Optimal and Unbiased Fluxes from Up-the-Ramp Detectors under Variable Illumination

TL;DR

This paper introduces a statistical model for extracting unbiased fluxes from near-infrared detectors under variable atmospheric conditions, improving accuracy over traditional methods that assume constant flux.

Contribution

The work develops a new spectral pixel-sharing model that accounts for time-variable signals, enabling unbiased flux estimation in ground-based NIR observations.

Findings

Model recovers unbiased flux and variability estimates in synthetic data.

Favored over constant-flux models when count rates vary by more than 3.5%.

Real data shows significant wavelength-independent time variability matching observing conditions.

Abstract

Near-infrared (NIR) detectors -- which use non-destructive readouts to measure time-series counts-per-pixel -- play a crucial role in modern astrophysics. Standard NIR flux extraction techniques were developed for space-based observations and assume that source fluxes are constant over an observation. However, ground-based telescopes often see short-timescale atmospheric variations that can dramatically change the number of photons arriving at a pixel. This work presents a new statistical model that shares information between neighboring spectral pixels to characterize time-variable observations and extract unbiased fluxes with optimal uncertainties. We generate realistic synthetic data using a variety of flux and amplitude-of-time-variability conditions to confirm that our model recovers unbiased and optimal estimates of both the true flux and the time-variable signal. We find that the time-variable model should be favored over a constant-flux model when the observed count rates change by more than 3.5%. Ignoring time variability in the data can result in flux-dependent, unknown-sign biases that are as large as ~120% of the flux uncertainty. Using real APOGEE spectra, we find empirical evidence for approximately wavelength-independent, time-dependent variations in count rates with amplitudes much greater than the 3.5% threshold. Our model can robustly measure and remove the time-dependence in real data, improving the quality of data-model comparison. We show several examples where the observed time-dependence quantitatively agrees with independent measurements of observing conditions, such as variable cloud cover and seeing.