Orateur
Description
With telescopes like the Euclid in orbit and CSST in the works, we are looking at an emerging era of astronomy with surveys producing millions and millions of slitless spectrograms. While the analytical extraction techniques are sufficient for the current amount of data, the community will require faster pipelines for future data releases. Added to the large amount of expected data, slitless spectroscopy, as executed by instruments such as Euclid/NISP, presents significant challenges for data processing — most notably spectral contamination arising from the overlap of multiple dispersed sources on the detector.
To address these difficulties, we are developing a machine learning approach aimed at extracting and decontaminating 1D spectra directly from real flight 2D spectrograms. Our strategy proceeds incrementally; as a first step, we develop and validate a pipeline to extract 1D spectra from 2D JWST slit spectroscopic data. In the subsequent stages, we will match the spectrogram resolution and wavelength range to that of Euclid, we will emulate the spectral overlap artificially to then train a model to undo said overlap, we will automate the identification of first orders in the NISP spectroscopic images, and finally test the end-to-end pipeline on real Euclid data.
In the first stage, we train a 3-branch attention-inception CNN that can estimate 1D spectra from 2D spectrograms with no information about observed wavelengths or resolution. Each branch estimates different parts of the spectrum, like the continuum, the continuum-free component and refinements. To achieve this performance, we divide spectrograms into smaller segments, allowing the network to learn the 2D-1D mapping with no regard for object properties, wavelength or resolution.
While the high-level goal is tailored for Euclid data, the strategies used in this work will serve as a blueprint for any future slitless or even slit spectroscopic surveys. This generality makes this work a valuable asset in the coming era of large-scale, slitless and highly multiplexed spectroscopic surveys, where efficient and robust automated extraction pipelines will be essential.