| Records |
| Author |
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. |
| Title |
AutoSourceID-FeatureExtractor Optical image analysis using a two-step mean variance estimation network for feature estimation and uncertainty characterisation |
Type |
Journal Article |
| Year |
2023 |
Publication |
Astronomy & Astrophysics |
Abbreviated Journal |
Astron. Astrophys. |
| Volume |
680 |
Issue |
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Pages |
A108 - 14pp |
| Keywords |
astronomical databases: miscellaneous; methods: data analysis; stars: imaging; techniques: image processing |
| 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. |
| 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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| Publisher |
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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yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
5887 |
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| Author |
Beenakker, W.; Caron, S.; Kip, J.; Ruiz de Austri, R.; Zhang, Z. |
| Title |
New energy spectra in neutrino and photon detectors to reveal hidden dark matter signals |
Type |
Journal Article |
| Year |
2023 |
Publication |
Journal of High Energy Physics |
Abbreviated Journal |
J. High Energy Phys. |
| Volume |
11 |
Issue |
11 |
Pages |
028 - 13pp |
| Keywords |
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| Abstract |
Neutral particles capable of travelling cosmic distances from a source to detectors on Earth are limited to photons and neutrinos. Examination of the Dark Matter annihilation/decay spectra for these particles reveals the presence of continuum spectra (e.g. due to fragmentation and W or Z decay) and peaks (due to direct annihilations/decays). However, when one explores extensions of the Standard Model (BSM), unexplored spectra emerge that differ significantly from those of the Standard Model (SM) for both neutrinos and photons. In this paper, we argue for the inclusion of important spectra that include peaks as well as previously largely unexplored entities such as boxes and combinations of box, peak and continuum decay spectra. |
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no |
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yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
6085 |
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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. |
| Title |
AutoSourceID-Classifier Star-galaxy classification using a convolutional neural network with spatial information |
Type |
Journal Article |
| Year |
2023 |
Publication |
Astronomy & Astrophysics |
Abbreviated Journal |
Astron. Astrophys. |
| Volume |
680 |
Issue |
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Pages |
A109 - 16pp |
| Keywords |
methods: data analysis; techniques: image processing; astronomical databases: miscellaneous; stars: imaging; Galaxies: statistics |
| 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. |
| 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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| Publisher |
Edp Sciences S A |
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English |
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0004-6361 |
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| Notes |
WOS:001131898100001 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
5888 |
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| Author |
Arina, C.; Di Mauro, M.; Fornengo, N.; Heisig, J.; Jueid, A.; Ruiz de Austri, R. |
| Title |
CosmiXs: cosmic messenger spectra for indirect dark matter searches |
Type |
Journal Article |
| Year |
2024 |
Publication |
Journal of Cosmology And Astroparticle Physics |
Abbreviated Journal |
J. Cosmol. Astropart. Phys. |
| Volume |
03 |
Issue |
3 |
Pages |
035 - 41pp |
| Keywords |
dark matter experiments; dark matter simulations; dark matter theory |
| Abstract |
The energy spectra of particles produced from dark matter (DM) annihilation or decay are one of the fundamental ingredients to calculate the predicted fluxes of cosmic rays and radiation searched for in indirect DM detection. We revisit the calculation of the source spectra for annihilating and decaying DM using the VINCIA shower algorithm in PYTHIA to include QED and QCD final state radiation and diagrams for the EW corrections with massive bosons, not present in the default PYTHIA shower model. We take into account the spin information of the particles during the entire EW shower and the off -shell contributions from massive gauge bosons. Furthermore, we perform a dedicated tuning of the VINCIA and PYTHIA parameters to LEP data on the production of pions, photons, and hyperons at the Z resonance and discuss the underlying uncertainties. To enable the use of our results in DM studies, we provide the tabulated source spectra for the most relevant cosmic messenger particles, namely antiprotons, positrons, gamma rays and the three neutrino flavors, for all the fermionic and bosonic channels and DM masses between 5 GeV and 100 TeV, on github. |
| Address |
[Arina, Chiara] Catholic Univ Louvain, Ctr Cosmol Particle Phys & Phenomenol CP3, Chemin Cyclotron 2, B-1348 Louvain La Neuve, Belgium, Email: chiara.arina@uclouvain.be; |
| Corporate Author |
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Thesis |
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| Publisher |
IOP Publishing Ltd |
Place of Publication |
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English |
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1475-7516 |
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| Notes |
WOS:001195757300003 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
6041 |
| Permanent link to this record |
