Milky Way Analogs


Jeffrey A. Newman
University of Pittsburgh


IFU observations for Milky Way analog galaxies chosen to supplement those that will be observed as a part of the main MaNGA sample.

Finding Targets

An object whose MANGA_TARGET3 or MNGTARG3 value includes one or more of the bitmasks in the following table was targeted for spectroscopy as part of this ancillary target program. See SDSS bitmasks to learn how to use these values to identify objects in this ancillary target program.

Program (bit name) Bit number Target Description
MWA 13 Milky Way Analog


In Licquia et al. (2015; hereafter L15) we identified a sample of SDSS galaxies whose distribution of total stellar masses (M*) and star formation rates (SFRs) match the posterior PDFs describing our current knowledge of these properties for the Milky Way (see Licquia & Newman 2015). This sample, which as an ensemble we call Milky Way analogs (MWAs), is a powerful tool for two major activities. First, they enable us to make drastically improved estimates of Milky Way properties (e.g., integrated optical color) which are difficult to measure directly. Second, and in light of the first, they provide deep insights into how the Milky Way fits in the extragalactic context by tightly constraining our Galaxy’s position in a variety of key parameter spaces that are used for studying galaxy evolution. Hence, this sample provides an important bridge between the intimate, high-detail studies feasible in our Galaxy and the trends and relations found for others. This ancillary program ensures a comprehensive sample of MWAs will be observed in the MaNGA survey, which will broadly expand the utility of the MWA technique.

Target Selection

Targets are drawn from the extended NSA catalog, and are intended to closely match the requirements for the MaNGA Primary Plus sample in order to maximize spatial resolution. In the below, sample B represents the set of all Milky Way analog galaxies that potentially would be observed in the main MaNGA sample. The distributions of M*, SFR, absolute magnitude and colors for sample B, however, do not match exactly the distributions for the full sample of MWAs identified in L15; they are slightly biased or deficient in certain regions of parameter space. Our selection process is designed to correct for these mismatches, so that ancillary targets in combination with those observed from sample B can be used to produce samples which match as closely as possible the full sample of MWAs from L15. We have optimized our selection assuming that objects from sample B that do get observed will be a randomly drawn subset. Our step-by-step procedure is laid out below:

0) Draw a sample of 500,000 Milky Way analogs using the method and dataset described in L15. This will contain a much smaller number of unique objects; the number of times each object is selected after performing 500,000 draws is recorded as its frequency, F.

1) Identify all unique MWAs drawn in step 0 that are present in the extended NSA catalog. Restrict to only those objects whose redshifts fall in the range zmin(Mi) – 1.25*zmax(Mi) given their absolute i-band magnitudes, Mi, where zmin and zmax denote the minimum and maximum redshift of the MaNGA Primary Plus sample as a function of Mi; call this sample A.

2) Identify all unique MWAs drawn in step 0 that are present in the Primary Plus sample of the MaNGA target catalog; call this sample B.

3) Construct the 4-dimensional parameter space defined by M*, SFR, Mi, and r-i color, normalized to units of the standard deviation of sample B along each axis. In this space, for each object in sample A calculate the number of objects from sample B that lie within a hyperspherical radius of 0.07, and record this as number of neighbors, N.

4) Each object in sample A is then assigned two prioritization values, PN and PF, based on their N and F values, respectively:

  • PN = 1, if N < 10
  • PN = 2, if 10 ≤ N ≤ 25
  • PN = 4, if N > 25
  • PF = 1.0, if F > 700
  • PF = 1.5, if 100 ≤ F ≤ 700
  • PF = 2.1, if F < 100

5) Each object in sample A is then finally prioritized by its value of PN × PF, where lower values indicate higher priority.


Licquia, T. C., & Newman, J. A. 2015, ApJ, 806, 96
Licquia, T. C., Newman, J. A., & Brinchmann, J. 2015, ApJ, 809, 96 (L15)