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Stoppa, F.; Ruiz de Austri, R.; Vreeswijk, P.; Bhattacharyya, S.; Caron, S.; Bloemen, S.; Zaharijas, G.; Principe, G.; Vodeb, V.; Groot, P.J.; Cator, E.; Nelemans, G. |
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Title |
AutoSourceID-FeatureExtractor Optical image analysis using a two-step mean variance estimation network for feature estimation and uncertainty characterisation |
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Journal Article |
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Year |
2023 |
Publication |
Astronomy & Astrophysics |
Abbreviated Journal |
Astron. Astrophys. |
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Volume |
680 |
Issue |
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Pages |
A108 - 14pp |
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Keywords |
astronomical databases: miscellaneous; methods: data analysis; stars: imaging; techniques: image processing |
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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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Address |
[Stoppa, F.; Vreeswijk, P.; Bloemen, S.; Groot, P. J.; Nelemans, G.] Radboud Univ Nijmegen, Dept Astrophys, IMAPP, POB 9010, NL-6500 GL Nijmegen, Netherlands, Email: f.stoppa@astro.ru.nl |
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Edp Sciences S A |
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English |
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0004-6361 |
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Notes |
WOS:001131898100003 |
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no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5887 |
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Author |
Stoppa, F.; Bhattacharyya, S.; Ruiz de Austri, R.; Vreeswijk, P.; Caron, S.; Zaharijas, G.; Bloemen, S.; Principe, G.; Malyshev, D.; Vodeb, V.; Groot, P.J.; Cator, E.; Nelemans, G. |
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Title |
AutoSourceID-Classifier Star-galaxy classification using a convolutional neural network with spatial information |
Type |
Journal Article |
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Year |
2023 |
Publication |
Astronomy & Astrophysics |
Abbreviated Journal |
Astron. Astrophys. |
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Volume |
680 |
Issue |
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Pages |
A109 - 16pp |
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Keywords |
methods: data analysis; techniques: image processing; astronomical databases: miscellaneous; stars: imaging; Galaxies: statistics |
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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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Address |
[Stoppa, F.; Vreeswijk, P.; Bloemen, S.; Groot, P. J.; Nelemans, G.] Radboud Univ Nijmegen, Dept Astrophys IMAPP, POB 9010, NL-6500 GL Nijmegen, Netherlands, Email: f.stoppa@astro.ru.nl |
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Edp Sciences S A |
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English |
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0004-6361 |
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Notes |
WOS:001131898100001 |
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no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5888 |
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Author |
Trotta, R.; Johannesson, G.; Moskalenko, I.V.; Porter, T.A.; Ruiz de Austri, R.; Strong, A.W. |
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Title |
Constraints on Cosmic-Ray Propagation Models from a Global Bayesian Analysis |
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Journal Article |
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Year |
2011 |
Publication |
Astrophysical Journal |
Abbreviated Journal |
Astrophys. J. |
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Volume |
729 |
Issue |
2 |
Pages |
106 - 16pp |
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Keywords |
astroparticle physics; cosmic rays; diffusion; Galaxy: general; ISM: general; methods: statistical |
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Abstract |
Research in many areas of modern physics such as, e. g., indirect searches for dark matter and particle acceleration in supernova remnant shocks rely heavily on studies of cosmic rays (CRs) and associated diffuse emissions (radio, microwave, X-rays, gamma-rays). While very detailed numerical models of CR propagation exist, a quantitative statistical analysis of such models has been so far hampered by the large computational effort that those models require. Although statistical analyses have been carried out before using semi-analytical models (where the computation is much faster), the evaluation of the results obtained from such models is difficult, as they necessarily suffer from many simplifying assumptions. The main objective of this paper is to present a working method for a full Bayesian parameter estimation for a numerical CR propagation model. For this study, we use the GALPROP code, the most advanced of its kind, which uses astrophysical information, and nuclear and particle data as inputs to self-consistently predict CRs, gamma-rays, synchrotron, and other observables. We demonstrate that a full Bayesian analysis is possible using nested sampling and Markov Chain Monte Carlo methods (implemented in the SuperBayeS code) despite the heavy computational demands of a numerical propagation code. The best-fit values of parameters found in this analysis are in agreement with previous, significantly simpler, studies also based on GALPROP. |
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[Trotta, R.] Univ London Imperial Coll Sci Technol & Med, Astrophys Grp, Blackett Lab, London SW7 2AZ, England |
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Iop Publishing Ltd |
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English |
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0004-637x |
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Notes |
ISI:000288608700029 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
541 |
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Author |
Johannesson, G.; Ruiz de Austri, R.; Vincent, A.C.; Moskalenko, I.V.; Orlando, E.; Porter, T.A.; Strong, A.W.; Trotta, R.; Feroz, F.; Graff, P.; Hobson, M.P. |
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Title |
Bayesian analysis of cosmic-ray propagation: evidence against homogeneous diffusion |
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Journal Article |
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Year |
2016 |
Publication |
Astrophysical Journal |
Abbreviated Journal |
Astrophys. J. |
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Volume |
824 |
Issue |
1 |
Pages |
16 - 19pp |
