Three-dimensional scene reconstruction using Roman slitless spectra

TL;DR

This paper introduces a novel 3D scene reconstruction algorithm for slitless spectra, enabling accurate removal of host galaxy contamination in supernova observations, which improves dark energy research.

Contribution

The paper presents a new scene reconstruction method that uses multiple observations at different angles to accurately model and subtract host galaxy light from supernova spectra.

Findings

Method achieves minimal systematic errors in simulations.

Significantly reduces noise compared to traditional subtraction methods.

Enhances accuracy of supernova spectral analysis for dark energy studies.

Abstract

The Nancy Grace Roman Space Telescope will carry out a wide-field imaging and slitless spectroscopic survey of Type Ia Supernovae to improve our understanding of dark energy. Crucial to this endeavor is obtaining supernova spectra uncontaminated by light from their host galaxies. However, obtaining such spectra is made more difficult by the inherent problem in wide-field slitless spectroscopic surveys: the blending of spectra of close objects. The spectrum of a supernova will blend with the host galaxy, even from regions distant from the supernova on the sky. If not properly removed, this contamination will introduce systematic bias when the supernova spectra are later used to determine intrinsic supernova parameters and to infer the parameters of dark energy. To address this problem we developed an algorithm that makes use of the spectroscopic observations of the host galaxy at all available observatory roll angles to reconstruct a three-dimensional (3d; 2d spatial, 1d spectral) representation of the underlying host galaxy that accurately matches the 2d slitless spectrum of the host galaxy when projected to an arbitrary rotation angle. We call this ``scene reconstruction''. The projection of the reconstructed scene can be subtracted from an observation of a supernova to remove the contamination from the underlying host. Using simulated Roman data, we show that our method has extremely small systematic errors and significantly less random noise than if we subtracted a single perfectly aligned spectrum of the host obtained before or after the supernova was visible.