Slitless spectrophotometry with forward modelling: principles and application to atmospheric transmission measurement

TL;DR

This paper presents a forward modelling approach using slitless spectrophotometry to accurately measure atmospheric transmission, aiming to improve photometric calibration for dark energy surveys like LSST.

Contribution

It introduces a novel forward model and software for slitless spectrophotometry to monitor atmospheric transmission, enhancing calibration precision in optical surveys.

Findings

Model accurately interprets spectrophotometric exposures

Software successfully applied to real and simulated data

Potential for real-time atmospheric monitoring in surveys

Abstract

In the next decade, many optical surveys will aim to tackle the question of dark energy nature, measuring its equation of state parameter at the permil level. This requires trusting the photometric calibration of the survey with a precision never reached so far, controlling many sources of systematic uncertainties. The measurement of the on-site atmospheric transmission for each exposure, or on average for each season or for the full survey, can help reach the permil precision for magnitudes. This work aims at proving the ability to use slitless spectroscopy for standard star spectrophotometry and its use to monitor on-site atmospheric transmission as needed, for example, by the Vera C. Rubin Observatory Legacy Survey of Space and Time supernova cosmology program. We fully deal with the case of a disperser in the filter wheel, which is the configuration chosen in the Rubin Auxiliary Telescope. The theoretical basis of slitless spectrophotometry is at the heart of our forward model approach to extract spectroscopic information from slitless data. We developed a publicly available software called Spectractor (https://github.com/LSSTDESC/Spectractor) that implements each ingredient of the model and finally performs a fit of a spectrogram model directly on image data to get the spectrum. We show on simulations that our model allows us to understand the structure of spectrophotometric exposures. We also demonstrate its use on real data, solving specific issues and illustrating how our procedure allows the improvement of the model describing the data. Finally, we discuss how this approach can be used to directly extract atmospheric transmission parameters from data and thus provide the base for on-site atmosphere monitoring. We show the efficiency of the procedure on simulations and test it on the limited data set available.