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Bhattacharya, A., Esmaili, A., Palomares-Ruiz, S., & Sarcevic, I. (2017). Probing decaying heavy dark matter with the 4-year IceCube HESE data. J. Cosmol. Astropart. Phys., 07(7), 027–36pp.
Abstract: After the first four years of data taking, the IceCube neutrino telescope has observed 54 high-energy starting events (HESE) with deposited energies between 20TeV and 2PeV. The background from atmospheric muons and neutrinos is expected to be of about 20 events, all below 100TeV, thus pointing towards the astrophysical origin of about 8 events per year in that data set. However, their precise origin remains unknown. Here, we perform a detailed analysis of this event sample (considering simultaneously the energy, hemisphere and topology of the events) by assuming two contributions for the signal events: an isotropic power-law flux and a flux from decaying heavy dark matter. We fit the mass and lifetime of the dark matter and the normalization and spectral index of an isotropic power-law flux, for various decay channels of dark matter. We find that a significant contribution from dark matter decay is always slightly favored, either to explain the excess below 100TeV, as in the case of decays to quarks or, as in the case of neutrino channels, to explain the three multi-PeV events. Also, we consider the possibility to interpret all the data by dark matter decays only, considering various combinations of two decay channels. We show that the decaying dark matter scenario provides a better fit to HESE data than the isotropic power-law flux.
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Marzocca, D., Petcov, S. T., Romanino, A., & Sevilla, M. C. (2013). Nonzero |U_e3| from charged lepton corrections and the atmospheric neutrino mixing angle. J. High Energy Phys., 05(5), 073–27pp.
Abstract: After the successful determination of the reactor neutrino mixing angle theta(13) not equal 0.16 not equal 0, a new feature suggested by the current neutrino oscillation data is a sizeable deviation of the atmospheric neutrino mixing angle theta(23) from pi/4. Using the fact that the neutrino mixing matrix U = (UeU nu)-U-dagger, where U-e and U-nu result from the diagonalisation of the charged lepton and neutrino mass matrices, and assuming that U-nu has a i) bimaximal (BM), H) tri-bimaximal (TBM) form, or else Hi) corresponds to the conservation of the lepton charge L' = L-e – L μ- L-tau (LC), we investigate quantitatively what are the minimal forms of U-e, in terms of angles and phases it contains, that can provide the requisite corrections to U-nu so that theta(13), theta(23) and the solar neutrino mixing angle theta(12) have values compatible with the current data. Two possible orderings of the 12 and the 23 rotations in U-e, “standard” and “inverse”, are considered. The results we obtain depend strongly on the type of ordering. In the case of “standard” ordering, in particular, the Dirac CP violation phase delta, present in U, is predicted to have a value in a narrow interval around i) delta similar or equal to pi in the BM (or LC) case, H) delta congruent to 3 pi/2 or pi/2 in the TBM case, the CP conserving values delta = 0, pi, 2 pi being excluded in the TBM case at more than 4 sigma.
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Stoppa, F., Ruiz de Austri, R., Vreeswijk, P., Bhattacharyya, S., Caron, S., Bloemen, S., et al. (2023). AutoSourceID-FeatureExtractor Optical image analysis using a two-step mean variance estimation network for feature estimation and uncertainty characterisation. Astron. Astrophys., 680, A108–14pp.
Abstract: Aims. In astronomy, machine learning has been successful in various tasks such as source localisation, classification, anomaly detection, and segmentation. However, feature regression remains an area with room for improvement. We aim to design a network that can accurately estimate sources' features and their uncertainties from single-band image cutouts, given the approximated locations of the sources provided by the previously developed code AutoSourceID-Light (ASID-L) or other external catalogues. This work serves as a proof of concept, showing the potential of machine learning in estimating astronomical features when trained on meticulously crafted synthetic images and subsequently applied to real astronomical data.Methods. The algorithm presented here, AutoSourceID-FeatureExtractor (ASID-FE), uses single-band cutouts of 32x32 pixels around the localised sources to estimate flux, sub-pixel centre coordinates, and their uncertainties. ASID-FE employs a two-step mean variance estimation (TS-MVE) approach to first estimate the features and then their uncertainties without the need for additional information, for example the point spread function (PSF). For this proof of concept, we generated a synthetic dataset comprising only point sources directly derived from real images, ensuring a controlled yet authentic testing environment.Results. We show that ASID-FE, trained on synthetic images derived from the MeerLICHT telescope, can predict more accurate features with respect to similar codes such as SourceExtractor and that the two-step method can estimate well-calibrated uncertainties that are better behaved compared to similar methods that use deep ensembles of simple MVE networks. Finally, we evaluate the model on real images from the MeerLICHT telescope and the Zwicky Transient Facility (ZTF) to test its transfer learning abilities.
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Stoppa, F., Bhattacharyya, S., Ruiz de Austri, R., Vreeswijk, P., Caron, S., Zaharijas, G., et al. (2023). AutoSourceID-Classifier Star-galaxy classification using a convolutional neural network with spatial information. Astron. Astrophys., 680, A109–16pp.
Abstract: Aims. Traditional star-galaxy classification techniques often rely on feature estimation from catalogs, a process susceptible to introducing inaccuracies, thereby potentially jeopardizing the classification's reliability. Certain galaxies, especially those not manifesting as extended sources, can be misclassified when their shape parameters and flux solely drive the inference. We aim to create a robust and accurate classification network for identifying stars and galaxies directly from astronomical images.Methods. The AutoSourceID-Classifier (ASID-C) algorithm developed for this work uses 32x32 pixel single filter band source cutouts generated by the previously developed AutoSourceID-Light (ASID-L) code. By leveraging convolutional neural networks (CNN) and additional information about the source position within the full-field image, ASID-C aims to accurately classify all stars and galaxies within a survey. Subsequently, we employed a modified Platt scaling calibration for the output of the CNN, ensuring that the derived probabilities were effectively calibrated, delivering precise and reliable results.Results. We show that ASID-C, trained on MeerLICHT telescope images and using the Dark Energy Camera Legacy Survey (DECaLS) morphological classification, is a robust classifier and outperforms similar codes such as SourceExtractor. To facilitate a rigorous comparison, we also trained an eXtreme Gradient Boosting (XGBoost) model on tabular features extracted by SourceExtractor. While this XGBoost model approaches ASID-C in performance metrics, it does not offer the computational efficiency and reduced error propagation inherent in ASID-C's direct image-based classification approach. ASID-C excels in low signal-to-noise ratio and crowded scenarios, potentially aiding in transient host identification and advancing deep-sky astronomy.
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ANTARES Collaboration(Adrian-Martinez, S. et al), Barrios-Marti, J., Bigongiari, C., Emanuele, U., Gomez-Gonzalez, J. P., Hernandez-Rey, J. J., et al. (2013). Search for muon neutrinos from gamma-ray bursts with the ANTARES neutrino telescope using 2008 to 2011 data. Astron. Astrophys., 559, A9–11pp.
Abstract: Aims. We search for muon neutrinos in coincidence with GRBs with the ANTARES neutrino detector using data from the end of 2007 to 2011. Methods. Expected neutrino fluxes were calculated for each burst individually. The most recent numerical calculations of the spectra using the NeuCosmA code were employed, which include Monte Carlo simulations of the full underlying photohadronic interaction processes. The discovery probability for a selection of 296 GRBs in the given period was optimised using an extended maximum-likelihood strategy. Results. No significant excess over background is found in the data, and 90% confidence level upper limits are placed on the total expected flux according to the model.
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