Using APOGEE Spectra

The apVisit/asVisit files contain individual visit spectra before resampling and removal of radial velocities. Note that while these have wavelength calibration information, the native wavelength scale is not an evenly spaced linear or logarithmic scale. The wavelength information is included as a separate wavelength array. The wavelength information is also available in a table that provides the parameters of the function used to fit the pixel-wavelength relation, and this information is required if you wish to plot flux against wavelength. For the apVisit/asVisit files, spectra from each of the three chips are stored in different rows in the image extensions.

The apVisit/asVisit files contain the following information for an individual visit spectrum:

• Summary Header
• Visit flux and uncertainties
• Pixel bitmask array
• Wavelength array
• Sky flux and uncertainties
• Telluric flux and uncertainties
• Wavelength solution coefficients
• Line-spread-function coefficients

The combined spectra are given in the apStar/asStar files. These combined spectra are generated by resampling individual visits onto a common, logarithmically-spaced wavelength scale after removing each visit's derived radial velocity (log λi+1 – log λi = 6E-6, with a common starting wavelength of 15100.802 Ångströms). The resulting spectra are in rest, vacuum wavelengths. Data from the entire APOGEE wavelength range (which includes some gaps ) are included in a single array. The wavelength scale is recorded in the header in standard FITS keywords, which users can access to build wavelength arrays corresponding to the stored fluxes.

The apStar/asStar files contain the following information for a combined spectrum:

• Summary Header
• Combined flux and uncertainties
• Velocity-corrected, resampled visit spectra and uncertainties
• Pixel bitmask array for combined and individual visit spectra
• Sky flux and uncertainties
• Telluric flux and uncertainties
• Radial velocity data table

The aspcapStar files contain combined and pseudo-continuum normalized spectra that have been velocity-corrected (to vacuum wavelengths), resampled like the apStar/asStar spectra. These files also contain a synthetic spectrum computed for the best fit parameters from ASPCAP, however users should note that this spectrum is only presents the best fit spectroscopic parameters from ASPCAP and does not take into account the best fit stellar abundances, so the fits for individual elements may appear discrepant.

The aspcapStar files contain the following information for a given star and its best ASPCAP fit spectrum:

• Summary Header
• Pseudo-continuum normalized observed flux and uncertainties
• Best ASPCAP fit spectrum
• ASPCAP data table

The night sky lines (i.e., "airglow"), primarily from OH emission in the Earth's atmosphere, can be extremely bright. The sky emission is removed from the science spectra using the combination of the emission from sky fibers at sky positions near that of the target. However, this subtraction is almost always imperfect for two reasons. First, the sky spectrum has to be wavelength-shifted to match the science spectra; this occurs because the fibers have different locations along the pseudo-slit. Second, the line spread function (LSF) varies by fiber due to changes in image quality across the field-of-view. Because the night skylines are so bright, even small fractional variations due to these issues can cause the subtraction to be very noticeably imperfect; thus, most skylines are either under- or over-subtracted.

Note that, even if the airglow subtraction were perfect, the area of the spectrum "under" the skylines would be of significantly lower signal-to-noise, due to the substantial Poisson contribution from the bright lines.

The imperfect night sky line subtraction does have the unfortunate result of making the APOGEE spectra appear a bit "ugly" to a quick, casual inspection. The APOGEE data products (e.g., apVisit/asVisit and apStar/asStar files) include a record of the sky spectrum that was subtracted, and it is possible to use this as a guide for recognizing pixels that are likely to be affected by imperfect sky subtraction.

The IR detectors used for APOGEE are not cosmetically perfect. Small regions of each chip are bad, and there are a significant number of individual bad or "hot" pixels. These are flagged during the data processing and can lead to bad or missing regions in any given spectrum. Because visit spectra are combined from multiple individual dithered spectra, a single bad pixel can propagate into multiple pixels in the visit-combined spectra. In combination with the poorly subtracted skylines, these bad pixels can have the effect of making individual visit spectra look rather "ugly." The mask arrays can be used to identify the cause of most bad pixels.

Because any given star will typically not use the same fiber for different visits, combined spectra generally look somewhat cleaner, especially if the observed radial velocity (including differences in barycentric RV) of a target differs significantly from visit-to-visit. However, even if the combined spectra do not have regions with missing data, there may be regions where the noise level is elevated if that portion of the spectrum landed on a bad region of one of the arrays in one or more of its visits.

The use of VPH gratings results in the production of some "ghosts" on the 2-D images. The most prominent of these is the "Littrow ghost," which for the APOGEE data falls somewhere in the wavelength region 1.624 to 1.626 microns, depending on the fiber.

The amplitude of the ghost depends on the brightness of other stars in the field, so it does not always contribute a significant amount of flux. Pixels possibly affected by the "Littrow ghost" are flagged with the LITTROW_GHOST bit in the APOGEE_PIXMASK bitmask.

The spacing of adjacent spectra is ~ 6.5 pixels (as measured between adjacent PSF peaks) to pack the spectra of as many stars as possible across the APOGEE detectors. Therefore, the wings of the PSF overlap slightly between neighboring spectra, and the effect is particularly apparent if an object is located adjacent to a much brighter object. As a result, the targets on each plate are sorted into three brightness categories -- bright (B), medium (M), and faint (F) -- and these categories are placed along the pseudo-slit (and hence, on the detectors) in the order FMBBMF FMBBMF. In principle, an object in the faint class or a sky fiber should never land next to a much brighter object. Yet, the magnitude ranges of these brightness categories can be broad and make it possible for targets of significantly different brightness to have spectra adjacent to one another.

