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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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HAWC Collaboration(Albert, A. et al), & Salesa Greus, F. (2023). Detailed Analysis of the TeV gamma-Ray Sources 3HWC J1928+178, 3HWC J1930+188, and the New Source HAWC J1932+192. Astrophys. J., 942(2), 96–18pp.
Abstract: The latest High Altitude Water Cherenkov (HAWC) point-like source catalog up to 56 TeV reported the detection of two sources in the region of the Galactic plane at galactic longitude 52 degrees < l < 55 degrees, 3HWC J1930+188 and 3HWC J1928+178. The first one is associated with a known TeV source, the supernova remnant SNR G054.1+00.3. It was discovered by one of the currently operating Imaging Atmospheric Cherenkov Telescope (IACT), the Very Energetic Radiation Imaging Telescope Array System (VERITAS), detected by the High Energy Stereoscopic System (H.E.S.S), and identified as a composite SNR. However, the source 3HWC J1928+178, discovered by HAWC and coincident with the pulsar PSR J1928+1746, was not detected by any IACT despite their long exposure on the region, until a recent new analysis of H.E.S.S. data was able to confirm it. Moreover, no X-ray counterpart has been detected from this pulsar. We present a multicomponent fit of this region using the latest HAWC data. This reveals an additional new source, HAWC J1932+192, which is potentially associated with the pulsar PSR J1932+1916, whose gamma-ray emission could come from the acceleration of particles in its pulsar wind nebula. In the case of 3HWC J1928+178, several possible explanations are explored, in an attempt to unveil the origins of the very-high-energy gamma-ray emission.
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AMON Team, A. N. T. A. R. E. S. and H. A. W. C. C.(A. S., H.A. et al), Alves Garres, S., Calvo, D., Carretero, V., Gozzini, R., Hernandez-Rey, J. J., et al. (2023). Search for Gamma-Ray and Neutrino Coincidences Using HAWC and ANTARES Data. Astrophys. J., 944(2), 166–9pp.
Abstract: In the quest for high-energy neutrino sources, the Astrophysical Multimessenger Observatory Network has implemented a new search by combining data from the High Altitude Water Cherenkov (HAWC) Observatory and the Astronomy with a Neutrino Telescope and Abyss environmental RESearch (ANTARES) neutrino telescope. Using the same analysis strategy as in a previous detector combination of HAWC and IceCube data, we perform a search for coincidences in HAWC and ANTARES events that are below the threshold for sending public alerts in each individual detector. Data were collected between 2015 July and 2020 February with a live time of 4.39 yr. Over this time period, three coincident events with an estimated false-alarm rate of <1 coincidence per year were found. This number is consistent with background expectations.
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Figueroa, D. G., Florio, A., Torrenti, F., & Valkenburg, W. (2023). CosmoLattice: A modern code for lattice simulations of scalar and gauge field dynamics in an expanding universe. Comput. Phys. Commun., 283, 108586–13pp.
Abstract: This paper describes CosmoGattice, a modern package for lattice simulations of the dynamics of interacting scalar and gauge fields in an expanding universe. CosmoGattice incorporates a series of features that makes it very versatile and powerful: i) it is written in C++ fully exploiting the object oriented programming paradigm, with a modular structure and a clear separation between the physics and the technical details, ii) it is MPI-based and uses a discrete Fourier transform parallelized in multiple spatial dimensions, which makes it specially appropriate for probing scenarios with well -separated scales, running very high resolution simulations, or simply very long ones, iii) it introduces its own symbolic language, defining field variables and operations over them, so that one can introduce differential equations and operators in a manner as close as possible to the continuum, iv) it includes a library of numerical algorithms, ranging from O(delta t(2)) to O(delta t(10)) methods, suitable for simulating global and gauge theories in an expanding grid, including the case of 'self-consistent' expansion sourced by the fields themselves. Relevant observables are provided for each algorithm (e.g. energy densities, field spectra, lattice snapshots) and we note that, remarkably, all our algorithms for gauge theories (Abelian or non-Abelian) always respect the Gauss constraint to machine precision. Program summary Program Title:: CosmoGattice CPC Library link to program files: https://doi .org /10 .17632 /44vr5xssc6 .1 Developer's repository link: http://github .com /cosmolattice /cosmolattice Licensing provisions: MIT Programming language: C++, MPI Nature of problem: The phenomenology of high energy physics in the early universe is typically characterized by non-linear dynamics, which cannot be captured accurately with analytical techniques. In order to fully understand the non-linearities developed in a given scenario, one needs to carry out lattice simulations. A number of public packages for lattice simulations have appeared over the years, but most of them are only capable of simulating scalar fields. However, realistic models of particle physics do contain other kind of field species, such as (Abelian or non-Abelian) gauge fields, whose non-linear dynamics can also play a relevant role in the early universe. Tensor modes representing gravitational waves are also naturally expected in many scenarios. Solution method: CosmoGattice represents a modern code for lattice simulations of scalar-gauge field theories in an expanding universe. It allows for the simulation of the evolution of interacting (singlet) scalar fields, charged scalar fields under U(1) and/or SU(2) gauge groups, and the corresponding associated Abelian and/or non-Abelian gauge fields. From version 1.1 onward, CosmoGattice also allows to simulate the production of gravitational waves. Simulations can be done either in a flat space-time background, or in a homogeneous and isotropic (spatially flat) expanding FLRW background. CosmoGattice provides symplectic integrators, with accuracy ranging from O (delta t(2)) up to O(delta t(10)), to simuate the non-linear dynamics of the appropriate fields in comoving three-dimensional lattices. The code is parallelized with MPI, and uses a discrete Fourier Transform parallelized in multiple spatial dimensions, which makes it a very powerful code for probing physical problems with well-separated scales. Moreover, the code has been designed as a `platform' to implement any system of dynamical equations suitable for discretization on a lattice.
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