- ID:
- ivo://CDS.VizieR/J/ApJS/147/1
- Title:
- Classification of nearby galaxies
- Short Name:
- J/ApJS/147/1
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- A major problem in extragalactic astronomy is the inability to distinguish in a robust, physical, and model-independent way how galaxy populations are physically related to each other and to their formation histories. A similar, but distinct, and also long-standing question is whether the structural appearances of galaxies, as seen through their stellar light distributions, contain enough physical information to offer this classification. We argue through the use of 240 images of nearby galaxies that three model-independent parameters measured on a single galaxy image reveal its major ongoing and past formation modes and can be used as a robust classification system. These parameters quantitatively measure: the concentration (C), asymmetry (A), and clumpiness (S) of a galaxy's stellar light distribution. When combined into a three-dimensional "CAS" volume all major classes of galaxies in various phases of evolution are cleanly distinguished. We argue that these three parameters correlate with important modes of galaxy evolution: star formation and major merging activity. This is argued through the strong correlation of H{alpha} equivalent width and broadband colors with the clumpiness parameter S, the uniquely large asymmetries of 66 galaxies undergoing mergers, and the correlation of bulge to total light ratios, and stellar masses, with the concentration index. As an obvious goal is to use this system at high redshifts to trace evolution, we demonstrate that these parameters can be measured, within a reasonable and quantifiable uncertainty with available data out to z~3 using the Hubble Space Telescope GOODS ACS and Hubble Deep Field images.
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- ID:
- ivo://CDS.VizieR/J/AJ/140/34
- Title:
- Classification of nova light curves
- Short Name:
- J/AJ/140/34
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- We present a catalog of 93 very-well-observed nova light curves. The light curves were constructed from 229,796 individual measured magnitudes, with the median coverage extending to 8.0mag below peak and 26% of the light curves following the eruption all the way to quiescence. Our time-binned light curves are presented in figures and as complete tabulations. We also calculate and tabulate many properties about the light curves, including peak magnitudes and dates, times to decline by 2, 3, 6, and 9mag from maximum, the time until the brightness returns to quiescence, the quiescent magnitude, power-law indices of the decline rates throughout the eruption, the break times in this decline, plus many more properties specific to each nova class. We present a classification system for nova light curves based on the shape and the time to decline by 3mag from the peak (t3). The designations are "S" for smooth light curves (38% of the novae), "P" for plateaus (21%), "D" for dust dips (18%), "C" for cusp-shaped secondary maxima (1%), "O" for quasi-sinusoidal oscillations superposed on an otherwise smooth decline (4%), "F" for flat-topped light curves (2%), and "J" for jitters or flares superposed on the decline (16%). Our classification consists of this single letter followed by the t3 value in parentheses; so, for example, V1500 Cyg is S(4), GK Per is O(13), DQ Her is D(100), and U Sco is P(3).
- ID:
- ivo://CDS.VizieR/J/A+A/475/217
- Title:
- Classification of planetary nebulae
- Short Name:
- J/A+A/475/217
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- In this paper we present a re-analysis of the criteria used to characterize the Peimbert classes I, IIa, IIb, III and IV, through a statistical study of a large sample of planetary nebulae previously classified according to these groups. In the original classification, it is usual to find planetary nebulae that cannot be associated with a single type; these most likely have dubious classifications into two or three types. Statistical methods can greatly contribute in providing a better characterization of planetary nebulae groups. We use the Bayes Theorem to calculate the posterior probabilities for an object to be member of each of the types I, IIa, IIb, III and IV. This calculation is particularly important for planetary nebulae that are ambiguously classified in the traditional method. The posterior probabilities are defined from the probability density function of classificatory parameters of a well-defined sample, composed only by planetary nebulae unambiguously fitted into the Peimbert types. Because the probabilities depend on the available observational data, they are conditional probabilities, and, as new observational data are added to the sample, the classification of the nebula can be improved, to take into account this new information. This method differs from the original classificatory scheme, because it provides a quantitative result of the representativity of the object within its group. Also, through the use of marginal distributions it is possible to extend the Peimbert classification even to those objects for which only a few classificatory parameters are known.
