The expected performance of stellar parametrization with Gaia spectrophotometry

Gaia will obtain astrometry and spectrophotometry for essentially all sources in the sky down to a broad band magnitude limit of G=20, an expected yield of 10^9 stars. Its main scientific objective is to reveal the formation and evolution of our Galaxy through chemo-dynamical analysis. In addition to inferring positions, parallaxes and proper motions from the astrometry, we must also infer the astrophysical parameters of the stars from the spectrophotometry, the BP/RP spectrum. Here we investigate the performance of three different algorithms (SVM, ILIUM, Aeneas) for estimating the effective temperature, line-of-sight interstellar extinction, metallicity and surface gravity of A-M stars over a wide range of these parameters and over the full magnitude range Gaia will observe (G=6-20mag). One of the algorithms, Aeneas, infers the posterior probability density function over all parameters, and can optionally take into account the parallax and the Hertzsprung-Russell diagram to improve the estimates. For all algorithms the accuracy of estimation depends on G and on the value of the parameters themselves, so a broad summary of performance is only approximate. For stars at G=15 with less than two magnitudes extinction, we expect to be able to estimate Teff to within 1%, logg to 0.1-0.2dex, and [Fe/H] (for FGKM stars) to 0.1-0.2dex, just using the BP/RP spectrum (mean absolute error statistics are quoted). Performance degrades at larger extinctions, but not always by a large amount. Extinction can be estimated to an accuracy of 0.05-0.2mag for stars across the full parameter range with a priori unknown extinction between 0 and 10mag. Performance degrades at fainter magnitudes, but even at G=19 we can estimate logg to better than 0.2dex for all spectral types, and [Fe/H] to within 0.35dex for FGKM stars, for extinctions below 1mag.Comment: MNRAS, in press. Minor corrections made in v

The K giant stars from the LAMOST survey data I: identification, metallicity, and distance

We present a support vector machine classifier to identify the K giant stars from the LAMOST survey directly using their spectral line features. The completeness of the identification is about 75% for tests based on LAMOST stellar parameters. The contamination in the identified K giant sample is lower than 2.5%. Applying the classification method to about 2 million LAMOST spectra observed during the pilot survey and the first year survey, we select 298,036 K giant candidates. The metallicities of the sample are also estimated with uncertainty of $0.13\sim0.29$\,dex based on the equivalent widths of Mg$_{\rm b}$ and iron lines. A Bayesian method is then developed to estimate the posterior probability of the distance for the K giant stars, based on the estimated metallicity and 2MASS photometry. The synthetic isochrone-based distance estimates have been calibrated using 7 globular clusters with a wide range of metallicities. The uncertainty of the estimated distance modulus at $K=11$\,mag, which is the median brightness of the K giant sample, is about 0.6\,mag, corresponding to $\sim30$% in distance. As a scientific verification case, the trailing arm of the Sagittarius stream is clearly identified with the selected K giant sample. Moreover, at about 80\,kpc from the Sun, we use our K giant stars to confirm a detection of stream members near the apo-center of the trailing tail. These rediscoveries of the features of the Sagittarius stream illustrate the potential of the LAMOST survey for detecting substructures in the halo of the Milky Way.Comment: 24 pages, 20 figures, submitted to Ap

Finding rare objects and building pure samples: Probabilistic quasar classification from low resolution Gaia spectra

We develop and demonstrate a probabilistic method for classifying rare objects in surveys with the particular goal of building very pure samples. It works by modifying the output probabilities from a classifier so as to accommodate our expectation (priors) concerning the relative frequencies of different classes of objects. We demonstrate our method using the Discrete Source Classifier, a supervised classifier currently based on Support Vector Machines, which we are developing in preparation for the Gaia data analysis. DSC classifies objects using their very low resolution optical spectra. We look in detail at the problem of quasar classification, because identification of a pure quasar sample is necessary to define the Gaia astrometric reference frame. By varying a posterior probability threshold in DSC we can trade off sample completeness and contamination. We show, using our simulated data, that it is possible to achieve a pure sample of quasars (upper limit on contamination of 1 in 40,000) with a completeness of 65% at magnitudes of G=18.5, and 50% at G=20.0, even when quasars have a frequency of only 1 in every 2000 objects. The star sample completeness is simultaneously 99% with a contamination of 0.7%. Including parallax and proper motion in the classifier barely changes the results. We further show that not accounting for class priors in the target population leads to serious misclassifications and poor predictions for sample completeness and contamination. (Truncated)Comment: MNRAS accepte

