K. W. Smith

Close binary companions of the HAeBe stars LkHα 198, Elias 1, HK Ori and V380 Ori

We present diffraction-limited bispectrum speckle interferometry observations of four well-known Herbig Ae/Be (HAeBe) stars, LkHα 198, Elias 1, HK Ori and V380 Ori. For two of these, LkHα 198 and Elias 1, we present the first unambiguous detection of close companions. The plane of the orbit of the new LkHα 198 companion appears to be significantly inclined to the plane of the circumprimary disk, as inferred from the orientation of the outflow. We show that the Elias 1 companion may be a convective star, and suggest that it could therefore be the true origin of the X-ray emission from this object. In the cases of HK Ori and V380 Ori, we present new measurements of the relative positions of already-known companions, indicating orbital motion. For HK Ori, photometric measurements of the brightness of the individual components in four bands allowed us to decompose the system spectral energy distribution (SED) into the two separate component SEDs. The primary exhibits a strong infrared excess which suggests the presence of circumstellar material, whereas the companion can be modelled as a naked photosphere. The infrared excess of HK Ori A was found to contribute around two thirds of the total emission from this component, suggesting that accretion power contributes significantly to the flux. Submillimetre constraints mean that the circumstellar disk cannot be particularly massive, whilst the near-infrared data indicates a high accretion rate. Either the disk lifetime is very short, or the disk must be seen in an outburst phase.

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)

Flares observed with XMM-Newton and the VLA

We present lightcurves obtained in X-ray by the XMM-Newton EPIC cameras and simultaneous radio lightcurves obtained with the VLA for five active M-type flare stars. A number of flare events were observed, and by comparing radio with X-ray data, we consider various possible flare mechanisms. In cases where there seems to be a clear correlation between radio and X-ray activity, we use an energy budget argument to show that the heating which leads to the X-ray emission could be due to the same particles emitting in the radio. In cases where there is radio activity without corresponding X-ray activity, we argue that the radio emission is likely to arise from coherent processes involving comparatively few particles. In one case, we are able to show from polarization of the radio emission that this is almost certainly the case. Cases for which X-ray activity is seen without corresponding radio activity are more difficult to explain. We suggest that the heating particles may be accelerated to very high energy, and the resulting synchrotron radio emission may be beamed in directions other than the line of sight.

DETECTION OF THE NEUPERT EFFECT IN THE CORONA OF AN RS CANUM VENATICORUM BINARY SYSTEM BY XMM-NEWTON AND THE VERY LARGE ARRAY

The RS CVn-type binary σ Geminorum was observed during a large, long-duration flare simultaneously with XMM-Newton and the Very Large Array. The light curves show a characteristic time dependence that is compatible with the Neupert effect observed in solar flares: the time derivative of the X-ray light curve resembles the radio light curve. This observation can be interpreted in terms of a standard flare scenario in which accelerated coronal electrons reach the chromosphere, where they heat the cool plasma and induce chromospheric evaporation. Such a scenario can hold only if the amount of energy in the fast electrons is sufficient to explain the X-ray radiative losses. We present a plausibility analysis that supports the chromospheric evaporation model.

SOM ensemble for unsupervised outlier analysis. Application to outlier identification in the Gaia astronomical survey

Gaia is an ESA cornerstone astronomical mission that will observe with unprecedented precision positions, distances, space motions, and many physical properties of more than one billion objects in our Galaxy and beyond. It will observe all objects in the sky in the visible magnitude range from 6 to 20, up to approximately 10^9 sources. An international scientific consortium, the Gaia Data Processing and Analysis Consortium (Gaia DPAC), has organized itself in several coordination units, with the aim, among others, of addressing the work of classifying the observed astronomical sources, using both supervised and unsupervised classification algorithms. This work focuses on the analysis of classification outliers by means of unsupervised classification. We present a novel method to combine SOMs trained with independent features that are calculated from spectrophotometry. The method as described here can help to improve the models used for the supervised classification of astronomical sources. Furthermore, it allows for data exploration and knowledge discovery in huge astronomical databases such as the upcoming Gaia mission.

