Skycorr: A general tool for spectroscopic sky subtraction

Abstract

Context. Airglow emission lines, which dominate the optical-to-near-infrared sky radiation, show strong, line-dependent variability on time scales from minutes to decades. Therefore, the subtraction of the sky background in the affected wavelength regime becomes a problem if plain-sky spectra have to be taken at a different time from the astronomical data. Aims. A solution of this problem is the physically motivated scaling of the airglow lines in the plain-sky data to fit the sky lines in the object spectrum. We have developed a corresponding instrument-independent approach based on one-dimensional spectra. Methods. Our code skycorr separates sky lines and sky/object continuum by an iterative approach involving a line finder and airglow line data. The sky lines, which mainly belong to OH and O 2 bands, are grouped according to their expected variability. The line groups in the sky data are then scaled to fit the sky in the science data. Required pixel-specific weights for overlapping groups are taken from a comprehensive airglow model. Deviations in the wavelength calibration are corrected for by fitting Chebyshev polynomials and rebinning via asymmetric damped sinc kernels. The scaled sky lines and the sky continuum are subtracted separately. Results. ESO-VLT X-shooter data covering 2.5 h with a good time resolution were selected to illustrate the performance. Data taken six nights and about one year before were also used as reference sky data. The variation of the sky-subtraction quality as a function of time difference between the object and sky data depends on changes in the airglow intensity, atmospheric transparency, and instrument calibration. Except for short time intervals of a few minutes, the sky line residuals were between 2.1 and 5.5 times weaker than for sky subtraction without fitting. Additional tests showed that skycorr performs consistently better than the method of Davies (2007, MNRAS, 375, 1099 developed for ESO-VLT SINFONI data.

THE DATA REDUCTION PIPELINE FOR THE SDSS-IV MaNGA IFU GALAXY SURVEY

“…Another way to check the sky subtraction quality of the DRP is to compare its performance for a typical galaxy plate against the results obtained using the skycorr tool (Noll et al 2014). Skycorr was designed as a data reduction tool to remove sky emission lines for astronomical spectra using physically motivated scaling relations, and has been found to consistently perform better than the popular algorithm of Davies (2007).…”

“…Example spectrum of a randomly chosen MaNGA fiber from a typical galaxy plate (plate 7443, MJD 56745, exposure 177685) in the vicinity of an OH airglow region. The black line represents the as-observed galaxy + sky spectrum, the dashed red line the science spectrum after sky subtraction using the MaNGA DRP, and the blue line the science spectrum using instead the skycorr (Noll et al 2014) algorithm. The two methods of sky subtraction produce nearly identical results to within the DRP-estimate uncertainty (green line).…”

The Initial Mass Function in the Nearest Strong Lenses from SNELLS: Assessing the Consistency of Lensing, Dynamical, and Spectroscopic Constraints

“…The data were processed using the standard X-shooter pipeline to generate five separate data cubes, one for each echelle order, from the exposures. Subsequently, we combined the object and sky exposures for each order, and used the ESO skycorr (Noll et al 2014) and molecfit tools to account for sky variation between the exposures, and to derive corrections for telluric absorption. These steps were performed separately for each of the three IFU slices, to help account for small shifts in the wavelength solution residuals between the slices.…”

Radial constraints on the initial mass function from TiO features and Wing–Ford band in early-type galaxies

“…Therefore, sky subtraction cannot be performed in the standard way of interpolating the sky from the two sides of the slit. We performed sky subtraction with the software skycorr (Noll et al 2014). Given the two-dimensional spectrum of the object, O, we consider the nearest sky frame, S. We extract a one-dimensional (1D) spectrum from both S and O, by median-combining each frame over an 8-pixel region, using four pixels from each edge of the slit (where the signal from the galaxy is less prominent).…”