Uncertainties in Low-Count STIS Spectra

TL;DR

This paper examines the limitations of traditional uncertainty calculations in low-count UV spectra from STIS, introduces improved methods for accurate Poisson-based uncertainty estimation, and discusses software updates and bug fixes relevant to data analysis.

Contribution

It presents a new utility for calculating Poisson confidence intervals in low-count regimes and discusses software improvements for accurate uncertainty estimation in STIS data.

Findings

The root-N approximation fails in low-count regimes.

A new utility, stistools.poisson_err, provides accurate Poisson confidence intervals.

A software bug in stistools.inttag was identified and fixed, improving data analysis accuracy.

Abstract

We evaluate uncertainty calculations in the calstis pipeline for data in the low-count regime. Due to the low dark rate and read-noise free nature of MAMA detectors, observations of UV-dim sources can result in exposures with 0 or 1 counts in some pixels. In this regime, the "root-N" approximation widely used to calculate uncertainties breaks down, and one must compute Poisson confidence intervals for more accurate uncertainty calculations. The CalCOS pipeline was updated in 2020 to account for these low-count uncertainties. Here, we assess how STIS observations are currently affected by this phenomenon, describe a new Jupyter notebook exploring the issue, and introduce a new utility, stistools.poisson_err, to manually calculate Poisson confidence intervals for 1D STIS spectra. Additionally, we describe a related software bug in the stistools$.$inttag utility, which splits TIME-TAG data into sub-exposures. This newly fixed bug serves as a useful case-study for the proper use of Poisson confidence intervals.