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Keywords |
astroparticle physics; cosmic rays; diffusion; Galaxy: general; ISM: general; methods: statistical |
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Abstract |
We present the results of the most complete scan of the parameter space for cosmic ray (CR) injection and propagation. We perform a Bayesian search of the main GALPROP parameters, using the MultiNest nested sampling algorithm, augmented by the BAMBI neural network machine-learning package. This is the first study to separate out low-mass isotopes (p, (p) over bar and He) from the usual light elements (Be, B, C, N, and O). We find that the propagation parameters that best-fit p, (p) over bar, and He data are significantly different from those that fit light elements, including the B/C and Be-10/Be-9 secondary-to-primary ratios normally used to calibrate propagation parameters. This suggests that each set of species is probing a very different interstellar medium, and that the standard approach of calibrating propagation parameters using B/C can lead to incorrect results. We present posterior distributions and best-fit parameters for propagation of both sets of nuclei, as well as for the injection abundances of elements from H to Si. The input GALDEF files with these new parameters will be included in an upcoming public GALPROP update. |
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[Johannesson, G.] Univ Iceland, Inst Sci, Dunhaga 3, IS-107 Reykjavik, Iceland |
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Publisher |
Iop Publishing Ltd |
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English |
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0004-637x |
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Notes |
WOS:000377937300016 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
2727 |
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Author |
Allanach, B.C.; Bednyakov, A.; Ruiz de Austri, R. |
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Title |
Higher order corrections and unification in the minimal supersymmetric standard model: SOFTSUSY3.5 |
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Journal Article |
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2015 |
Publication |
Computer Physics Communications |
Abbreviated Journal |
Comput. Phys. Commun. |
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Volume |
189 |
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Pages |
192-206 |
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Keywords |
Sparticle; MSSM |
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Abstract |
We explore the effects of three-loop minimal supersymmetric standard model renormalisation group equation terms and some leading two-loop threshold corrections on gauge and Yukawa unification: each being one loop higher order than current public spectrum calculators. We also explore the effect of the higher order terms (often 2-3 GeV) on the lightest CP even Higgs mass prediction. We illustrate our results in the constrained minimal supersymmetric standard model. Neglecting threshold corrections at the grand unified scale, the discrepancy between the unification scale alpha(s) and the other two unified gauge couplings changes by 0.1% due to the higher order corrections and the difference between unification scale bottom-tau Yukawa couplings neglecting unification scale threshold corrections changes by up to 1%. The difference between unification scale bottom and top Yukawa couplings changes by a few percent. Differences due to the higher order corrections also give an estimate of the size of theoretical uncertainties in the minimal supersymmetric standard model spectrum. We use these to provide estimates of theoretical uncertainties in predictions of the dark matter relic density (which can be of order one due to its strong dependence on sparticle masses) and the LHC sparticle production cross-section (often around 30%). The additional higher order corrections have been incorporated into SOFTSUSY, and we provide details on how to compile and use the program. We also provide a summary of the approximations used in the higher order corrections. Program Summary Nature of problem: Calculating supersymmetric particle spectrum and mixing parameters in the minimal supersymmetric standard model. The solution to the renormalisation group equations must be consistent with boundary conditions on supersymmetry breaking parameters, as well as the weak-scale boundary condition on gauge couplings, Yukawa couplings and the Higgs potential parameters. Program title: SOFTSUSY Catalogue identifier: ADPMv50 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/ADPMv50.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 240528 No. of bytes in distributed program, including test data, etc.: 2597933 Distribution format: tar.gz Programming language: C++, Fortran. Computer: Personal computer. Operating system: Tested on Linux 3.4.6. Word size: 64 bits. Classification: 11.1, 11.6. External routines: At least GiNaC1.3.5 [1] and CLN1.3.1 (both freely obtainable from http://www.ginac.de). Does the new version supersede the previous version?: Yes Catalogue identifier of previous version: ADPMv40 Journal reference of previous version: Comput. Phys. Comm. 185 (2014) 2322 Solution method: Nested iterative algorithm. Reasons for new version: Extension to include additional two and three-loop terms. Summary of revisions: All quantities in the minimal supersymmetric standard model are extended to have three-loop renormalisation group equations (including 3-family mixing) in the limit of real parameters and some leading two-loop threshold corrections are incorporated to the third family Yukawa couplings and the strong gauge coupling. Restrictions: SOFTSUSY will provide a solution only in the perturbative regime and it assumes that all couplings of the model are real (i.e. CP-conserving). If the parameter point under investigation is non-physical for some reason (for example because the electroweak potential does not have an acceptable minimum), SOFTSUSY returns an error message. The higher order corrections included are for the real R-parity conserving minimal supersymmetric standard model (MSSM) only. Running time: A minute per parameter point. The tests provided with the package only take a few seconds to run. |
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Address |
[Allanach, B. C.] Univ Cambridge, DAMTP, CMS, Cambridge CB3 0WA, England, Email: rruiz@ific.uv.es |
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Publisher |
Elsevier Science Bv |
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English |
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ISSN |
0010-4655 |
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Notes |
WOS:000350087300021 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
2142 |
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