- ID:
- ivo://CDS.VizieR/J/MNRAS/427/2917
- Title:
- Classification of Hipparcos variables
- Short Name:
- J/MNRAS/427/2917
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- The Hipparcos catalogue (ESA 1997, Cat. I/239) and the AAVSO Variable Star Index (Watson et al., 2011, Cat. B/vsx) are employed to complement the training set of periodic variables of Dubath et al. (2011, Cat. J/MNRAS/414/2602) with irregular and non-periodic representatives, leading to 3881 sources in total which described 24 variability types. The attributes employed to characterize light-curve features are selected according to their relevance for classification. Classifier models are produced with random forests and a multi-stage methodology based on Bayesian networks, achieving overall misclassification rates under 12%. Both classifiers are applied to predict variability types for 6051 Hipparcos variables associated with uncertain or missing types in the literature.
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- ID:
- ivo://CDS.VizieR/II/130
- Title:
- 2-D Classification, Vilnius Photometry M56 Region
- Short Name:
- II/130
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- This catalogue gives the seven-color photometry in the Vilnius system stars and two-dimensional classification of 752 stars. Photographically determined magnitudes obtained by COSMOS (MacGillivray and Stobie 1984) on the Schmidt plates were input into software described by Smriglio et al. (1986) as a method of automated two-dimensional stellar classification in the Vilnius seven-color intermediate band photometric system (Straizys and Zdanavicius 1970). A region of approximately two square degrees in Lyra centered on RA(1950) = 19h14.6min, DE(1950) = 30deg05' in the direction of the globular cluster M56 was studied and the two-dimensional classification of 752 stars in the magnitude range V = 11 to 15 mag was studied. The photometric system, the method of reduction, the classification procedure and errors were described by Smriglio et al. (1986). The number of stars for which all the six color indices in the Vilnius system are available is close to 3000, but the number of stars which have received two dimensional classification is 752. The success rate of classification appears to be high for stars brighter than V = 15.0 mag for which photometric accuracy is better than +-5%.
- ID:
- ivo://CDS.VizieR/J/A+A/433/117
- Title:
- L- & M-band imaging of the Galactic Center
- Short Name:
- J/A+A/433/117
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- Photometric data of the sources detected in NAOS/CONICA H- and K-band (1.6 and 2.1{mu}m) and ISAAC L- and M-band (3.8 and 4.7{mu}m) images of the Galactic Center, obtained in May and August 2002, are presented. For each source, name (if relevant), position offset, H-, K-, L- and M-band magnitude (if available) and observed colors (H-K, K-L and L-M) are given. The position zero-point is RA = 17:45:40.26, DE = -29:00:29.91 (IRS 16NE) in the J2000 system, with an offset of 2.83" (RA) and -0.91" (DE) from Sgr A*. The total photometric accuracy is +/-0.25mag in H- and K-band, +/-0.15mag in L- and M-band, positional accuracy is +/-0.09".
- ID:
- ivo://CDS.VizieR/J/ApJ/642/861
- Title:
- N-band imaging of the Galactic Center
- Short Name:
- J/ApJ/642/861
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- We present mid-infrared N- and Q-band photometry of the Galactic center from images obtained with the mid-infrared camera VISIR at the ESO VLT in 2004 May. The high resolution and sensitivity possible with VISIR enable us to investigate a total of over 60 pointlike sources, an unprecedented number for the Galactic center at these wavelengths. Combining these data with previous results at shorter wavelengths (Viehmann and coworkers, Cat. J/A+A/433/117) enables us to construct SEDs covering the H- to Q-band regions of the spectrum, i.e., 1.6-19.5{mu}m.
- ID:
- ivo://CDS.VizieR/II/177
- Title:
- Near IR Photometry of a Sample of IRAS Point Sources
- Short Name:
- II/177
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- The results are given for the 516 sources that we have observed from La Silla (452 in the southern hemisphere, plus 64 in the range 0 to 20deg). For each source, the IRAS name is given followed by the epoch of observation (with the convention given in Table 1), the J, H, K, L', M magnitudes that we have obtained, then three classifications based on IRAS Low Resolution Spectra (LRS), IRAS broad-band photometry (VH) and the combination of IRAS broad-band photometry with our near-infrared photometry (Val), and, finally, one association.
- ID:
- ivo://CDS.VizieR/J/AJ/109/517
- Title:
- Photoelectric types of bright galaxies
- Short Name:
- J/AJ/109/517
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- The photoelectric total magnitudes and color indices published in the Third Reference Catalogue of Bright Galaxies (RC3, Cat. <VII/155>) are based on an analysis of ~26000 B, 25000 B-V, and 17000 U-B multiaperture measurements available up to mid 1987 from nearly 350 sources. This paper provides the full details of the analysis and estimates of internal and external errors in the parameters. The derivation of the parameters is based on techniques described by de Vaucouleurs & Corwin (1977ApJS...33..219D) whereby photoelectric multiaperture data are fitted by mean Hubble-type-dependent curves which describe the integral of the B-band flux and the typical B-V and U-B integrated color gradients. A sophisticated analysis of the residuals of these measurements from the curves was made to allow for the random and systematic errors that affect such data. The result is a homogeneous set of total magnitudes B^A^_T_, total colors (B-V)_T_ and (U-B)_T_, and effective colors (B-V)_e_ and (U-B)_e_ for more than 3000 bright galaxies in RC3.
