Abstract
The Subaru Strategic Program (SSP) is an ambitious multi-band survey using the Hyper Suprime-Cam (HSC) on the Subaru telescope. The Wide layer of the SSP is both wide and deep, reaching a detection limit of i ∼ 26.0mag. At these depths, it is challenging to achieve accurate, unbiased, and consistent photometry across all five bands. The HSC data are reduced using a pipeline that builds on the prototype pipeline for the Large Synoptic Survey Telescope. We have developed a Python-based, flexible framework to inject synthetic galaxies into real HSC images, called SynPipe. Here we explain the design and implementation of SynPipe and generate a sample of synthetic galaxies to examine the photometric performance of the HSC pipeline. For stars, we achieve 1% photometric precision at i ∼ 19.0mag and 6% precision at i ∼ 25.0 in the i band (corresponding to statistical scatters of ∼0.01 and ∼0.06mag respectively). For synthetic galaxies with single-Sersic profiles, forced CModel photometry achieves 13% photometric precision at i ∼ 20.0mag and 18% precision at i ∼ 25.0 in the i band (corresponding to statistical scatters of ∼0.15 and ∼0.22mag respectively). We show that both forced point spread function and CModel photometry yield unbiased color estimates that are robust to seeing conditions.We identify several caveats that apply to the version of HSC pipeline used for the first public HSC data release (DR1) that need to be taking into consideration. First, the degree to which an object is blended with other objects impacts the overall photometric performance. This is especially true for point sources. Highly blended objects tend to have larger photometric uncertainties, systematically underestimated fluxes, and slightly biased colors. Secondly, >20% of stars at 22.5 < i < 25.0mag can be misclassified as extended objects. Thirdly, the current CModel algorithm tends to strongly underestimate the half-light radius and ellipticity of galaxy with i > 21.5mag.
Original language | English (US) |
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Article number | S6 |
Journal | Publications of the Astronomical Society of Japan |
Volume | 70 |
Issue number | Special Issue 1 |
DOIs | |
State | Published - Jan 1 2018 |
All Science Journal Classification (ASJC) codes
- Astronomy and Astrophysics
- Space and Planetary Science
Keywords
- Methods
- Observational-surveys-techniques
- Photometric