- ID:
- ivo://CDS.VizieR/J/AJ/133/2495
- Title:
- Classification of RASS optical counterparts
- Short Name:
- J/AJ/133/2495
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- Previous work (Rutledge et al., 2000ApJS..131..335R) statistically identified 5492 optical counterparts, with >~90% confidence, from among the ~18000 X-ray sources appearing in the ROSAT All-Sky Survey Bright Source Catalog (RASS BSC; Voges et al. 1999, Cat. <IX/10>). Using low-resolution spectra in the wavelength range 3700-7900{AA}, we present spectroscopic classifications for 195 of these counterparts which have not previously been classified. Of these 195, we find 168 individual stars of F, G, K, or M type, 6 individual stars of unknown type, 6 double stars, 6 AGNs or galaxies, and 7 unclassifiable objects; the spectra of the 2 remaining objects were saturated.
- ID:
- ivo://CDS.VizieR/J/ApJ/756/27
- Title:
- Classification of sources from the 2XMMi-DR3 cat.
- Short Name:
- J/ApJ/756/27
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- We carry out classification of 4330 X-ray sources in the 2XMMi-DR3 catalog. They are selected under the requirement of being a point source with multiple XMM-Newton observations and at least one detection with the signal-to-noise ratio larger than 20. For about one-third of them we are able to obtain reliable source types from the literature. They mostly correspond to various types of stars (611), active galactic nuclei (AGNs, 753), and compact object systems (138) containing white dwarfs, neutron stars, and stellar-mass black holes. We find that about 99% of stars can be separated from other source types based on their low X-ray-to-IR flux ratios and frequent X-ray flares. AGNs have remarkably similar X-ray spectra, with the power-law photon index centered around 1.91+/-0.31, and their 0.2-4.5keV flux long-term variation factors have a median of 1.48, with 98.5% being less than 10. In contrast, 70% of compact object systems can be very soft or hard, highly variable in X-rays, and/or have very large X-ray-to-IR flux ratios, separating them from AGNs. Using these results, we derive a source type classification scheme to classify the other sources and find 644 candidate stars, 1376 candidate AGNs, and 202 candidate compact object systems, whose false identification probabilities are estimated to be about 1%, 3%, and 18%, respectively. There are still 320 sources associated with nearby galaxies and 151 in the Galactic plane, which we expect to be mostly compact object systems or background AGNs. We also have 100 candidate ultraluminous X-ray sources. They are found to be much less variable than other accreting compact objects.
- ID:
- ivo://CDS.VizieR/J/A+A/566/A50
- Title:
- Classification of stellar spectra 644-681nm
- Short Name:
- J/A+A/566/A50
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- We present a study of spectral diagnostics available from optical spectra with R=17000 obtained with the VLT/Giraffe HR15n setup, using observations from the Gaia-ESO Survey, on the {gamma} Vel young cluster, with the purpose of classifying these stars and finding their fundamental parameters. We define several spectroscopic indices, sampling the amplitude of TiO bands, the H{alpha} line core and wings, and temperature- and gravity-sensitive sets of lines, each useful as a Teff or logg indicator over a limited range of stellar spectral types. H{alpha} line indices are also useful as chromospheric activity or accretion indicators. Furthermore, we use all indices to define additional global Teff- and logg-sensitive indices {tau} and {gamma}, valid for the entire range of types in the observed sample.
- ID:
- ivo://CDS.VizieR/J/A+A/657/A138
- Title:
- Classification of Swift and XMM-Newton sources
- Short Name:
- J/A+A/657/A138
- Date:
- 22 Feb 2022
- Publisher:
- CDS
- Description:
- With the advent of very large X-ray surveys, an automated classification of X-ray sources becomes increasingly valuable. This work proposes a revisited naive Bayes classification of the X-ray sources in the Swift-XRT and XMM- Newton catalogs into four classes - AGN, stars, X-ray binaries (XRBs), and cataclysmic variables (CVs) - based on their spatial, spectral, and timing properties and their multiwavelength counterparts. An outlier measure is used to identify objects of other natures. The classifier is optimized to maximize the classification performance of a chosen class (here XRBs), and it is adapted to data mining purposes. We augmented the X-ray catalogs with multiwavelength data, source class, and variability properties. We then built a reference sample of about 25000 X-ray sources of known nature. From this sample, the distribution of each property was carefully estimated and taken as reference to assign probabilities of belonging to each class. The classification was then performed on the whole catalog, combining the information from each property. Using the algorithm on the Swift reference sample, we retrieved 99%, 98%, 92%, and 34% of AGN, stars, XRBs, and CVs, respectively, and the false positive rates are 3%, 1%, 9%, and 15%. Similar results are obtained on XMM sources. When applied to a carefully selected test sample, representing 55% of the X-ray catalog, the classification gives consistent results in terms of distributions of source properties. A substantial fraction of sources not belonging to any class is efficiently retrieved using the outlier measure, as well as AGN and stars with properties deviating from the bulk of their class. Our algorithm is then compared to a random forest method; the two showed similar performances, but the algorithm presented in this paper improved insight into the grounds of each classification. This robust classification method can be tailored to include additional or different source classes and can be applied to other X-ray catalogs. The transparency of the classification compared to other methods makes it a useful tool in the search for homogeneous populations or rare source types, including multi-messenger events. Such a tool will be increasingly valuable with the development of surveys of unprecedented size, such as LSST, SKA, and Athena, and the search for counterparts of multi-messenger events.
