Surveying the Inner Halo of the Galaxy with 2MASS-Selected Horizontal Branch Candidates

We use 2MASS photometry to select blue horizontal branch (BHB) candidates covering the sky |b|>15 deg. A 12.5<J<15.5 sample of BHB stars traces the thick disk and inner halo to d<9 kpc, with a density comparable to that of M giant stars. We base our sample selection strategy on the Century Survey Galactic Halo Project, a survey that provides a complete, spectroscopically-identified sample of blue stars to a similar depth as the 2MASS catalog. We show that a -0.20<(J-H)_0<0.10, -0.10<(H-K)_0<0.10 color-selected sample of stars is 65% complete for BHB stars, and is composed of 47% BHB stars. We apply this photometric selection to the full 2MASS catalog, and see no spatial overdensities of BHB candidates at high Galactic latitude |b|>50 deg. We insert simulated star streams into the data and conclude that the high Galactic latitude BHB candidates are consistent with having no ~5 deg wide star stream with density greater than 0.33 objects deg^-2 at the 95% confidence level. The absence of structure suggests there have been no major accretion events in the inner halo in the last few Gyr. However, at low Galactic latitudes a two-point angular correlation analysis reveals structure on angular scales <1 deg. This structure is apparently associated with stars in the thick disk, and has a physical scale of 10-100 pc. Interestingly, such structures are expected by cosmological simulations that predict the majority of the thick disk may arise from accretion and disruption of satellite mergers.Comment: 11 pages, including figures. Accepted by AJ with minor revision

On the detection of statistical heterogeneity in rain measurements

The application of the Wiener–Khintchine theorem for translating a readily measured correlation function into the variance spectrum, important for scale analyses and for scaling transformations of data, requires that the data be wide-sense homogeneous (stationary), that is, that the first and second moments of the probability distribution of the variable are the same at all times (stationarity) or at all locations (homogeneity) over the entire observed domain. This work provides a heuristic method independent of statistical models for evaluating whether a set of data in rain is wide-sense stationary (WSS). The alternative, statistical heterogeneity, requires 1) that there be no single global mean value and/or 2) that the variance of the variable changes in the domain. Here, the number of global mean values is estimated using a Bayesian inversion approach, while changes in the variance are determined using record counting techniques. An index of statistical heterogeneity (IXH) is proposed for rain such that as its value approaches zero, the more likely the data are wide-sense stationary and the more acceptable is the use of the Wiener–Khintchine theorem. Numerical experiments as well as several examples in real rain demonstrate the potential of IXH to identify statistical homogeneity, heterogeneity, and statistical mixtures. In particular, the examples demonstrate that visual inspections of data alone are insufficient for determining whether they are wide-sense stationary. Furthermore, in this small data collection, statistical heterogeneity was associated with convective rain, while statistical homogeneity appeared in more stratiform or mixed rain events. These tentative associations, however, need further substantiation