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The PAU Survey is partially supported by MINECO under grants CSD2007-00060, AYA2015-71825, ESP2017-89838, PGC2018-094773, PGC2018-102021, SEV-2016-0588, SEV-20160597, MDM-2015-0509, PID2019-111317GB-C31 and Juan de la Cierva fellowship and LACEGAL and EWC Marie SklodowskaCurie grant No 734374 and no.776247 with ERDF funds from the EU Horizon 2020 Programme, some of which include ERDF funds from the European Union. IEEC and IFAE are partially funded by the CERCA and Beatriu de Pinos program of the Generalitat de Catalunya. Funding for PAUS has also been provided by Durham University (via the ERC StG DEGAS-259586), ETH Zurich, Leiden University (via ERC StGADULT-279396 and Netherlands Organisation for ScientificResearch(NWO) Vici grant 639.043.512), Bochum University (via a Heisenberg grant of the Deutsche Forschungsgemeinschaft (Hi 1495/5-1) as well as an ERC Consolidator Grant (No. 770935)), University College London, Portsmouth support through theRoyal SocietyWolfson fellowship and from the European Union's Horizon 2020 research and innovation programme under the grant agreement No 776247 EWC.The PAU data centre is hosted by the Port d'Informaci ' o Cient ' ifica (PIC), maintained through a collaboration of CIEMAT and IFAE, with additional support from Universitat Aut`onoma de Barcelona and ERDF. We acknowledge the PIC services department team for their support and fruitful discussions. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan V GPU used for this research.

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The PAU survey: estimating galaxy photometry with deep learning

Publicated to:MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY. 506 (3): 4048-4069 - 2021-09-01 506(3), DOI: 10.1093/mnras/stab1909

Authors: Cabayol, L; Eriksen, M; Carretero, J; Fernández, E; Miquel, R; Padilla, C; Amara, A; Casas, R; Castander, FJ; Gaztanaga, E; Serrano, S; Sevilla-Noarbe,; De Vicente, J; Sánchez, E; Tallada-Crespí, P; García-Bellido, J; Hildebrandt, H

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Abstract

With the dramatic rise in high-quality galaxy data expected from Euclid and Vera C. Rubin Observatory, there will be increasing demand for fast high-precision methods for measuring galaxy fluxes. These will be essential for inferring the redshifts of the galaxies. In this paper, we introduce Lumos, a deep learning method to measure photometry from galaxy images. Lumos builds on BKGNET, an algorithm to predict the background and its associated error, and predicts the background-subtracted flux probability density function. We have developed LUMOS for data from the Physics of the Accelerating Universe Survey (PAUS), an imaging survey using a 40 narrow-band filter camera (PAUCam). PAUCam images are affected by scattered light, displaying a background noise pattern that can be predicted and corrected for. On average, Lumos increases the SNR of the observations by a factor of 2 compared to an aperture photometry algorithm. It also incorporates other advantages like robustness towards distorting artefacts, e.g. cosmic rays or scattered light, the ability of deblending and less sensitivity to uncertainties in the galaxy profile parameters used to infer the photometry. Indeed, the number of flagged photometry outlier observations is reduced from 10 to 2 percent, comparing to aperture photometry. Furthermore, with LUMOS photometry, the photo-z scatter is reduced by approximate to 10 percent with the Deepz machine-learning photo-z code and the photo-z outlier rate by 20 percent. The photo-z improvement is lower than expected from the SNR increment, however, currently the photometric calibration and outliers in the photometry seem to be its limiting factor.

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