The extraction portion of the data reduction pipeline accounts for contributions of light from the two neighboring spectra for each target. The effectiveness of this extraction depends on our knowledge of the amplitude of the wings of the light distribution. In cases where adjacent targets are significantly brighter than a given object, small inaccuracies in the PSF model may lead to significant errors in the extraction of the spectrum.

For each visit, a bit is set in the APOGEE_STARFLAG bitmask if an adjacent object is more than 100 times brighter than the star itself (VERY_BRIGHT_NEIGHBOR) or more than 10 times brighter (BRIGHT_NEIGHBOR). The former case, which is rare, is automatically considered as a bad spectrum and will not be included in the combined spectrum.

Some areas of the detectors used in the APOGEE northern instrument suffer from a problem referred to as "super-persistence." In these locations on the detector, previous exposure to light causes a glow in subsequent images that can be substantial and last for a significant amount of time. The problem is most severe on about 1/3 of the "blue" chip, i.e., the chip that records wavelengths between 1.514 and 1.581 microns. Due to the chip orientation, this full wavelength region may be impacted for all objects in this part of the chip (~1/3). There are also regions in the "green" chip that are affected by a lower level of super-persistence, but these regions are less clearly defined by fiber number or wavelength.

After the completion of the APOGEE-1 survey in summer 2014, the instrument was opened, and the blue detector, which had the worst impact from super-persistence, was replaced with a detector with better performance. While this will mitigate the effects of super-persistence for data taken after summer 2014, DR16 includes data taken from before this date as well.

The impact of super-persistence for a given object is dependent on (1) the prior exposure history and (2) the brightness of the current target. The fiber management system described in the Fiber Cross Talk section provides some level of mitigation. Grouping the fibers by target brightness makes it relatively uncommon for a faint target to be observed through a fiber that was previously placed on a bright target. However, because the magnitude ranges that define these categories are broad, there can still be cases where faint targets follow relatively brighter ones. Also, calibration flat field exposures are taken between every plate to map the distribution of light between fibers and to measure the fiber-to-fiber throughput variations. These roughly evenly-illuminated frames are sufficient to give rise to some super-persistence.

Superpersistence is a complex phenomenon. In DR13/DR14, we attempted to implement a first-order super-persistence correction, based on some calibration data that were obtained, and scaling the results to try to match persistence observed in dark frames taken before most of the science exposures. We also implemented a scheme in which pixels affected by persistence are assigned a lower weight when different visits are combined. Additional description of how this problem is addressed is available in Holtzman et al. (2018). We implemented the same procedure for DR17.

The effect of super-persistence can be significant and is easily noticed: the flux levels in the region of the spectrum affected can be enhanced by tens of percent or more. This enhancement is likely to have some wavelength dependence meaning that spectral features may be distorted. However, depending on the brightness of the target and the preceding ones, it is not guaranteed that the spectra are adversely affected at a significant level, so we do not flag all data that falls within the super-persistence region as bad by default.

In the data reduction pipeline, we flag all pixels in the regions where significant superperstence is known to occur in the APOGEE_PIXMASK bitmask using three different flags corresponding to the level of the effect: PERSIST_HIGH, PERSIST_MED, and PERSIST_LOW. In addition, we have a visit level flag, APOGEE_STARFLAG, for each object, with bits that get set when a significant number of pixels ($\gt$20%) in the spectrum are affected, which are again split into categories of PERSIST_HIGH, PERSIST_MED, and PERSIST_LOW. In addition, we look for evidence in the spectra of a "jump" in flux between the "green" and the "blue" chips, and if this is present at an easily recognized level, we set a flag PERSIST_JUMP_HIGH or PERSIST_JUMP_LOW if the "blue" portion of the spectrum seems abnormally high or abnormally low (the latter could occur, e.g., if a sky fiber from a region affected by superpersistence is used for sky subtraction, although the pipeline takes some steps to try to avoid this occurrence).

In the combined spectra, star level flags are provided that are bitwise AND and bitwise OR combinations of the visit APOGEE_STARFLAG flags to indicate whether a given object was marked as having a significant number of pixels in the super-persistence region in all or any of the visit spectra comprising the combination. Starting in DR13, a scheme was implemented in which pixels affected by super-persistence are given inflated uncertainties to reduce their impact on the combined spectra. For those stars where some visits were impacted by super-persistence, and others were not, the random uncertainties are larger because those visits affected by the super-persistence are essentially ignored. For stars in which all visits are impacted by persistence, this has the effect of giving the persistence-affected wavelengths less weight in the ASPCAP fitting than pixels at other wavelengths.

As noted in Holtzman et al. (2015), the effects of persistence can be seen in some of the individual element abundances for the DR12 data. While this may be true for some objects in the current data release, comparison of results for stars unaffected by persistence to those affected by persistence in all visits suggests that the down-weighting scheme has helped significantly to mitigate persistence effects. The stars most likely to still be impacted are those that are the faintest stars or those with weak absorption features. Additional discussion on these issues relevant to the DR17 reductions can be found in Holtzman et al. (2018).