- ID:
- ivo://CDS.VizieR/J/MNRAS/424/2832
- Title:
- Pulsars in {gamma}-ray sources
- Short Name:
- J/MNRAS/424/2832
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- Machine learning, algorithms designed to extract empirical knowledge from data, can be used to classify data, which is one of the most common tasks in observational astronomy. In this paper, we focus on Bayesian data classification algorithms using the Gaussian mixture model and show two applications in pulsar astronomy. After reviewing the Gaussian mixture model and the related expectation-maximization algorithm, we present a data classification method using the Neyman-Pearson test. To demonstrate the method, we apply the algorithm to two classification problems. First, it is applied to the well-known period-period derivative diagram. Our second example is to calculate the likelihood of unidentified Fermi point sources being pulsars.
- ID:
- ivo://CDS.VizieR/J/A+A/617/A12
- Title:
- Taxonomic classification of asteroids
- Short Name:
- J/A+A/617/A12
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- We tabulate the taxonomic classification of 18265 asteroids catalogued by MOVIS. A probabilistic method and the k-nearest neighbors (KNN k=3) algorithm are used to assign a taxonomic type for each object.
- ID:
- ivo://CDS.VizieR/J/A+A/550/A120
- Title:
- Variability classification of CoRoT targets
- Short Name:
- J/A+A/550/A120
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- We present an improved method for automated stellar variability classification, using fundamental parameters derived from high resolution spectra, with the goal to improve the variability classification obtained using information derived from CoRoT light curves only. Although we focus on Giraffe spectra and CoRoT light curves in this work, the methods are much more widely applicable. In order to improve the variability classification obtained from the photometric time series, only rough estimates of the stellar physical parameters (Teff and logg) are needed because most variability types that overlap in the space of time series parameters, are well separated in the space of physical parameters (e.g. {gamma} Dor/SPB or {delta} Sct/{beta} Cep). In this work, several state-of-the-art machine learning techniques are combined to estimate these fundamental parameters from high resolution Giraffe spectra. Next, these parameters are used in a multi-stage Gaussian-Mixture classifier to perform an improved supervised variability classification of CoRoT light curves. The variability classifier can be used independently of the regression module that estimates the physical parameters, so that non-spectroscopic estimates derived e.g. from photometric colour indices can be used instead. Teff and logg are derived from Giraffe spectra, for 6832 CoRoT targets. The use of those parameters in addition to information extracted from the CoRoT light curves, significantly improves the results of our previous automated stellar variability classification. Several new pulsating stars are identified with high confidence levels, including hot pulsators such as SPB and {beta} Cep, and several {gamma} Dor-{delta} Sct hybrids. From our samples of new {gamma} Dor and {delta} Sct stars, we find strong indications that the instability domains for both types of pulsators are larger than previously thought.
- ID:
- ivo://CDS.VizieR/J/A+A/647/A116
- Title:
- YSO candidate catalog from ANN
- Short Name:
- J/A+A/647/A116
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- Observed young stellar objects (YSOs) are used to study star formation and characterize star-forming regions. For this purpose, YSO candidate catalogs are compiled from various surveys, especially in the infrared (IR), and simple selection schemes in color-magnitude diagrams (CMDs) are often used to identify and classify YSOs. We propose a methodology for YSO classification through machine learning (ML) using Spitzer IR data. We detail our approach in order to ensure reproducibility and provide an in-depth example on how to efficiently apply ML to an astrophysical classification. We used feed forward artificial neural networks (ANNs) that use the four IRAC bands (3.6, 4.5, 5.8, and 8 micron) and the 24 micron MIPS band from Spitzer to classify point source objects into CI and CII YSO candidates or as contaminants. We focused on nearby (~1kpc) star-forming regions including Orion and NGC 2264, and assessed the generalization capacity of our network from one region to another. We found that ANNs can be efficiently applied to YSO classification with a contained number of neurons (~25). Knowledge gathered on one star-forming region has shown to be partly efficient for prediction in new regions. The best generalization capacity was achieved using a combination of several star-forming regions to train the network. Carefully rebalancing the training proportions was necessary to achieve good results. We observed that the predicted YSOs are mainly contaminated by under-constrained rare subclasses like Shocks and polycyclic aromatic hydrocarbons (PAHs), or by the vastly dominant other kinds of stars (mostly on the main sequence). We achieved above 90% and 97% recovery rate for CI and CII YSOs, respectively, with a precision above 80% and 90% for our most general results. We took advantage of the great flexibility of ANNs to define, for each object, an effective membership probability to each output class. Using a threshold in this probability was found to efficiently improve the classification results at a reasonable cost of object exclusion. With this additional selection, we reached 90% and 97% precision on CI and CII YSOs, respectively, for more than half of them. Our catalog of YSO candidates in Orion (365 CI, 2381 CII) and NGC 2264 (101 CI, 469 CII) predicted by our final ANN, along with the class membership probability for each object, is publicly available at the CDS. Compared to usual CMD selection schemes, ANNs provide a possibility to quantitatively study the properties and quality of the classification. Although some further improvement may be achieved by using more powerful ML methods, we established that the result quality depends mostly on the training set construction. Improvements in YSO identification with IR surveys using ML would require larger and more reliable training catalogs, either by taking advantage of current and future surveys from various facilities like VLA, ALMA, or Chandra, or by synthesizing such catalogs from simulations.
