Robust Measurement of Stellar Streams Around the Milky Way: Correcting Spatially Variable Observational Selection Effects in Optical Imaging Surveys

Abstract

Observations of density variations in stellar streams are a promising probe of low-mass dark matter substructure in the Milky Way. However, survey systematics such as variations in seeing and sky brightness can also induce artificial fluctuations in the observed densities of known stellar streams. These variations arise because survey conditions affect both object detection and star-galaxy misclassification rates. To mitigate these effects, we use Balrog synthetic source injections in the Dark Energy Survey (DES) Y3 data to calculate detection rate variations and classification rates as functions of survey properties. We show that these rates are nearly separable with respect to survey properties and can be estimated with sufficient statistics from the synthetic catalogs. Applying these corrections reduces the standard deviation of relative detection rates across the DES footprint by a factor of five, and our corrections significantly change the inferred linear density of the Phoenix stream when including faint objects. Additionally, for artificial streams with DES like survey properties we are able to recover density power spectra with reduced bias. We also find that uncorrected power-spectrum results for LSST-like data can be around five times more biased, highlighting the need for such corrections in future ground based surveys.

Figures (16)

• Figure 1: Distributions of two of the survey properties used, stellar density (left) and exposure time sum in the $i$-band (right).
• Figure 1: (Left) Example of 2D distributions of objects in inseparable and separable cases before and after corrections. (Right) A weighted distribution of the metric improvement value, weighted by the pre-correction metric. Any value over zero is considered improvement, and a one would be perfect removal of any dependency. Every metric improvement value was greater than $0.65$, with the vast majority being above $0.95$.
• Figure 1: Each plot is using relative detection rates for our faintest magnitude bin. The dotted black line shows where the two rates are equal. (Left) Relative detection rates of stars detected as stars vs galaxies detected as stars. (Right) Relative detection rates of stars detected as stars vs stars detected as galaxies.
• Figure 1: (Left) Median power spectra over 5000 realizations for a baseline stream (red), uncorrected data (blue), and corrected data with each correction algorithm (orange and green). The recover classified counts algorithm shows lower noise levels than the recover observed counts algorithm. (Right) One dimensional dependencies of stream density from the relative detection rate of correctly classified stars in our faintest magnitude bin ($23.9<r\leq 24.5$). 5000 realizations are performed, and the averages are plotted with standard deviation error bars. Both corrected lines show decreased variance.
• Figure 1: (Left) Average maximum deviations for relative detection rate variations for Balrog galaxies. Higher object counts allowed for probing at lower percentages of the Balrog sample. (Right) Average maximum deviations for relative detection rates variations for Balrog stars classified as stars in the faintest magnitude bin used.