# Characterization and Photometric Performance of the Hyper Suprime-Cam Software Pipeline

## TL;DR

This paper evaluates the photometric accuracy of the Hyper Suprime-Cam pipeline using synthetic galaxies injected into real images, revealing strengths and limitations in star and galaxy photometry at various magnitudes.

## Contribution

It introduces SynPipe, a Python framework for synthetic galaxy injection, and assesses the HSC pipeline's photometric performance and biases at different depths.

## Key findings

- Achieves 1% photometric precision for stars at i~19.0 mag
- Attains 13% photometric precision for synthetic galaxies at i~20.0 mag
- Identifies blending and classification issues affecting photometry and morphology measurements

## 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~26.0 mag. 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~19.0 mag and 6% precision at i~25.0 in the i-band. For synthetic galaxies with single-Sersic profiles, forced CModel photometry achieves 13% photometric precision at i~20.0 mag and 18% precision at i~25.0 in the i-band. We show that both forced PSF 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. Second, >20% of stars at 22.5< i < 25.0 mag can be misclassified as extended objects. Third, the current CModel algorithm tends to strongly underestimate the half-light radius and ellipticity of galaxy with i>21.5 mag.