Measuring the Variability of Hydroxyl Emissions in Infrared Sky Spectra using SPIRou

Subtracting the changing sky contribution from the near infrared (NIR) spectra of faint astronomical objects is challenging and crucial to a wide range of science cases. Since the sky background varies with time and location, NIR spectral observations, especially those employing fiber spectrometers and targeting extended sources, require frequent sky-only observations. However, sky subtraction can be optimized with sufficient a priori knowledge of the sky's variability. In this work, we analyze 1075 high resolution NIR spectra from the CFHT's SPIRou on Maunakea to estimate the variability as a function of time of 481 hydroxyl (OH) lines. These spectra were collected over $\approx$3.5,years, and included two sets of three nights dedicated to obtaining sky observations every five minutes. We identify 169 spectral doublet candidates, or OH lines that split into at least two components. We employ the Lomb-Scargle Periodogram method to confirm that most OH lines vary on similar timescales. In addition, we suggest that for stable conditions, which can be assessed with a separate observing method, or a minimum of fibers in the case of multi-fiber NIR spectrograph, sky measurements may be taken at a frequency of every 25 minutes to achieve satisfactory correction. Finally, we discuss potential machine learning methods for measuring the sky at any given time, specifically Gaussian process regression and various neural network models.