Accurate photometric calibration by fitting the system transmission

Department of Particle Physics and Astrophysics, Weizmann Institute of Science, 76100 Rehovot, Israel

^★ Corresponding authors: simone.garrappa@weizmann.ac.il; eran.ofek@weizmann.ac.il

Received: 13 December 2024
Accepted: 9 May 2025

Abstract

Context. Transforming the instrumental photometry of ground-based telescopes into a calibrated physical flux in a well-defined passband is a major challenge in astronomy. Along with the intrinsic instrumental difference between telescopes sharing the same filter, the effective transmission is continuously modified by the effects of the variable atmosphere of the Earth.

Aims. We have developed a new approach to the absolute photometric calibration (i.e., tied to the CALSPEC standards) that simultaneously treats instrumental and atmospheric effects on an image-by-image basis by fitting the system transmission. This approach aims at breaking the 1% absolute photometric accuracy which limits current calibration methods for ground-based observatories.

Methods. We fit the transmission, as a function of wavelength, for each image. The fit is done by comparing the instrumental fluxes of stars in the image to the synthetic photometry of the stars given their spectrum and the transmission function which have free parameters. A key element that enables this approach is the set of about 220 million low-resolution spectra measured by Gaia , which provides a large number of stellar calibrators in the image that are calibrated against the CALSPEC scale.

Results. We demonstrate the method using data from the Large Array Survey Telescope (LAST). We show that the residuals between observations and synthetic photometry of the Gaia spectra in the fitted transmission have a standard deviation <1% on an image-by-image basis, with no spatial and color dependencies. The median accuracy of the zero-point throughout the image is between 3-5 mmag, depending on the total image exposure. Furthermore we show that this method provides high stability over long temporal scales.