TY - JOUR
T1 - The PAU survey
T2 - background light estimation with deep learning techniques
AU - Cabayol-Garcia, L.
AU - Eriksen, M.
AU - Alarcón, A.
AU - Amara, A.
AU - Carretero, J.
AU - Casas, R.
AU - Castander, F. J.
AU - Fernández, E.
AU - García-Bellido, J.
AU - Gaztanaga, E.
AU - Hoekstra, H.
AU - Miquel, R.
AU - Neissner, C.
AU - Padilla, C.
AU - Sánchez, E.
AU - Serrano, S.
AU - Sevilla-Noarbe, I.
AU - Siudek, M.
AU - Tallada, P.
AU - Tortorelli, L.
N1 - Funding Information:
Funding for PAUS has 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) and University College London. The PAUS participants from Spanish institutions are partially supported by MINECO under grants CSD2007-00060, AYA2015-71825, ESP2015-88861, FPA2015-68048, SEV-2016-0588, SEV-2016-0597, and MDM-2015-0509, some of which include ERDF funds from the European Union. IEEC and IFAE are partially funded by the CERCA program of the Generalitat de Catalunya. The PAU data center is hosted by the Port d?Informaci? Cient?fica (PIC), maintained through a collaboration of CIEMAT and IFAE, with additional support from Universitat Aut?noma de Barcelona and ERDF. CosmoHub has been developed by PIC and was partially funded by the ?Plan Estatal de Investigaci?n Cient?fica y T?cnica y de Innovaci?n? program of the Spanish government. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan V GPU used for this research. This project has received funding from the European Union?s Horizon 2020 research and innovation programme under grant agreement No 776247. AA is supported by a Royal Society Wolfson Fellowship. MS has been supported by the National Science Centre (grant UMO-2016/23/N/ST9/02963).
Publisher Copyright:
© 2019 The Author(s).
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/2/1
Y1 - 2020/2/1
N2 - In any imaging survey, measuring accurately the astronomical background light is crucial to obtain good photometry. This paper introduces BKGNET, a deep neural network to predict the background and its associated error. BKGNET has been developed for data from the Physics of the Accelerating Universe Survey (PAUS), an imaging survey using a 40 narrow-band filter camera (PAUCam). The images obtained with PAUCam are affected by scattered light: an optical effect consisting of light multiply reflected that deposits energy in specific detector regions affecting the science measurements. Fortunately, scattered light is not a random effect, but it can be predicted and corrected for. We have found that BKGNET background predictions are very robust to distorting effects, while still being statistically accurate. On average, the use of BKGnet improves the photometric flux measurements by 7 per cent and up to 20 per cent at the bright end. BKGNET also removes a systematic trend in the background error estimation with magnitude in the i band that is present with the current PAU data management method. With BKGNET, we reduce the photometric redshift outlier rate by 35 per cent for the best 20 per cent galaxies selected with a photometric quality parameter.
AB - In any imaging survey, measuring accurately the astronomical background light is crucial to obtain good photometry. This paper introduces BKGNET, a deep neural network to predict the background and its associated error. BKGNET has been developed for data from the Physics of the Accelerating Universe Survey (PAUS), an imaging survey using a 40 narrow-band filter camera (PAUCam). The images obtained with PAUCam are affected by scattered light: an optical effect consisting of light multiply reflected that deposits energy in specific detector regions affecting the science measurements. Fortunately, scattered light is not a random effect, but it can be predicted and corrected for. We have found that BKGNET background predictions are very robust to distorting effects, while still being statistically accurate. On average, the use of BKGnet improves the photometric flux measurements by 7 per cent and up to 20 per cent at the bright end. BKGNET also removes a systematic trend in the background error estimation with magnitude in the i band that is present with the current PAU data management method. With BKGNET, we reduce the photometric redshift outlier rate by 35 per cent for the best 20 per cent galaxies selected with a photometric quality parameter.
KW - Instrumentation: photometers
KW - Light pollution
KW - Techniques: photometric
UR - http://www.scopus.com/inward/record.url?scp=85096993929&partnerID=8YFLogxK
U2 - 10.1093/mnras/stz3274
DO - 10.1093/mnras/stz3274
M3 - Article
AN - SCOPUS:85096993929
SN - 0035-8711
VL - 491
SP - 5392
EP - 5405
JO - Monthly Notices of the Royal Astronomical Society
JF - Monthly Notices of the Royal Astronomical Society
IS - 4
ER -