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.

翻译：近红外（NIR）探测器——通过非破坏性读出方式测量像素级时间序列计数——在现代天体物理学中发挥着关键作用。标准的近红外通量提取技术是为天基观测开发的，其假设观测期间源通量保持恒定。然而，地基望远镜常会遇到短时标的大气变化，这种变化会显著改变到达像素的光子数量。本研究提出了一种新的统计模型，该模型通过共享相邻光谱像素间的信息来表征时变观测数据，从而提取具有最优不确定度的无偏通量。我们使用多种通量条件和时变幅度条件生成逼真的合成数据，以验证我们的模型能够无偏且最优地估计真实通量和时变信号。我们发现，当观测计数率变化超过3.5%时，时变模型应优于恒定通量模型。忽略数据中的时间变异性可能导致与通量相关的、符号未知的偏差，其大小可达通量不确定度的约120%。通过分析真实的APOGEE光谱数据，我们发现了计数率存在近似波长无关、时间依赖变化的经验证据，其变化幅度远超过3.5%的阈值。我们的模型能够稳健地测量并消除真实数据中的时间依赖性，从而提升数据与模型比较的质量。我们展示了多个实例，其中观测到的时间依赖性与观测条件（如变化的云量和视宁度）的独立测量结果在定量上吻合。