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BABAR Collaboration(Aubert, B. et al), Azzolini, V., Lopez-March, N., Martinez-Vidal, F., Milanes, D. A., & Oyanguren, A. (2013). The BABAR detector: Upgrades, operation and performance. Nucl. Instrum. Methods Phys. Res. A, 729, 615–701.
Abstract: The BABAR detector operated successfully at the PEP-Il asymmetric e(+) e(-) collider at the SLAC National Accelerator Laboratory from 1999 to 2008. This report covers upgrades, operation, and performance of the collider and the detector systems, as well as the trigger, online and offline computing, and aspects of event reconstruction since the beginning of data taking.
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ANTARES Collaboration(Bhandari, S. et al), Barrios-Marti, J., Coleiro, A., Hernandez-Rey, J. J., Illuminati, G., Tönnis, C., et al. (2018). The SUrvey for Pulsars and Extragalactic Radio Bursts – II. New FRB discoveries and their follow-up. Mon. Not. Roy. Astron. Soc., 475(2), 1427–1446.
Abstract: We report the discovery of four Fast Radio Bursts (FRBs) in the ongoing SUrvey for Pulsars and Extragalactic Radio Bursts at the Parkes Radio Telescope: FRBs 150610, 151206, 151230 and 160102. Our real-time discoveries have enabled us to conduct extensive, rapid multimessenger follow-up at 12 major facilities sensitive to radio, optical, X-ray, gamma-ray photons and neutrinos on time-scales ranging from an hour to a few months post-burst. No counterparts to the FRBs were found and we provide upper limits on afterglow luminosities. None of the FRBs were seen to repeat. Formal fits to all FRBs show hints of scattering while their intrinsic widths are unresolved in time. FRB 151206 is at low Galactic latitude, FRB 151230 shows a sharp spectral cut-off, and FRB 160102 has the highest dispersion measure (DM = 2596.1 +/- 0.3 pc cm(-3)) detected to date. Three of the FRBs have high dispersion measures (DM > 1500 pc cm(-3)), favouring a scenario where the DMis dominated by contributions from the intergalactic medium. The slope of the Parkes FRB source counts distribution with fluences > 2 Jy ms is alpha = – 2.2(-1.2)(+0.6) and still consistent with a Euclidean distribution (alpha = -3/2). We also find that the all-sky rate is 1.7(-0.9)(+1.5) x 10(3)FRBs/(4 pi sr)/day above similar to 2 Jy ms and there is currently no strong evidence for a latitude- dependent FRB sky rate.
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ANTARES Collaboration(Adrian-Martinez, S. et al), Barrios-Marti, J., Hernandez-Rey, J. J., Sanchez-Losa, A., Tönnis, C., Zornoza, J. D., et al. (2016). Murchison Widefield Array Limits on Radio Emission from ANTARES Neutrino Events. Astrophys. J. Lett., 820(2), L24–7pp.
Abstract: We present a search, using the Murchison Widefield Array (MWA), for electromagnetic (EM) counterparts to two candidate high-energy neutrino events detected by the ANTARES neutrino telescope in 2013 November and 2014 March. These events were selected by ANTARES because they are consistent, within 0 degrees.4, with the locations of galaxies within 20 Mpc of Earth. Using MWA archival data at frequencies between 118 and 182 MHz, taken similar to 20. days prior to, at the same time as, and up to a year after the neutrino triggers, we look for transient or strongly variable radio sources that are consistent with the neutrino positions. No such counterparts are detected, and we set a 5 sigma upper limit for low-frequency radio emission of similar to 10(37) erg s(-1) for progenitors at 20 Mpc. If the neutrino sources are instead not in nearby galaxies, but originate in binary neutron star coalescences, our limits place the progenitors at z greater than or similar to 0.2. While it is possible, due to the high background from atmospheric neutrinos, that neither event is astrophysical, the MWA observations are nevertheless among the first to follow up neutrino candidates in the radio, and illustrate the promise of wide-field instruments like MWA for detecting EM counterparts to such events.
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de los Rios, M., Petac, M., Zaldivar, B., Bonaventura, N. R., Calore, F., & Iocco, F. (2023). Determining the dark matter distribution in simulated galaxies with deep learning. Mon. Not. Roy. Astron. Soc., 525(4), 6015–6035.
Abstract: We present a novel method of inferring the dark matter (DM) content and spatial distribution within galaxies, using convolutional neural networks (CNNs) trained within state-of-the-art hydrodynamical simulations (Illustris-TNG100). Within the controlled environment of the simulation, the framework we have developed is capable of inferring the DM mass distribution within galaxies of mass similar to 10(11)-10(13)M(circle dot) from the gravitationally baryon-dominated internal regions to the DM-rich, baryon-depleted outskirts of the galaxies, with a mean absolute error always below approximate to 0.25 when using photometrical and spectroscopic information. With respect to traditional methods, the one presented here also possesses the advantages of not relying on a pre-assigned shape for the DM distribution, to be applicable to galaxies not necessarily in isolation, and to perform very well even in the absence of spectroscopic observations.
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Gammaldi, V., Zaldivar, B., Sanchez-Conde, M. A., & Coronado-Blazquez, J. (2023). A search for dark matter among Fermi-LAT unidentified sources with systematic features in machine learning. Mon. Not. Roy. Astron. Soc., 520(1), 1348–1361.
Abstract: Around one-third of the point-like sources in the Fermi-LAT catalogues remain as unidentified sources (unIDs) today. Indeed, these unIDs lack a clear, univocal association with a known astrophysical source. If dark matter (DM) is composed of weakly interacting massive particles (WIMPs), there is the exciting possibility that some of these unIDs may actually be DM sources, emitting gamma-rays from WIMPs annihilation. We propose a new approach to solve the standard, machine learning (ML) binary classification problem of disentangling prospective DM sources (simulated data) from astrophysical sources (observed data) among the unIDs of the 4FGL Fermi-LAT catalogue. We artificially build two systematic features for the DM data which are originally inherent to observed data: the detection significance and the uncertainty on the spectral curvature. We do it by sampling from the observed population of unIDs, assuming that the DM distributions would, if any, follow the latter. We consider different ML models: Logistic Regression, Neural Network (NN), Naive Bayes, and Gaussian Process, out of which the best, in terms of classification accuracy, is the NN, achieving around 93 . 3 per cent +/- 0 . 7 per cent performance. Other ML evaluation parameters, such as the True Ne gativ e and True Positive rates, are discussed in our work. Applying the NN to the unIDs sample, we find that the de generac y between some astrophysical and DM sources can be partially solved within this methodology. None the less, we conclude that there are no DM source candidates among the pool of 4FGL Fermi-LAT unIDs.
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