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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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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Amerio, A., Calore, F., Serpico, P. D., & Zaldivar, B. (2024). Deepening gamma-ray point-source catalogues with sub-threshold information. J. Cosmol. Astropart. Phys., 03(3), 055–18pp.
Abstract: We propose a novel statistical method to extend Fermi-LAT catalogues of highlatitude -y-ray sources below their nominal threshold. To do so, we rely on the determination of the differential source -count distribution of sub -threshold sources which only provides the statistical flux distribution of faint sources. By simulating ensembles of synthetic skies, we assess quantitatively the likelihood for pixels in the sky with relatively low -test statistics to be due to sources, therefore complementing the source -count distribution with spatial information. Besides being useful to orient efforts towards multi -messenger and multi -wavelength identification of new -y-ray sources, we expect the results to be especially advantageous for statistical applications such as cross -correlation analyses.
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Amerio, A., Cuoco, A., & Fornengo, N. (2023). Extracting the gamma-ray source-count distribution below the Fermi-LAT detection limit with deep learning. J. Cosmol. Astropart. Phys., 09(9), 029–39pp.
Abstract: We reconstruct the extra-galactic gamma-ray source-count distribution, or dN/dS, of resolved and unresolved sources by adopting machine learning techniques. Specifically, we train a convolutional neural network on synthetic 2-dimensional sky-maps, which are built by varying parameters of underlying source-counts models and incorporate the FermiLAT instrumental response functions. The trained neural network is then applied to the Fermi-LAT data, from which we estimate the source count distribution down to flux levels a factor of 50 below the Fermi-LAT threshold. We perform our analysis using 14 years of data collected in the (1, 10) GeV energy range. The results we obtain show a source count distribution which, in the resolved regime, is in excellent agreement with the one derived from cataloged sources, and then extends as dN/dS " S-2 in the unresolved regime, down to fluxes of 5 center dot 10-12 cm-2 s-1. The neural network architecture and the devised methodology have the flexibility to enable future analyses to study the energy dependence of the source-count distribution.
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Caballero, L., Albiol, F., Corbi Bellot, A., Domingo-Pardo, C., Leganes Nieto, J. L., Agramunt Ros, J., et al. (2018). Gamma-ray imaging system for real-time measurements in nuclear waste characterisation. J. Instrum., 13, P03016–23pp.
Abstract: Acompact, portable and large field-of-viewgamma camera that is able to identify, locate and quantify gamma-ray emitting radioisotopes in real-time has been developed. The device delivers spectroscopic and imaging capabilities that enable its use it in a variety of nuclear waste characterisation scenarios, such as radioactivity monitoring in nuclear power plants and more specifically for the decommissioning of nuclear facilities. The technical development of this apparatus and some examples of its application in field measurements are reported in this article. The performance of the presented gamma-camera is also benchmarked against other conventional techniques.
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