A Statistical Detection of Wide Binary Systems in the Ultra-Faint Dwarf Galaxy Reticulum II

TL;DR

This paper introduces a novel application of nearest neighbor statistics to detect wide binary stars in the ultra-faint dwarf galaxy Reticulum II, revealing a low binary fraction and constraining the initial mass function.

Contribution

First application of NN statistics to identify wide binaries in a UFD, providing new estimates of binary fraction and insights into stellar populations.

Findings

Detected an enhancement in NN distances indicating wide binaries.

Estimated a binary fraction of approximately 0.7% at separations >3000 AU.

Constrained the initial mass function with a shallow power-law slope.

Abstract

Binary stars can inflate the observed velocity dispersion of stars in dark matter dominated systems such as ultra-faint dwarf galaxies (UFDs). However, the population of binaries in UFDs is poorly constrained by observations, with preferred binary fractions for individual galaxies ranging from a few percent to nearly unity. Searching for wide binaries through nearest neighbor (NN) statistics (or the two-point correlation function) has been suggested in the literature, and we apply this method for the first time to detect wide binaries in a UFD. By analyzing the positions of stars in Reticulum II (Ret II) from Hubble Space Telescope images, we search for angularly resolved wide binaries in Ret II. We find that the distribution of their NN distances shows an enhancement at projected separations of $<1$ arcsec relative to a model containing no binaries. We show that such an enhancement can be explained by a wide~binary fraction of $f_b\approx0.007^{+0.008}_{-0.003}$ at separations of more than 3000 AU. Under the assumption that the binary separation distribution is similar to that in the Milky Way, the total binary fraction in Ret II may be on the order of 50%. We also use the observed magnitude distribution of stars in Ret II to constrain the initial mass function over the mass range $0.34-0.78~M_{\odot}$, finding that a shallow power-law slope of $1.01 \le \alpha \le 1.15$ matches the data.