CLASSIFICATION OF FIELD DWARFS AND GIANTS IN RAVE AND ITS USE IN STELLAR STREAM DETECTION

Samples of bright stars, as they emerge from surveys such as RAVE, contain comparable fractions of dwarf and giant stars. An efficient separation of these two luminosity classes is therefore important, especially for studies in which distances are estimated through photometric parallax relations. We use the available spectroscopic log g estimates from the second RAVE data release (DR2) to assign each star a probability for being a dwarf or subgiant/giant based on mixture model fits to the log g distribution in different color bins. We further attempt to use these stars as a labeled training set in order to classify stars which lack log g estimates into dwarfs and giants with a Support Vector Machine algorithm. We assess the performance of this classification against different choices of the input feature vector. In particular, we use different combinations of reduced proper motions, 2MASS JHK, DENIS IJK, and USNO-B B2R2 apparent magnitudes. Our study shows that—for our color ranges—the infrared bands alone provide no relevant information to separate dwarfs and giants. Even when optical bands and reduced proper motions are added, the fraction of true giants classified as dwarfs (the contamination) remains above 20%. Using only the dwarfs with available spectroscopic log g and distance estimates (the latter from Breddels et al.), we then repeat the stream search by Klementet al. (KFR08), which assumed that all stars were dwarfs and claimed the discovery of a new stellar stream at V ≈ –160 km s–1 in a sample of 7015 stars from RAVE DR1. The existence of the KFR08 stream has been supported by two recent studies using other independent data sets. Our re-analysis of the pure DR2 dwarf sample exhibits an overdensity of five stars at the phase-space position of the KFR08 stream, with a metallicity distribution that appears inconsistent with that of stars at comparably low rotational velocities. Compared to several smooth Milky Way models, the mean standardized deviation of the KFR08 stream is only marginal at 1.6 ± 0.4. Our data therefore do not allow to draw definite conclusions about its existence, but future RAVE data releases and other (best spectroscopic) surveys are going to help in resolving this issue.

Photometric identification of blue horizontal branch stars

We investigate the performance of some common machine learning techniques in identifying blue horizontal branch (BHB) stars from photometric data. To train the machine learning algorithms, we use previously published spectroscopic identifications of BHB stars from Sloan digital sky survey (SDSS) data. We investigate the performance of three different techniques, namely k nearest neighbour classification, kernel density estimation for discriminant analysis and a support vector machine (SVM). We discuss the performance of the methods in terms of both completeness (what fraction of input BHB stars are successfully returned as BHB stars) and contamination (what fraction of contaminating sources end up in the output BHB sample). We discuss the prospect of trading off these values, achieving lower contamination at the expense of lower completeness, by adjusting probability thresholds for the classification. We also discuss the role of prior probabilities in the classification performance, and we assess via simulations the reliability of the dataset used for training. Overall it seems that no-prior gives the best completeness, but adopting a prior lowers the contamination. We find that the support vector machine generally delivers the lowest contamination for a given level of completeness, and so is our method of choice. Finally, we classify a large sample of SDSS Data Release 7 (DR7) photometry using the SVM trained on the spectroscopic sample. We identify 27 074 probable BHB stars out of a sample of 294 652 stars. We derive photometric parallaxes and demonstrate that our results are reasonable by comparing to known distances for a selection of globular clusters. We attach our classifications, including probabilities, as an electronic table, so that they can be used either directly as a BHB star catalogue, or as priors to a spectroscopic or other classification method. We also provide our final models so that they can be directly applied to new data.

Photometric Classification of Stars, Galaxies and Quasars in the Sloan Digital Sky Survey DR6 Using Support Vector Machines

This paper describes the automated classification of objects from the DR6 release of the Sloan Digital Sky Survey (SDSS) using support vector machines (SVM). First the SVM classifier was trained on a dataset comprising the u−g, g−r, r−i and i−z colours of 47,401 stars, 415,634 galaxies and 71,031 quasars with spectral classifications. An analysis of the performance of the classifier showed a total classification error of 3.80% and demonstrates that the SVM is efficiently able to learn the non‐linear, four dimensional class boundaries. Afterwards class membership probabilities for stars, galaxies and quasars were predicted for 12,362,179 objects in DR6 without spectra which were situated within the inner 90% of the training colour space and had magnitude errors below 10%. The SVM predicted 11,012,775 stars, 1,088,862 galaxies and 260,542 quasars. The relatively high number of galaxies can be explained by our constraints on colours and magnitude errors. The results were validated by cross‐matching against t...

Infall variability in the Classical T Tauri system VZ Chamaeleonis

We present time series spectroscopy of the Classical T Tauri star VZ Cha. We follow spectral variations at intermediate resolution over ve successive nights, or approximately two rotation periods. We see prole features which persist on timescales longer than the expected infall time from the inner disc, and we see expected evidence of rotational variations in the lines, but we also note that rotation alone cannot produce all the observed variability and some other mechanism must be invoked. The behaviour of H is observed to be markedly dierent from that of the other lines. In particular, the evidence of rotational eects is lacking at H, and the activity in the red and blue wings of the line is not signicantly correlated, in contrast to the other Balmer lines.

Incorporating Domain Specific Information into Gaia Source Classification

Astronomy is in the age of large scale surveys in which the gathering of multidimensional data on thousands of millions of objects is now routine. Efficiently processing these data — classifying objects, searching for structure, fitting astrophysical models — is a significant conceptual (not to mention computational) challenge. While standard statistical methods, such as Bayesian clustering, k-nearest neighbours, neural networks and support vector machines, have been successfully applied to some areas of astronomy, it is often difficult to incorporate domain specific information into these. For example, in astronomy we often have good physical models for the objects (e.g. stars) we observe. That is, we can reasonably well predict the observables (typically, the stellar spectrum or colours) from the astrophysical parameters (APs) we want to infer (such as mass, age and chemical composition). This is the “forward model”: The task of classification or parameter estimation is then an inverse problem. In this paper, we discuss the particular problem of combining astrometric information, effectively a measure of the distance of the source, with spectroscopic information.