The VISTA ZYJHKs Photometric System: Calibration from 2MASS
Monthly Notices of the Royal Astronomical Society(2017)
摘要
In this paper, we describe the routine photometric calibration of data taken with the VISTA infrared camera (VIRCAM) instrument on the ESO Visible and Infrared Survey Telescope for Astronomy (VISTA) telescope. The broad-band ZYJHKs data are directly calibrated from Two Micron all Sky Survey (2MASS) point sources visible in every VISTA image. We present the empirical transformations between the 2MASS and VISTA, and Wide-Field Camera and VISTA, photometric systems for regions of low reddening. We investigate the long-term performance of VISTA+VIRCAM. An investigation of the dependence of the photometric calibration on interstellar reddening leads to these conclusions: (1) For all broad-band filters, a linear colour-dependent correction compensates the gross effects of reddening where E(B - V) < 5.0. (2) For Z and Y, there is a significantly larger scatter above E(B - V) = 5.0, and insufficient measurements to adequately constrain the relation beyond this value. (3) The JHKs filters can be corrected to a few per cent up to E(B - V) = 10.0. We analyse spatial systematics over month-long time-scales, both inter-and intradetector and show that these are present only at very low levels in VISTA. We monitor and remove residual detector-to-detector offsets. We compare the calibration of the main pipeline products: pawprints and tiles. We show how variable seeing and transparency affect the final calibration accuracy of VISTA tiles, and discuss a technique, grouting, for mitigating these effects. Comparison between repeated reference fields is used to demonstrate that the VISTA photometry is precise to better than similar or equal to 2 per cent for the YJHKs bands and 3 per cent for the Z bands. Finally, we present empirically determined offsets to transform VISTA magnitudes into a true Vega system.
更多查看译文
关键词
methods: data analysis,surveys,infrared: general
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要