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1. Classification of Hipparcos variables
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.
2. 2-D Classification, Vilnius Photometry M56 Region
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%.
3. L- & M-band imaging of the Galactic Center
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".
4. N-band imaging of the Galactic Center
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.
5. Pulsars in {gamma}-ray sources
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.
6. Variability classification of CoRoT targets
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.
7. YSO candidate catalog from ANN
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.
8. YSOs in California Molecular Cloud
ID:
ivo://CDS.VizieR/J/A+A/606/A100
Title:
YSOs in California Molecular Cloud
Short Name:
J/A+A/606/A100
Date:
21 Oct 2021
Publisher:
CDS
Description:
We present new high resolution and dynamic range dust column density and temperature maps of the California Molecular Cloud derived from a combination of Planck and Herschel dust-emission maps, and 2MASS NIR dust-extinction maps. We used these data to determine the ratio of the 2.2{mu}m extinction coefficient to the 850{mu}m opacity and found the value to be close to that found in similar studies of the Orion B and Perseus clouds but higher than that characterizing the Orion A cloud, indicating that variations in the fundamental optical properties of dust may exist between local clouds. We show that over a wide range of extinction, the column density probability distribution function (pdf) of the cloud can be well described by a simple power law (i.e., PDF_N_{prop.to}A_K_^-n^) with an index (n=4.0+/-0.1) that represents a steeper decline with A_K_ than found (n~=3) in similar studies of the Orion and Perseus clouds. Using only the protostellar population of the cloud and our extinction maps we investigate the Schmidt relation, that is, the relation between the protostellar surface density, {Sigma}_*_, and extinction, A_K_, within the cloud. We show that {Sigma}_*_ is directly proportional to the ratio of the protostellar and cloud pdfs, i.e., PDF_*_(A_K_)/PDF_N_(A_K_). We use the cumulative distribution of protostars to infer the functional forms for both {Sigma}_*_ and PDF_*_. We find that {Sigma}_*_ is best described by two power-law functions. At extinctions A_K_<=2.5mag, {Sigma}_*_{prop.to}A_K_^{beta}^ with {beta}=3.3 while at higher extinctions {beta}=2.5, both values steeper than those (~=2) found in other local giant molecular clouds (GMCs). We find that PDF_*_ is a declining function of extinction also best described by two power-laws whose behavior mirrors that of {Sigma}_*_. Our observations suggest that variations both in the slope of the Schmidt relation and in the sizes of the protostellar populations between GMCs are largely driven by variations in the slope, n, of PDF_N_(A_K_). This confirms earlier studies suggesting that cloud structure plays a major role in setting the global star formation rates in GMCs

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