The Gaia satellite will survey the entire celestial sphere down to 20th magnitude, obtaining astrometry, photometry, and low resolution spectrophotometry on one billion astronomical sources, plus radial velocities for over one hundred million stars.
Its main objective is to take a census of the stellar content of our Galaxy, with the goal of revealing its formation and evolution. Gaias unique feature is the measurement of parallaxes and proper motions with hitherto unparalleled accuracy for many objects. As a survey, the physical properties of most of these objects are unknown. Here we describe the data analysis system put together by the Gaia consortium to classify these objects and to infer their astrophysical properties using the satellites data. This system covers single stars, (unresolved) binary stars, quasars, and galaxies, all covering a wide parameter space. Multiple methods are used for many types of stars, producing multiple results for the end user according to different models and assumptions. Prior to its application to real Gaia data the accuracy of these methods cannot be assessed definitively. But as an example of the current performance, we can attain internal accuracies (RMS residuals) on F,G,K,M dwarfs and giants at G=15 (V=15-17) for a wide range of metallicites and interstellar extinctions of around 100K in effective temperature (Teff), 0.1mag in extinction (A0), 0.2dex in metallicity ([Fe/H]), and 0.25dex in surface gravity (logg). The accuracy is a strong function of the parameters themselves, varying by a factor of more than two up or down over this parameter range. After its launch in November 2013, Gaia will nominally observe for five years, during which the system we describe will continue to evolve in light of experience with the real data.
Gaia will provide parallaxes and proper motions with accuracy ranging from 10 to 1000 microarcsecond on up to one billion stars. Most of these will be disk stars: for an unreddened K giant at 6 kpc, it will measure the distance accurate to 15% and th
e transverse velocity to an accuracy of about 1 km/s. Gaia will observe tracers of Galactic structure across the whole HR diagram, including Cepheids, RR Lyrae, white dwarfs, F dwarfs and HB stars. Onboard low resolution spectrophotometry will permit -- in addition to a Teff estimate -- dwarf/giant discrimination, metallicity measurement and extinction determination. For the first time, then, Gaia will provide us with a 3D spatial/properties map and at least a 2D velocity map of these tracers (RVs will be obtained too for brighter stars.) This will be a goldmine of information from which to learn about the origin and evolution of the Galactic disk. I briefly review the Gaia mission, and then show how the expected astrometric accuracies translate into distance and velocity accuracies and statistics. I examine the impact Gaia should have on a few scientific areas relevant to the Galactic disk. I discuss how a better determination of the spiral arm locations and pattern speed, plus a better reconstruction of the Suns orbit over the past billion years (from integration through the Gaia-measured gravitational potential) will allow us to assess the possible role of spiral arm crossings in ice ages and mass extinctions on the Earth.
Nearby gamma-ray bursts (GRBs) are likely to have represented a significant threat to life on the Earth. Recent observations suggest that a significant source of such bursts is compact binary mergers in globular clusters. This link between globular c
lusters and GRBs offers the possibility to find time intervals in the past with higher probabilities of a nearby burst, by tracing globular cluster orbits back in time. Here we show that the expected flux from such bursts is not flat over the past 550 Myr but rather exhibits three broad peaks, at 70, 180 and 340 Myr ago. The main source for nearby GRBs for all three time intervals is the globular cluster 47 Tuc, a consequence of its large mass and high stellar encounter rate, as well as the fact that it is one of the globular clusters which comes quite close to the Sun. Mass extinction events indeed coincide with all three time intervals found in this study, although a chance coincidence is quite likely. Nevertheless, the identified time intervals can be used as a guide to search for specific signatures of GRBs in the geological record around these times.
We present some of the strategies being developed to classify and parameterize objects obtained with spectra from the Sloan Digital Sky Survey (SDSS) and the RAdial Velocity Experiment (RAVE) and present some results. We estimate stellar atmospheric
parameters (effective temperature, gravity, and metallicity) from spectral and photometric data and use these to analyse Galactic populations. We demonstrate this through the selection of a sample of candidate Blue Horizontal-Branch and RR Lyrae stars selected from SDSS/SEGUE.
Using the Wide Field Imager (WFI) at the ESO 2.2m telescope at La Silla and the CPAPIR camera at the CTIO 1.5m telescope at Cerro Tololo, we have performed an extensive, multiband photometric survey of the open cluster IC2391 (D~146pc, age~50Myr, sol
ar metallicity). Here we present the results from our photometric survey and from a spectroscopic follow-up of the central part of the survey.
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 (p
riors) 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)
Models of brown dwarf atmospheres suggest they exhibit complex physical behaviour. Observations have shown that they are indeed dynamic, displaying small photometric variations over timescales of hours. Here I report results of infrared (0.95-1.64 mi
cron) spectrophotometric monitoring of four field L and T dwarfs spanning timescales of 0.1-5.5 hrs, the goal being to learn more about the physical nature of this variability. Spectra are analysed differentially with respect to a simultaneously observed reference source in order to remove Earth-atmospheric variations. The variability amplitude detected is typically 2-10%, depending on the source and wavelength. I analyse the data for correlated variations between spectral indices. This approach is more robust than single band or chisq analyses, because it does not assume an amplitude for the (often uncertain) noise level (although the significance test still assumes a shape for the noise power spectrum). Three of the four targets show significant evidence for correlated variability. Some of this can be associated with specific features including Fe, FeH, VO and KI, and there is good evidence for intrinsic variability in water and possibly also methan. Yet some of this variability covers a broader spectral range which would be consistent with dust opacity variations. The underlying common cause is plausibly localized temperature or composition fluctuations caused by convection. Looking at the high signal-to-noise ratio stacked spectra we see many previously identified spectral features of L and T dwarfs, such as KI, NaI, FeH, water and methane. In particular we may have detected methane absorption at 1.3-1.4 micron in the L5 dwarf SDSS 0539-0059.
I outline a method for estimating astrophysical parameters (APs) from multidimensional data. It is a supervised method based on matching observed data (e.g. a spectrum) to a grid of pre-labelled templates. However, unlike standard machine learning me
thods such as ANNs, SVMs or k-nn, this algorithm explicitly uses domain information to better weight each data dimension in the estimation. Specifically, it uses the sensitivity of each measured variable to each AP to perform a local, iterative interpolation of the grid. It avoids both the non-uniqueness problem of global regression as well as the grid resolution limitation of nearest neighbours.
