Speaker
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, slit-less 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, then we match the spectrogram resolution and wavelength range to that of Euclid, and then we will emulate the spectral overlap artificially. We will modify our pipeline at each step to increase the complexity that can be handled. While this work 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.