1. The PAU survey: estimating galaxy photometry with deep learning
- Author
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J. Carretero, Ramon Miquel, Martin Eriksen, F. J. Castander, E. J. Sanchez, Ricard Casas, Enrique Gaztanaga, I. Sevilla-Noarbe, S. Serrano, Hendrik Hildebrandt, C. Padilla, P. Tallada-Crespí, Enrique Fernández, Laura Cabayol, Juan Garcia-Bellido, J. De Vicente, Adam Amara, Ministerio de Ciencia, Innovación y Universidades (España), Ministerio de Economía y Competitividad (España), European Commission, Generalitat de Catalunya, Durham University, Netherlands Organization for Scientific Research, German Research Foundation, and University College London
- Subjects
Cosmology and Nongalactic Astrophysics (astro-ph.CO) ,Aperture ,FOS: Physical sciences ,Techniques: image processing ,Probability density function ,Astrophysics ,Astrophysics::Cosmology and Extragalactic Astrophysics ,01 natural sciences ,Photometry (optics) ,Background noise ,Observatory ,0103 physical sciences ,image processing [Techniques] ,Astrophysics::Solar and Stellar Astrophysics ,observations [Cosmology] ,010303 astronomy & astrophysics ,Astrophysics::Galaxy Astrophysics ,Physics ,010308 nuclear & particles physics ,photometry [Galaxies] ,photometric [Techniques] ,Cosmology: observations ,Astrophysics::Instrumentation and Methods for Astrophysics ,Astronomy and Astrophysics ,Galaxies: photometry ,Galaxy ,Redshift ,Space and Planetary Science ,Outlier ,Techniques: photometric ,Astrophysics - Cosmology and Nongalactic Astrophysics - 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 per cent, comparing to aperture photometry. Furthermore, with LUMOS photometry, the photo-z scatter is reduced by ≈10 per cent with the Deepz machine-learning photo-z code and the photo-z outlier rate by 20 per cent. 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., The PAU Survey is partially supported by MINECO under grants CSD2007-00060, AYA2015-71825, ESP2017-89838, PGC2018-094773, PGC2018-102021, SEV-2016-0588, SEV-2016-0597, MDM-2015-0509, PID2019-111317GB-C31 and Juan de la Cierva fellowship and LACEGAL and EWC Marie Sklodowska-Curie 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 StG ADULT-279396 and Netherlands Organisation for Scientific Research (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 the Royal Society Wolfson fellowship and from the European Union’s Horizon 2020 research and innovation programme under the grant agreement No 776247 EWC.
- Published
- 2021