Showing papers by "David W. Hogg published in 2016"

Comoving stars in Gaia DR1: An abundance of very wide separation co-moving pairs

Abstract: The primary sample of the {\it Gaia} Data Release 1 is the Tycho-Gaia Astrometric Solution (TGAS): $\approx$ 2 million Tycho-2 sources with improved parallaxes and proper motions relative to the initial catalog. This increased astrometric precision presents an opportunity to find new binary stars and moving groups. We search for high-confidence comoving pairs of stars in TGAS by identifying pairs of stars consistent with having the same 3D velocity using a marginalized likelihood ratio test to discriminate candidate comoving pairs from the field population. Although we perform some visualizations using (bias- corrected) inverse parallax as a point estimate of distance, the likelihood ratio is computed with a probabilistic model that includes the covariances of parallax and proper motions and marginalizes the (unknown) true distances and 3D velocities of the stars. We find 13,085 comoving star pairs among 10,606 unique stars with separations as large as 10 pc (our search limit). Some of these pairs form larger groups through mutual comoving neighbors: many of these pair networks correspond to known open clusters and OB associations, but we also report the discovery of several new comoving groups. Most surprisingly, we find a large number of very wide ($>1$ pc) separation comoving star pairs, the number of which increases with increasing separation and cannot be explained purely by false-positive contamination. Our key result is a catalog of high-confidence comoving pairs of stars in TGAS. We discuss the utility of this catalog for making dynamical inferences about the Galaxy, testing stellar atmosphere models, and validating chemical abundance measurements.

Do fast stellar centroiding methods saturate the Cramér-Rao lower bound?

Abstract: One of the most demanding tasks in astronomical image processing---in terms of precision---is the centroiding of stars. Upcoming large surveys are going to take images of billions of point sources, including many faint stars, with short exposure times. Real-time estimation of the centroids of stars is crucial for real-time PSF estimation, and maximal precision is required for measurements of proper motion. The fundamental Cramer-Rao lower bound sets a limit on the root-mean-squared-error achievable by optimal estimators. In this work, we aim to compare the performance of various centroiding methods, in terms of saturating the bound, when they are applied to relatively low signal-to-noise ratio unsaturated stars assuming zero-mean constant Gaussian noise. In order to make this comparison, we present the ratio of the root-mean-squared-errors of these estimators to their corresponding Cramer-Rao bound as a function of the signal-to-noise ratio and the full-width at half-maximum of faint stars. We discuss two general circumstances in centroiding of faint stars: (i) when we have a good estimate of the PSF, (ii) when we do not know the PSF. In the case that we know the PSF, we show that a fast polynomial centroiding after smoothing the image by the PSF can be as efficient as the maximum-likelihood estimator at saturating the bound. In the case that we do not know the PSF, we demonstrate that although polynomial centroiding is not as optimal as PSF profile fitting, it comes very close to saturating the Cramer-Rao lower bound in a wide range of conditions. We also show that the moment-based method of center-of-light never comes close to saturating the bound, and thus it does not deliver reliable estimates of centroids.

Survey Cross-Calibration with The Cannon: APOGEE-scale Stellar Labels from LAMOST Spectra

Abstract: To capitalize on a diverse set of large spectroscopic stellar surveys, it is essential to develop techniques for precise and accurate survey cross-calibration. Here, we demonstrate that this can be achieved by a data-driven approach to spectral modeling: we use The Cannon (Ness et al. 2015) to cross-calibrate APOGEE and LAMOST, two large-scale surveys that currently yield inconsistent results due to differing experimental setups and data analysis methodologies. The Cannon constructs a predictive model for LAMOST spectra using a reference set of 9952 stars observed in common between the two surveys, taking five labels as ground truth from APOGEE DR12: Teff, log g, [Fe/H], [\alpha/M], and K-band extinction A_k. The model is then used to infer Teff, log g, [Fe/H], and [\alpha/M] for 454,180 giant stars in LAMOST DR2, thus tying low-resolution (R=1800) LAMOST spectra to the APOGEE (R=22,500) label scale. Despite being derived directly from LAMOST spectra, which have lower spectral resolution and very different wavelength coverage, these new Cannon labels have an accuracy and precision comparable to the stated APOGEE DR12 values and uncertainties, essentially eliminating the systematic label inconsistencies resulting from the individual survey pipelines. By transferring [\alpha/M] labels from APOGEE, The Cannon produces the first [\alpha/M] values measured from LAMOST spectra, and the largest catalog of [\alpha/M] for giant stars to date. This demonstrates that The Cannon can successfully bring different surveys onto the same label scale, and effectively transfer label systems from a high-resolution survey to low-resolution spectra.