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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.
Keywords: astronomical databases; miscellaneous; methods; data analysis; stars; imaging; techniques; image processing
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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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n_TOF Collaboration(Stamatopoulos, A. et al), Domingo-Pardo, C., Tain, J. L., & Tarifeño-Saldivia, A. (2020). Investigation of the Pu-240(n, f) reaction at the n_TOF/EAR2 facility in the 9 meV-6 MeV range. Phys. Rev. C, 102(1), 014616–23pp.
Abstract: Background: Nuclear waste management is considered amongst the major challenges in the field of nuclear energy. A possible means of addressing this issue is waste transmutation in advanced nuclear systems, whose operation requires a fast neutron spectrum. In this regard, the accurate knowledge of neutron-induced reaction cross sections of several (minor) actinide isotopes is essential for design optimization and improvement of safety margins of such systems. One such case is Pu-240, due to its accumulation in spent nuclear fuel of thermal reactors and its usage in fast reactor fuel. The measurement of the Pu-240(n, f) cross section was previously attempted at the CERN nTOF facility EAR1 measuring station using the time-of-flight technique. Due to the low amount of available material and the given flux at EAR1, the measurement had to last several months to achieve a sufficient statistical accuracy. This long duration led to detector deterioration due to the prolonged exposure to the high alpha activity of the fission foils, therefore the measurement could not be successfully completed. Purpose: It is aimed to determine whether it is feasible to study neutron-induced fission at nTOF/EAR2 and provide data on the Pu-240(n, f) reaction in energy regions requested for applications. Methods: The study of the Pu-240(n, f) reaction was made at a new experimental area (EAR2) with a shorter flight path which delivered on average 30 times higher flux at fast neutron energies. This enabled the measurement to be performed much faster, thus limiting the exposure of the detectors to the intrinsic activity of the fission foils. The experimental setup was based on microbulk Micromegas detectors and the time-of-flight data were analyzed with an optimized pulse-shape analysis algorithm. Special attention was dedicated to the estimation of the non-negligible counting loss corrections with the development of a new methodology, and other corrections were estimated via Monte Carlo simulations of the experimental setup. Results: This new measurement of the Pu-240(n, f) cross section yielded data from 9 meV up to 6 MeV incident neutron energy and fission resonance kernels were extracted up to 10 keV. Conclusions: Neutron-induced fission of high activity samples can be successfully studied at the n_TOF/EAR2 facility at CERN covering a wide range of neutron energies, from thermal to a few MeV.
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Stadler, J., Boehm, C., & Mena, O. (2019). Comprehensive study of neutrino-dark matter mixed damping. J. Cosmol. Astropart. Phys., 08(8), 014–23pp.
Abstract: Mixed damping is a physical effect that occurs when a heavy species is coupled to a relativistic fluid which is itself free streaming. As a cross-case between collisional damping and free-streaming, it is crucial in the context of neutrino-dark matter interactions. In this work, we establish the parameter space relevant for mixed damping, and we derive an analytical approximation for the evolution of dark matter perturbations in the mixed damping regime to illustrate the physical processes responsible for the suppression of cosmological perturbations. Although extended Boltzmann codes implementing neutrino-dark matter scattering terms automatically include mixed damping, this effect has not been systematically studied. In order to obtain reliable numerical results, it is mandatory to reconsider several aspects of neutrino-dark matter interactions, such as the initial conditions, the ultra-relativistic fluid approximation and high order multiple moments in the neutrino distribution. Such a precise treatment ensures the correct assessment of the relevance of mixed damping in neutrino-dark matter interactions.
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