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ANTARES and TANAMI Collaborations(Adrian-Martinez, S. et al), Barrios-Marti, J., Gomez-Gonzalez, J. P., Hernandez-Rey, J. J., Lambard, G., Mangano, S., et al. (2015). ANTARES constrains a blazar origin of two IceCube PeV neutrino events. Astron. Astrophys., 576, L8–6pp.
Abstract: Context. The source(s) of the neutrino excess reported by the IceCube Collaboration is unknown. The TANAMI Collaboration recently reported on the multiwavelength emission of six bright, variable blazars which are positionally coincident with two of the most energetic IceCube events. Objects like these are prime candidates to be the source of the highest-energy cosmic rays, and thus of associated neutrino emission. Aims. We present an analysis of neutrino emission from the six blazars using observations with the ANTARES neutrino telescope. Methods. The standard methods of the ANTARES candidate list search are applied to six years of data to search for an excess of muons – and hence their neutrino progenitors – from the directions of the six blazars described by the TANAMI Collaboration, and which are possibly associated with two IceCube events. Monte Carlo simulations of the detector response to both signal and background particle fluxes are used to estimate the sensitivity of this analysis for different possible source neutrino spectra. A maximum-likelihood approach, using the reconstructed energies and arrival directions of through-going muons, is used to identify events with properties consistent with a blazar origin. Results. Both blazars predicted to be the most neutrino-bright in the TANAMI sample (1653-329 and 1714-336) have a signal flux fitted by the likelihood analysis corresponding to approximately one event. This observation is consistent with the blazar-origin hypothesis of the IceCube event IC 14 for a broad range of blazar spectra, although an atmospheric origin cannot be excluded. No ANTARES events are observed from any of the other four blazars, including the three associated with IceCube event IC20. This excludes at a 90% confidence level the possibility that this event was produced by these blazars unless the neutrino spectrum is flatter than -2.4.
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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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Arnalte-Mur, P., Labatie, A., Clerc, N., Martinez, V. J., Starck, J. L., Lachieze-Rey, M., et al. (2012). Wavelet analysis of baryon acoustic structures in the galaxy distribution. Astron. Astrophys., 542, A34–11pp.
Abstract: Context. Baryon acoustic oscillations (BAO) are imprinted in the density field by acoustic waves travelling in the plasma of the early universe. Their fixed scale can be used as a standard ruler to study the geometry of the universe. Aims. The BAO have been previously detected using correlation functions and power spectra of the galaxy distribution. We present a new method to detect the real-space structures associated with BAO. These baryon acoustic structures are spherical shells of relatively small density contrast, surrounding high density central regions. Methods. We design a specific wavelet adapted to search for shells, and exploit the physics of the process by making use of two different mass tracers, introducing a specific statistic to detect the BAO features. We show the effect of the BAO signal in this new statistic when applied to the Lambda – cold dark matter (Lambda CDM) model, using an analytical approximation to the transfer function. We confirm the reliability and stability of our method by using cosmological N-body simulations from the MareNostrum Institut de Ciencies de l'Espai (MICE). Results. We apply our method to the detection of BAO in a galaxy sample drawn from the Sloan Digital Sky Survey (SDSS). We use the “main” catalogue to trace the shells, and the luminous red galaxies (LRG) as tracers of the high density central regions. Using this new method, we detect, with a high significance, that the LRG in our sample are preferentially located close to the centres of shell-like structures in the density field, with characteristics similar to those expected from BAO. We show that stacking selected shells, we can find their characteristic density profile. Conclusions. We delineate a new feature of the cosmic web, the BAO shells. As these are real spatial structures, the BAO phenomenon can be studied in detail by examining those shells.
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Davesne, D., Pastore, A., & Navarro, J. (2016). Extended Skyrme equation of state in asymmetric nuclear matter. Astron. Astrophys., 585, A83–11pp.
Abstract: We present a new equation of state for infinite systems (symmetric, asymmetric, and neutron matter) based on an extended Skyrme functional that has been constrained by microscopic Brueckner-Bethe-Goldstone results. The resulting equation of state reproduces the main features of microscopic calculations very accurately and is compatible with recent measurements of two times Solar-mass neutron stars. We provide all necessary analytical expressions to facilitate a quick numerical implementation of quantities of astrophysical interest.
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De La Torre Luque, P., Gaggero, D., Grasso, D., Fornieri, O., Egberts, K., Steppa, C., et al. (2023). Galactic diffuse gamma rays meet the PeV frontier. Astron. Astrophys., 672, A58–11pp.
