Advanced Data Reduction Techniques for MUSE

TL;DR

This paper presents a novel data reduction scheme for the MUSE instrument that minimizes resampling steps, improving data quality and noise propagation in integral field spectroscopy.

Contribution

It introduces a single-resampling data processing approach for MUSE, enhancing data quality and artifact minimization compared to traditional methods.

Findings

Improved data quality through reduced resampling.

Accurate variance propagation achieved.

Minimized artifacts and correlated noise.

Abstract

MUSE, a 2nd generation VLT instrument, will become the world's largest integral field spectrograph. It will be an AO assisted instrument which, in a single exposure, covers the wavelength range from 465 to 930 nm with an average resolution of 3000 over a field of view of 1'x1' with 0.2'' spatial sampling. Both the complexity and the rate of the data are a challenge for the data processing of this instrument. We will give an overview of the data processing scheme that has been designed for MUSE. Specifically, we will use only a single resampling step from the raw data to the reduced data product. This allows us to improve data quality, accurately propagate variance, and minimize spreading of artifacts and correlated noise. This approach necessitates changes to the standard way in which reduction steps like wavelength calibration and sky subtraction are carried out, but can be expanded to include combination of multiple exposures.