# On the optimal calibration of VVV photometry

## TL;DR

This paper identifies and corrects key biases in the VVV survey's photometric calibration, improving data accuracy by revising calibration procedures to address sky background effects and stellar blending issues.

## Contribution

It introduces simple modifications to the standard calibration process that effectively eliminate biases caused by sky background and star blending in dense regions.

## Key findings

- Biases in zero-points due to sky background and star blending are significant.
- Revised calibration procedures improve the accuracy and reliability of VVV photometry.
- Preliminary corrections enhance data quality for current and future analyses.

## Abstract

Prompted by some inconsistencies in the photometry of the VISTA Variables in the V\'ia L\'actea (VVV) survey, we conduct a revision of the standard calibration procedure of VISTA data in the $J$, $H$, and $K_S$ passbands. Two independent sources of bias in the photometric zero-points are identified: First, high sky backgrounds severely affect the $H$-band measurements, but this can mostly be minimized by strict data vetting. Secondly, during the zero-point calibration, stars serving as standards are taken from the 2MASS catalog, which can suffer from high degrees of blending in regions of high stellar density, affecting both the absolute photometric calibration, as well as the scatter of repeated observations. The former affects studies that rely on an absolute magnitude scale, while the latter can also affect the shapes and amount of scatter in the VVV light curves, thus potentially hampering their proper classification. We show that these errors can be effectively eliminated by relatively simple modifications of the standard calibration procedure, and demonstrate the effect of the recalibration on the VVV survey's data quality. We give recommendations for future improvements of the pipeline calibration of VISTA photometry, while also providing preliminary corrections to the VVV $JHK_\mathrm{S}$ observations as a temporary measure.