Abstract: The Tibet AS gamma and LHAASO collaborations recently reported the observation of a gamma-ray diffuse emission with energy up to the PeV level from the Galactic plane.Aims. We discuss the relevance of non-uniform cosmic-ray transport scenarios and the implications of these results for cosmic-ray physics.Methods. We used the DRAGON and HERMES codes to build high-resolution maps and spectral distributions of that emission for several representative models under the condition that they reproduce a wide set of local cosmic-ray data up to 100 PeV.Results. We show that the energy spectra measured by Tibet AS gamma, LHAASO, ARGO-YBJ, and Fermi-LAT in several regions of interest in the sky can all be reasonably described in terms of the emission arising by the Galactic cosmic-ray “sea”. We also show that all our models are compatible with IceTop gamma-ray upper limits.Conclusions. We compare the predictions of conventional and space-dependent transport models with those data sets. Although the Fermi-LAT, ARGO-YBJ, and LHAASO preliminary data slightly favor this scenario, due to the still large experimental errors, the poorly known source spectral shape at the highest energies, the potential role of spatial fluctuations in the leptonic component, and a possible larger-than-expected contamination due to unresolved sources, a solid confirmation requires further investigations. We discuss which measurements will be most relevant in order to resolve the remaining degeneracy.
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HAWC Collaboration(Alfaro, R. et al), & Salesa Greus, F. (2022). Validation of standardized data formats and tools for ground-level particle-based gamma-ray observatories. Astron. Astrophys., 667, A36–12pp.
Abstract: Context. Ground-based gamma-ray astronomy is still a rather young field of research, with strong historical connections to particle physics. This is why most observations are conducted by experiments with proprietary data and analysis software, as is usual in the particle physics field. However, in recent years, this paradigm has been slowly shifting toward the development and use of open-source data formats and tools, driven by upcoming observatories such as the Cherenkov Telescope Array (CTA). In this context, a community-driven, shared data format (the gamma-astro-data-format, or GADF) and analysis tools such as Gammapy and ctools have been developed. So far, these efforts have been led by the Imaging Atmospheric Cherenkov Telescope community, leaving out other types of ground-based gamma-ray instruments. Aims. We aim to show that the data from ground particle arrays, such as the High-Altitude Water Cherenkov (HAWC) observatory, are also compatible with the GADF and can thus be fully analyzed using the related tools, in this case, Gammapy. Methods. We reproduced several published HAWC results using Gammapy and data products compliant with GADF standard. We also illustrate the capabilities of the shared format and tools by producing a joint fit of the Crab spectrum including data from six different gamma-ray experiments. Results. We find excellent agreement with the reference results, a powerful confirmation of both the published results and the tools involved. Conclusions. The data from particle detector arrays such as the HAWC observatory can be adapted to the GADF and thus analyzed with Gammapy. A common data format and shared analysis tools allow multi-instrument joint analysis and effective data sharing. To emphasize this, a sample of Crab nebula event lists is made public with this paper. Because of the complementary nature of pointing and wide-field instruments, this synergy will be distinctly beneficial for the joint scientific exploitation of future observatories such as the Southern Wide-field Gamma-ray Observatory and CTA.
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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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Sanchis-Lozano, M. A., Melia, F., Lopez-Corredoira, M., & Sanchis-Gual, N. (2022). Missing large-angle correlations versus even-odd point-parity imbalance in the cosmic microwave background. Astron. Astrophys., 660, A121–10pp.
Abstract: Context. The existence of a maximum correlation angle (theta(max) & 60 greater than or similar to degrees) in the two-point angular temperature correlations of cosmic microwave background (CMB) radiation, measured by WMAP and Planck, stands in sharp contrast to the prediction of standard inflationary cosmology, in which the correlations should extend across the full sky (i.e., 180 degrees). The introduction of a hard lower cuto ff (k(min)) in the primordial power spectrum, however, leads naturally to the existence of theta(max). Among other cosmological anomalies detected in these data, an apparent dominance of odd-over-even parity multipoles has been seen in the angular power spectrum of the CMB. This feature, however, may simply be due to observational contamination in certain regions of the sky. Aims. In attempting to provide a more detailed assessment of whether this odd-over-even asymmetry is intrinsic to the CMB, we therefore proceed in this paper, first, to examine whether this odd-even parity imbalance also manifests itself in the angular correlation function and, second, to examine in detail the interplay between the presence of theta(max) and this observed anomaly. Methods. We employed several parity statistics and recalculated the angular correlation function for di fferent values of the cuto ff kmin in order to optimize the fit to the di fferent Planck 2018 data. Results. We find a phenomenological connection between these features in the data, concluding that both must be considered together in order to optimize the theoretical fit to the Planck 2018 data. Conclusions. This outcome is independent of whether the parity imbalance is intrinsic to the CMB, but if it is, the odd-over-even asymmetry would clearly point to the emergence of new physics.
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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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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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