Star-galaxy separation strategies for WISE-2MASS all-sky infrared galaxy catalogs

We combine photometric information of the WISE and 2MASS all-sky infrared databases, and demonstrate how to produce clean and complete galaxy catalogs for future analyses. Adding 2MASS colors to WISE photometry improves star-galaxy separation efficiency substantially at the expense of loosing a small fraction of the galaxies. We find that 93% of the WISE objects within W1<15.2 mag have a 2MASS match, and that a class of supervised machine learning algorithms, Support Vector Machines (SVM), are efficient classifiers of objects in our multicolor data set. We constructed a training set from the SDSS PhotoObj table with known star-galaxy separation, and determined redshift distribution of our sample from the GAMA spectroscopic survey. Varying the combination of photometric parameters input into our algorithm we show that W1 - J is a simple and effective star-galaxy separator, capable of producing results comparable to the multi-dimensional SVM classification. We present a detailed description of our star-galaxy separation methods, and characterize the robustness of our tools in terms of contamination, completeness, and accuracy. We explore systematics of the full sky WISE-2MASS galaxy map, such as contamination from Moon glow. We show that the homogeneity of the full sky galaxy map is improved by an additional J<16.5 mag flux limit. The all-sky galaxy catalog we present in this paper covers 21,200 sq. degrees with dusty regions masked out, and has an estimated stellar contamination of 1.2% and completeness of 70.1% among 2.4 million galaxies with $z_{med}= 0.14$. WISE-2MASS galaxy maps with well controlled stellar contamination will be useful for spatial statistical analyses, including cross correlations with other cosmological random fields, such as the Cosmic Microwave Background. The same techniques also yield a statistically controlled sample of stars as well.Comment: 10 pages, 11 figures. Accepted for publication in MNRA

Variables in the Southern Polar Region Evryscope 2016 Dataset

The regions around the celestial poles offer the ability to find and characterize long-term variables from ground-based observatories. We used multi-year Evryscope data to search for high-amplitude (~5% or greater) variable objects among 160,000 bright stars (Mv < 14.5) near the South Celestial Pole. We developed a machine learning based spectral classifier to identify eclipse and transit candidates with M-dwarf or K-dwarf host stars - and potential low-mass secondary stars or gas giant planets. The large amplitude transit signals from low-mass companions of smaller dwarf host stars lessens the photometric precision and systematics removal requirements necessary for detection, and increases the discoveries from long-term observations with modest light curve precision. The Evryscope is a robotic telescope array that observes the Southern sky continuously at 2-minute cadence, searching for stellar variability, transients, transits around exotic stars and other observationally challenging astrophysical variables. In this study, covering all stars 9 < Mv < 14.5, in declinations -75 to -90 deg, we recover 346 known variables and discover 303 new variables, including 168 eclipsing binaries. We characterize the discoveries and provide the amplitudes, periods, and variability type. A 1.7 Jupiter radius planet candidate with a late K-dwarf primary was found and the transit signal was verified with the PROMPT telescope network. Further followup revealed this object to be a likely grazing eclipsing binary system with nearly identical primary and secondary K5 stars. Radial velocity measurements from the Goodman Spectrograph on the 4.1 meter SOAR telescope of the likely-lowest-mass targets reveal that six of the eclipsing binary discoveries are low-mass (.06 - .37 solar mass) secondaries with K-dwarf primaries, strong candidates for precision mass-radius measurements.Comment: 32 pages, 17 figures, accepted to PAS