of supersymmetry-inspired simplified models, significantly extending the limits on the gluino mass in those models. In particular, limits on the gluino mass are set at 2 TeV when the lightest neutralino is nearly massless in a model assuming a two-step cascade decay via the lightest chargino and second-lightest neutralino.
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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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