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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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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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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Guerrero, C., Tessler, M., Paul, M., Lerendegui-Marco, J., Heinitz, S., Maugeri, E. A., et al. (2019). The s-process in the Nd-Pm-Sm region: Neutron activation of Pm-147. Phys. Lett. B, 797, 134809–6pp.
Abstract: The Nd-Pm-Sm branching is of interest for the study of the s-process, related to the production of heavy elements in stars. As Sm-148 and Sm-150 are s-only isotopes, the understanding of the branching allows constraining the s-process neutron density. In this context the key physics input needed is the cross section of the three unstable nuclides in the region: Nd-147 (10.98 d half-life), Pm-147 (2.62 yr) and Pm-148 (5.37 d). This paper reports on the activation measurement of Pm-147, the longest-lived of the three nuclides. The cross section measurement has been carried out by activation at the SARAF LiLiT facility using a 56(2) μg target. Compared to the single previous measurement of Pm-147, the measurement presented herein benefits from a target 2000 times more massive. The resulting Maxwellian Averaged Cross Section (MACS) to the ground and metastable states in Pm-148 are 469(50) mb and 357(27) mb. These values are 41% higher (to the ground state) and 15% lower (to the metastable state) than the values reported so far, leading however to a total cross section of 826(107) mb consistent within uncertainties with the previous result and hence leaving unchanged the previous calculation of the s-process neutron density.
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n_TOF Collaboration(Mazzone, A. et al), Babiano-Suarez, V., Caballero, L., Domingo-Pardo, C., Ladarescu, I., & Tain, J. L. (2020). Measurement of the Gd-154(n, gamma) cross section and its astrophysical implications. Phys. Lett. B, 804, 135405–6pp.
Abstract: The neutron capture cross section of Gd-154 was measured from 1 eV to 300 keV in the experimental area located 185 m from the CERN n_TOF neutron spallation source, using a metallic sample of gadolinium, enriched to 67% in Gd-154. The capture measurement, performed with four C6D6 scintillation detectors, has been complemented by a transmission measurement performed at the GELINA time-of-flight facility (JRC-Geel), thus minimising the uncertainty related to sample composition. An accurate Maxwellian averaged capture cross section (MACS) was deduced over the temperature range of interest for s process nucleosynthesis modelling. We report a value of 880(50) mb for the MACS at kT = 30 keV, significantly lower compared to values available in literature. The new adopted Gd-154(n, gamma) cross section reduces the discrepancy between observed and calculated solar s-only isotopic abundances predicted by s-process nucleosynthesis models.
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Hall, O. et al, Agramunt, J., Algora, A., Domingo-Pardo, C., Morales, A. I., Rubio, B., et al. (2021). beta-delayed neutron emission of r-process nuclei at the N=82 shell closure. Phys. Lett. B, 816, 136266–7pp.
Abstract: Theoretical models of beta-delayed neutron emission are used as crucial inputs in r-process calculations. Benchmarking the predictions of these models is a challenge due to a lack of currently available experimental data. In this work the beta-delayed neutron emission probabilities of 33 nuclides in the important mass regions south and south-west of Sn-132 are presented, 16 for the first time. The measurements were performed at RIKEN using the Advanced Implantation Detector Array (AIDA) and the BRIKEN neutron detector array. The P-1n values presented constrain the predictions of theoretical models in the region, affecting the final abundance distribution of the second r-process peak at A approximate to 130.
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Hueso-Gonzalez, F., Casaña Copado, J. V., Fernandez Prieto, A., Gallas Torreira, A., Lemos Cid, E., Ros Garcia, A., et al. (2022). A dead-time-free data acquisition system for prompt gamma-ray measurements during proton therapy treatments. Nucl. Instrum. Methods Phys. Res. A, 1033, 166701–9pp.
Abstract: In cancer patients undergoing proton therapy, a very intense secondary radiation is produced during the treatment, which lasts around one minute. About one billion prompt gamma-rays are emitted per second, and their detection with fast scintillation detectors is useful for monitoring a correct beam delivery. To cope with the expected count rate and pile-up, as well as the scarce statistics due to the short treatment duration, we developed an eidetic data acquisition system capable of continuously digitizing the detector signal with a high sampling rate and without any dead time. By streaming the fully unprocessed waveforms to the computer, complex pile-up decomposition algorithms can be applied and optimized offline. We describe the data acquisition architecture and the multiple experimental tests designed to verify the sustained data throughput speed and the absence of dead time. While the system is tailored for the proton therapy environment, the methodology can be deployed in any other field requiring the recording of raw waveforms at high sampling rates with zero dead time.
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NEXT Collaboration(Jones, B. J. P. et al), Carcel, S., Carrion, J. V., Diaz, J., Martin-Albo, J., Martinez, A., et al. (2022). The dynamics of ions on phased radio-frequency carpets in high pressure gases and application for barium tagging in xenon gas time projection chambers. Nucl. Instrum. Methods Phys. Res. A, 1039, 167000–19pp.
Abstract: Radio-frequency (RF) carpets with ultra-fine pitches are examined for ion transport in gases at atmospheric pressures and above. We develop new analytic and computational methods for modeling RF ion transport at densities where dynamics are strongly influenced by buffer gas collisions. An analytic description of levitating and sweeping forces from phased arrays is obtained, then thermodynamic and kinetic principles are used to calculate ion loss rates in the presence of collisions. This methodology is validated against detailed microscopic SIMION simulations. We then explore a parameter space of special interest for neutrinoless double beta decay experiments: transport of barium ions in xenon at pressures from 1 to 10 bar. Our computations account for molecular ion formation and pressure dependent mobility as well as finite temperature effects. We discuss the challenges associated with achieving suitable operating conditions, which lie beyond the capabilities of existing devices, using presently available or near-future manufacturing techniques.
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Araujo Filho, A. A., Zare, S., Porffrio, P. J., Kriz, J., & Hassanabadi, H. (2023). Thermodynamics and evaporation of a modified Schwarzschild black hole in a non-commutative gauge theory. Phys. Lett. B, 838, 137744–9pp.
Abstract: In this work, we study the thermodynamic properties on a non-commutative background via gravitational gauge field potentials. This procedure is accomplished after contracting de Sitter (dS) group, SO(4, 1), with the Poincare group, ISO(3, 1). Particularly, we focus on a static spherically symmetric black hole. In this manner, we calculate the modified Hawking temperature and the other deformed thermal state quantities, namely, entropy, heat capacity, Helmholtz free energy and pressure. Finally, we also investigate the black hole evaporation process in such a context.
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