New methods to improve the decontamination of slitless spectra

TL;DR

The paper tackles contamination in slitless spectroscopy by casting decontamination as a blind source separation problem. It develops two local modeling frameworks—a linear instantaneous model and a convolutive model in the Fourier domain—each paired with four methods that jointly exploit multi-directional dispersion data ($d_i \in \{0^\circ,180^\circ,184^\circ,-4^\circ\}$) and direct photometric images. The main contributions are (i) mixing-matrix and source-matrix estimation strategies for both local models, (ii) three LS/beamforming-based variants (LI-LSQ, LI-LSQN, LI-MPDR) and one convolutive approach (LC-LCMP) that can decontaminate while optionally deconvolving spectra, and (iii) extensive testing on realistic Euclid-like data showing that LC-LCMP generally yields the best decontamination performance across scenarios, including hot pixels. The results demonstrate robust, parallelizable, and multi-directional decontamination capabilities that can be extended to real mission data and generalized to broader slitless spectroscopy setups, with potential improvements from incorporating additional photometric bands and wavelength-dependent PSF effects.

Abstract

This paper proposes four new methods to decontaminate spectra of stars and galaxies resulting from slitless spectroscopy used in many space missions such as Euclid. These methods are based on two distinct approaches and simultaneously take into account multiple dispersion directions of light. The first approach, called the local instantaneous approach, is based on an approximate linear instantaneous model. The second approach, called the local convolutive approach, is based on a more realistic convolutive model that allows simultaneous decontamination and deconvolution of spectra. For each approach, a mixing model was developed that links the observed data to the source spectra. This was done either in the spatial domain for the local instantaneous approach or in the Fourier domain for the local convolutive approach. Four methods were then developed to decontaminate these spectra from the mixtures, exploiting the direct images provided by photometers. Test results obtained using realistic, noisy, Euclid-like data confirmed the effectiveness of the proposed methods.

Figures (12)

• Figure 1: Contamination of the spectra of neighboring objects at the output of the grism.
• Figure 2: 's observation strategy.
• Figure 3: Flux distribution of the direct image and the uncontaminated spectrogram of an object.
• Figure 4: Mixing coefficient estimation from direct images.
• Figure 5: Observed spectrograms (left) and corresponding 1D spectra (right) for the first scenario.