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ANTARES Collaboration(Albert, A. et al), Colomer, M., Gozzini, R., Hernandez-Rey, J. J., Illuminati, G., Khan-Chowdhury, N. R., et al. (2021). Constraining the contribution of Gamma-Ray Bursts to the high-energy diffuse neutrino flux with 10 yr of ANTARES data. Mon. Not. Roy. Astron. Soc., 500(4), 5614–5628.
Abstract: Addressing the origin of the astrophysical neutrino flux observed by IceCube is of paramount importance. Gamma-Ray Bursts (GRBs) are among the few astrophysical sources capable of achieving the required energy to contribute to such neutrino flux through p gamma interactions. In this work, ANTARFS data have been used to search for upward going muon neutrinos in spatial and temporal coincidence with 784 GRBs occurred from 2007 to 2017. For each GRB, the expected neutrino flux has been calculated in the framework of the internal shock model and the impact of the lack of knowledge on the majority of source redshifts and on other intrinsic parameters of the emission mechanism has been quantified. It is found that the model parameters that set the radial distance where shock collisions occur have the largest impact on neutrino flux expectations. In particular, the bulk Lorentz factor of the source ejecta and the minimum variability time-scale are found to contribute significantly to the GRB-neutrino flux uncertainty. For the selected sources, ANTARES data have been analysed by maximizing the discovery probability of the stacking sample through an extended maximum-likelihood strategy. Since no neutrino event passed the quality cuts set by the optimization procedure, 90 per cent confidence level upper limits (with their uncertainty) on the total expected diffuse neutrino flux have been derived, according to the model. The GRB contribution to the observed diffuse astrophysical neutrino flux around 100 TeV is constrained to be less than 10 percent.
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ANTARES Collaboration(Albert, A. et al), Barrios-Marti, J., Coleiro, A., Hernandez-Rey, J. J., Illuminati, G., Lotze, M., et al. (2017). All-sky search for high-energy neutrinos from gravitational wave event GW170104 with the ANTARES neutrino telescope. Eur. Phys. J. C, 77(12), 911–7pp.
Abstract: Advanced LIGO detected a significant gravitational wave signal (GW170104) originating from the coalescence of two black holes during the second observation run on January 4th, 2017. Anall-sky high-energy neutrino follow-up search has been made using data from the Antares neutrino telescope, including both upgoing and downgoing events in two separate analyses. No neutrino candidates were found within +/- 500 s around the GW event time nor any time clustering of events over an extended time window of +/- 3 months. The non-detection is used to constrain isotropic-equivalent high-energy neutrino emission from GW170104 to less than similar to 1.2 x 10(55) erg for a E-2 spectrum. This constraint is valid in the energy range corresponding to the 5-95% quantiles of the neutrino flux [3.2 TeV; 3.6 PeV], if the GW emitter was below the Antares horizon at the alert time.
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Centelles Chulia, S., Srivastava, R., & Vicente, A. (2021). The inverse seesaw family: Dirac and Majorana. J. High Energy Phys., 03(3), 248–29pp.
Abstract: After developing a general criterion for deciding which neutrino mass models belong to the category of inverse seesaw models, we apply it to obtain the Dirac analogue of the canonical Majorana inverse seesaw model. We then generalize the inverse seesaw model and obtain a class of inverse seesaw mechanisms both for Majorana and Dirac neutrinos. We further show that many of the models have double or multiple suppressions coming from tiny symmetry breaking “mu -parameters”. These models can be tested both in colliders and with the observation of lepton flavour violating processes.
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Cordero-Carrion, I., Hirsch, M., & Vicente, A. (2019). Master Majorana neutrino mass parametrization. Phys. Rev. D, 99(7), 075019–6pp.
Abstract: After introducing a master formula for the Majorana neutrino mass matrix, we present a master parametrization for the Yukawa matrices automatically in agreement with neutrino oscillation data. This parametrization can be used for any model that induces Majorana neutrino masses. The application of the master parametrization is also illustrated in an example model, with special focus on its lepton flavor violating phenomenology.
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Baglio, J., Campanario, F., Glaus, S., Mühlleitner, M., Ronca, J., & Spira, M. (2023). Full NLO QCD predictions for Higgs-pair production in the 2-Higgs-doublet model. Eur. Phys. J. C, 83(9), 826–14pp.
Abstract: After the discovery of the Higgs boson in 2012 at the CERN Large Hadron Collider (LHC), the study of its properties still leaves room for an extended Higgs sector with more than one Higgs boson. 2-Higgs doublet models (2HDMs) are well-motivated extensions of the Standard Model (SM) with five physical Higgs bosons: two CP-even states h and H, one CP-odd state A, and two charged states H-+/-. In this letter, we present the calculation of the full next-to-leading order (NLO) QCD corrections to hH and AA production at the LHC in the 2HDM at small values of the ratio of the vacuum expectation values, tan beta, including the exact top-mass dependence everywhere in the calculation. Using techniques applied in the NLO QCD SM Higgs pair production calculation, we present results for the total cross section as well as for the Higgs-pair-mass distribution at the LHC. We also provide the top-quark scale and scheme uncertainties which are found to be sizeable.
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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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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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Stoppa, F., Vreeswijk, P., Bloemen, S., Bhattacharyya, S., Caron, S., Johannesson, G., et al. (2022). AutoSourceID-Light Fast optical source localization via U-Net and Laplacian of Gaussian. Astron. Astrophys., 662, A109–8pp.
Abstract: Aims. With the ever-increasing survey speed of optical wide-field telescopes and the importance of discovering transients when they are still young, rapid and reliable source localization is paramount. We present AutoSourceID-Light (ASID-L), an innovative framework that uses computer vision techniques that can naturally deal with large amounts of data and rapidly localize sources in optical images. Methods. We show that the ASID-L algorithm based on U-shaped networks and enhanced with a Laplacian of Gaussian filter provides outstanding performance in the localization of sources. A U-Net network discerns the sources in the images from many different artifacts and passes the result to a Laplacian of Gaussian filter that then estimates the exact location. Results. Using ASID-L on the optical images of the MeerLICHT telescope demonstrates the great speed and localization power of the method. We compare the results with SExtractor and show that our method outperforms this more widely used method. ASID-L rapidly detects more sources not only in low- and mid-density fields, but particularly in areas with more than 150 sources per square arcminute. The training set and code used in this paper are publicly available.
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