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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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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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ANTARES and HESS Collaborations(Petroff, E. et al), Barrios-Marti, J., Hernandez-Rey, J. J., Illuminati, G., Lotze, M., Tönnis, C., et al. (2017). A polarized fast radio burst at low Galactic latitude. Mon. Not. Roy. Astron. Soc., 469(4), 4465–4482.
Abstract: We report on the discovery of a new fast radio burst (FRB), FRB 150215, with the Parkes radio telescope on 2015 February 15. The burst was detected in real time with a dispersion measure (DM) of 1105.6 +/- 0.8 pc cm(-3), a pulse duration of 2.8(-0.5)(+1.2) ms, and a measured peak flux density assuming that the burst was at beam centre of 0.7(-0.1)(+0.2) Jy. The FRB originated at a Galactic longitude and latitude of 24.66 degrees, 5.28 degrees and 25 degrees away from the Galactic Center. The burst was found to be 43 +/- 5 per cent linearly polarized with a rotation measure (RM) in the range -9 < RM < 12 rad m(-2) (95 per cent confidence level), consistent with zero. The burst was followed up with 11 telescopes to search for radio, optical, X-ray, gamma-ray and neutrino emission. Neither transient nor variable emission was found to be associated with the burst and no repeat pulses have been observed in 17.25 h of observing. The sightline to the burst is close to the Galactic plane and the observed physical properties of FRB 150215 demonstrate the existence of sight lines of anomalously low RM for a given electron column density. The Galactic RM foreground may approach a null value due to magnetic field reversals along the line of sight, a decreased total electron column density from the Milky Way, or some combination of these effects. A lower Galactic DM contribution might explain why this burst was detectable whereas previous searches at low latitude have had lower detection rates than those out of the plane.
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Rasco, B. C., Brewer, N. T., Yokoyama, R., Grzywacz, R., Rykaczewski, K. P., Tolosa-Delgado, A., et al. (2018). The ORNL analysis technique for extracting beta-delayed multi-neutron branching ratios with BRIKEN. Nucl. Instrum. Methods Phys. Res. A, 911, 79–86.
Abstract: Many choices are available in order to evaluate large radioactive decay networks. There are many parameters that influence the calculated beta-decay delayed single and multi-neutron emission branching fractions. We describe assumptions about the decay model, background, and other parameters and their influence on beta-decay delayed multi-neutron emission analysis. An analysis technique, the ORNL BRIKEN analysis procedure, for determining beta-delayed multi-neutron branching ratios in beta-neutron precursors produced by means of heavy-ion fragmentation is presented. The technique is based on estimating the initial activities of zero, one, and two neutrons occurring in coincidence with an ion-implant and beta trigger. The technique allows one to extract beta-delayed multi-neutron decay branching ratios measured with the He-3 BRIKEN neutron counter. As an example, two analyses of the beta-neutron emitter Cu-77 based on different a priori assumptions are presented along with comparisons to literature values.
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