| Records |
| 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 |
|
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 |
| Corporate Author |
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Thesis |
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| Publisher |
Edp Sciences S A |
Place of Publication |
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Editor |
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| Language |
English |
Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Issue |
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Edition |
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| ISSN |
0004-6361 |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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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 |
NEXT Collaboration (Simon, A. et al); Gomez-Cadenas, J. J.; Alvarez, V.; Benlloch-Rodriguez, J. M.; Botas, A.; Carcel, S.; Carrion, J.V.; Diaz, J.; Felkai, R.; Ferrario, P.; Laing, A.; Liubarsky, I.; Lopez-March, N.; Martin-Albo, J.; Martinez, A.; Muñoz Vidal, J.; Musti, M.; Nebot-Guinot, M.; Novella, P.; Palmeiro, B.; Perez, J.; Querol, M.; Renner, J.; Rodriguez, J.; Sorel, M.; Torrent, J.; Yahlali, N. |
| Title |
Application and performance of an ML-EM algorithm in NEXT |
Type |
Journal Article |
| Year |
2017 |
Publication |
Journal of Instrumentation |
Abbreviated Journal |
J. Instrum. |
| Volume |
12 |
Issue |
|
Pages |
P08009 - 22pp |
| Keywords |
Gaseous imaging and tracking detectors; Image reconstruction in medical imaging; Time projection Chambers (TPC); Medical-image reconstruction methods and algorithms; computer-aided software |
| Abstract |
The goal of the NEXT experiment is the observation of neutrinoless double beta decay in Xe-136 using a gaseous xenon TPC with electroluminescent amplification and specialized photodetector arrays for calorimetry and tracking. The NEXT Collaboration is exploring a number of reconstruction algorithms to exploit the full potential of the detector. This paper describes one of them: the Maximum Likelihood Expectation Maximization (ML-EM) method, a generic iterative algorithm to find maximum-likelihood estimates of parameters that has been applied to solve many different types of complex inverse problems. In particular, we discuss a bi-dimensional version of the method in which the photosensor signals integrated over time are used to reconstruct a transverse projection of the event. First results show that, when applied to detector simulation data, the algorithm achieves nearly optimal energy resolution (better than 0.5% FWHM at the Q value of 136Xe) for events distributed over the full active volume of the TPC. |
| Address |
[Simon, A.; Gomez-Cadenas, J. J.; Alvarez, V.; Benlloch-Rodriguez, J. M.; Botas, A.; Carcel, S.; Carrion, J. V.; Diaz, J.; Felkai, R.; Ferrario, P.; Laing, A.; Liubarsky, I.; Lopez-March, N.; Martin-Albo, J.; Martinez, A.; Munoz Vidal, J.; Musti, M.; Nebot-Guinot, M.; Novella, P.; Palmeiro, B.; Perez, J.; Querol, M.; Renner, J.; Rodriguez, J.; Sorel, M.; Torrent, J.; Yahlali, N.] CSIC, Inst Fis Corpuscular IFIC, Calle Catedrat Jose Beltran 2, Valencia 46980, Spain, Email: ander.simon@ific.uv.es |
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Thesis |
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| Publisher |
Iop Publishing Ltd |
Place of Publication |
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Editor |
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| Language |
English |
Summary Language |
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Original Title |
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Series Title |
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Abbreviated Series Title |
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Series Issue |
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Edition |
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| ISSN |
1748-0221 |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
WOS:000414159500009 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
3358 |
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| Author |
Muñoz, E.; Barrientos, L.; Bernabeu, J.; Borja-Lloret, M.; Llosa, G.; Ros, A.; Roser, J.; Oliver, J.F. |
| Title |
A spectral reconstruction algorithm for two-plane Compton cameras |
Type |
Journal Article |
| Year |
2020 |
Publication |
Physics in Medicine and Biology |
Abbreviated Journal |
Phys. Med. Biol. |
| Volume |
65 |
Issue |
2 |
Pages |
025011 - 17pp |
| Keywords |
Compton imaging; Compton camera; hadron therapy; image reconstruction |
| Abstract |
One factor limiting the current applicability extent of hadron therapy is the lack of a reliable method for real time treatment monitoring. The use of Compton imaging systems as monitors requires the correct reconstruction of the distribution of prompt gamma productions during patient irradiation. In order to extract the maximum information from all the measurable events, we implemented a spectral reconstruction method that assigns to all events a probability of being either partial or total energy depositions. The method, implemented in a list-mode maximum likelihood expectation maximization algorithm, generates a four dimensional image in the joint spatial-spectral domain, in which the voxels containing the emission positions and energies are obtained. The analytical model used for the system response function is also employed to derive an analytical expression for the sensitivity, which is calculated via Monte Carlo integration. The performance of the method is evaluated through reconstruction of various experimental and simulated sources with different spatial and energy distributions. The results show that the proposed method can recover the spectral and spatial information simultaneously, but only under the assumption of ideal measurements. The analysis of the Monte Carlo simulations has led to the identification of two important degradation sources: the mispositioning of the gamma interaction point and the missing energy recorded in the interaction. Both factors are related to the high energy transferred to the recoil electrons, which can travel far from the interaction point and even escape the detector. These effects prevent the direct application of the current method in more realistic scenarios. Nevertheless, experimental point-like sources have been accurately reconstructed and the spatial distributions and spectral emission of complex simulated phantoms can be identified. |
| Address |
[Munoz, Enrique; Barrientos, Luis; Bernabeu, Jose; Borja-Lloret, Marina; Llosa, Gabriela; Ros, Ana; Roser, Jorge; Oliver, Josep F.] Univ Valencia, CSIC, Inst Fis Corpuscular, Valencia, Spain, Email: Enrique.Munoz@ific.uv.es |
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Thesis |
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| Publisher |
Iop Publishing Ltd |
Place of Publication |
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Editor |
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| Language |
English |
Summary Language |
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Original Title |
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Series Title |
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Abbreviated Series Title |
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Series Issue |
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Edition |
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| ISSN |
0031-9155 |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
WOS:000520111400001 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
no |
| Call Number |
IFIC @ pastor @ |
Serial |
4332 |
| Permanent link to this record |
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| Author |
Domingo-Pardo, C. |
| Title |
A new technique for 3D gamma-ray imaging: Conceptual study of a 3D camera |
Type |
Journal Article |
| Year |
2012 |
Publication |
Nuclear Instruments & Methods in Physics Research A |
Abbreviated Journal |
Nucl. Instrum. Methods Phys. Res. A |
| Volume |
675 |
Issue |
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Pages |
123-132 |
| Keywords |
Gamma-ray detector; Three dimensional gamma-ray imaging; Compton camera; Gamma camera |
| Abstract |
A novel technique for 3D gamma-ray imaging is presented. This method combines the positron annihilation Compton scattering imaging technique with a supplementary position sensitive detector, which registers gamma-rays scattered in the object at angles of about 90 degrees. The 3D coordinates of the scattering location can be determined rather accurately by applying the Compton principle. This method requires access to the object from two orthogonal sides and allows one to achieve a position resolution of few mm in all three space coordinates. A feasibility study for a 3D camera is presented based on Monte Carlo calculations. |
| Address |
Univ Valencia, Inst Fis Corpuscular, CSIC, E-46071 Valencia, Spain, Email: domingo@ific.uv.es |
| Corporate Author |
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Thesis |
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| Publisher |
Elsevier Science Bv |
Place of Publication |
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Editor |
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| Language |
English |
Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
0168-9002 |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
WOS:000302973600019 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
no |
| Call Number |
IFIC @ pastor @ |
Serial |
989 |
| Permanent link to this record |