- ID:
- ivo://CDS.VizieR/J/MNRAS/414/1617
- Title:
- Classification of type Ia supernovae
- Short Name:
- J/MNRAS/414/1617
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- Type Ia supernovae (SNe Ia) spectra are compared using the coefficient of the largest wavelet scale in their decomposition. Two distinct subgroups have been identified, and their occurrence is discussed with regards to the use of SNe Ia as cosmological probes. Apart from the group of normal SNe, another trend characterized by intrinsically redder colours consists of many different SN events, which exhibit diverse properties. These include the interaction with the circumstellar material and the existence of a specific shell structure in or surrounding the SN ejecta or super-Chandrasekhar mass progenitors. Compared with normal objects, these SNe may violate the standard width-luminosity correction. This could influence the cosmological results if these are all calibrated equally, as their fraction among SNe Ia is not negligible when performing precision cosmology. Using the largest wavelet scale coefficient in combination with long-baseline B-I colours, we show how to disentangle the SN intrinsic colour from the part that corresponds to the reddening as a result of dust extinction in the host galaxy in the SALT2 colour parameter c.
- ID:
- ivo://CDS.VizieR/J/A+A/403/659
- Title:
- Classification of WR planetary nebulae
- Short Name:
- J/A+A/403/659
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- We analyse 42 emission-line nuclei of Planetary Nebulae (PNe), in the framework of a large spectrophotometric survey of [WC] nuclei of PNe conducted since 1994, using low/medium resolution spectra obtained at ESO and at OHP. We construct a grid of selected line-intensities (normalized to C IV-5806{AA}=100) ordered by decreasing ionisation potential going from 871 to 24eV. In this grid, the stars appear to belong clearly to prominent O (hot [WO1-4] types) or C (cooler [WC4-11] types) line-sequences, in agreement with the classification of massive WR stars applied to Central Stars of Planetary Nebulae (CSPNe) by Crowther et al. (1998MNRAS.296..367C, CMB98). We propose 20 selected line ratios and the FWHM of CIV and CIII lines as classification diagnostics, which agree well with the 7 line ratios and the FWHM proposed by CMB98.
- ID:
- ivo://CDS.VizieR/J/ApJ/786/20
- Title:
- Classification of 2XMM variable sources
- Short Name:
- J/ApJ/786/20
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- To maximize the discovery potential of future synoptic surveys, especially in the field of transient science, it will be necessary to use automatic classification to identify some of the astronomical sources. The data mining technique of supervised classification is suitable for this problem. Here, we present a supervised learning method to automatically classify variable X-ray sources in the Second XMM-Newton Serendipitous Source Catalog (2XMMi-DR2). Random Forest is our classifier of choice since it is one of the most accurate learning algorithms available. Our training set consists of 873 variable sources and their features are derived from time series, spectra, and other multi-wavelength contextual information. The 10 fold cross validation accuracy of the training data is ~97% on a 7 class data set. We applied the trained classification model to 411 unknown variable 2XMM sources to produce a probabilistically classified catalog. Using the classification margin and the Random Forest derived outlier measure, we identified 12 anomalous sources, of which 2XMM J180658.7-500250 appears to be the most unusual source in the sample. Its X-ray spectra is suggestive of a ultraluminous X-ray source but its variability makes it highly unusual. Machine-learned classification and anomaly detection will facilitate scientific discoveries in the era of all-sky surveys.