# Revisiting the Emission Line Source Detection Problem in Integral Field Spectroscopic Data

**Authors:** Edmund Christian Herenz  
**arXiv:** [2212.00662](https://arxiv.org/abs/2212.00662)  
**Date:** 2022-12-01  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2212.00662v2/

## Key points

- The authors present a 3-dimensional matched filtering approach for detecting faint emission-line sources in integral-field spectroscopic datasets.
- The filter accounts for rapidly varying background noise caused by the telluric air glow spectrum, which has previously hindered accurate detection of faint sources.
- The new filter design provides higher detection significances for faint emission line sources buried in between atmospheric [OH]-bands at $\lambda \gtrsim 7000$\,\AA{} using public data from the MUSE-Wide survey.
- For a given source parameterisation, the selection function of the improved algorithm can be derived analytically from the variances of the data and verified against source insertion and recovery experiments in the recently released dataset of the MUSE eXtreme Deep Field (MXDF).
- The selection function can be re-scaled for 3D emission line source profiles that are not fully congruent with the template, removing computational burdens associated with constructing realistic selection functions.
- This work presents a significant improvement to existing methods for detecting faint emission-line sources in integral-field spectroscopic datasets.

## Summary

In this paper, the authors present a novel 3-dimensional matched filtering approach for detecting faint emission-line sources in integral-field spectroscopic datasets. The filter is designed to account for the rapidly varying background noise caused by the telluric air glow spectrum, which has previously hindered accurate detection of faint sources. The authors implement this new matched filtering search in an updated version of the Line Source Detection Cataloguing tool (LSDCat2.0). Using public data from the MUSE-Wide survey, they demonstrate that their new filter design provides higher detection significances for faint emission line sources buried in between atmospheric [OH]-bands at $\lambda \gtrsim 7000$\,\AA{}. They also show how, for a given source parameterisation, the selection function of the improved algorithm can be derived analytically from the variances of the data. This analytic solution is verified against source insertion and recovery experiments in the recently released dataset of the MUSE eXtreme Deep Field (MXDF). Furthermore, they illustrate how their selection function can be re-scaled for 3D emission line source profiles that are not fully congruent with the template. This procedure alleviates computational burdens associated with constructing realistic selection functions by removing the need for computationally cumbersome source insertion and recovery experiments. Overall, this work presents a significant improvement to existing methods for detecting faint emission-line sources in integral-field spectroscopic datasets. The new matched filtering approach accounts for previously problematic background noise and provides higher detection significances while reducing computational complexity associated with constructing realistic selection functions.