- ID:
- ivo://CDS.VizieR/J/MNRAS/422/1527
- Title:
- Australia Telescope PMN follow-up survey
- Short Name:
- J/MNRAS/422/1527
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- We present a source catalogue derived from high-resolution observations of a selection of PMN sources with the Australia Telescope Compact Array (ATCA). The catalogue lists 8385 sources with flux-density measurements at 4.8 and 8.6GHz, derived from observations of all fields in the declination range -87{deg}<DE<-38.5{deg} (exclusive of galactic latitudes |b|<2{deg{) with PMN flux-density S_4850_>70mJy (50mJy south of DE=-73{deg}). We assess the quality of the data, which was gathered in 1992-1994, and the resulting source parameters. We describe the population of catalogued sources, and compare it to samples from complementary catalogues. In particular we find 126 radio sources with probable association with gamma-ray sources observed by the orbiting Fermi Large Area Telescope.
Number of results to display per page
Search Results
- ID:
- ivo://CDS.VizieR/J/ApJ/813/28
- Title:
- Autoclassification of the variable 3XMM sources
- Short Name:
- J/ApJ/813/28
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- In the current era of large surveys and massive data sets, autoclassification of astrophysical sources using intelligent algorithms is becoming increasingly important. In this paper we present the catalog of variable sources in the Third XMM-Newton Serendipitous Source catalog (3XMM) autoclassified using the Random Forest machine learning algorithm (RF). We used a sample of manually classified variable sources from the second data release of the XMM-Newton catalogs (2XMMi-DR2) to train the classifier, obtaining an accuracy of ~92%. We also evaluated the effectiveness of identifying spurious detections using a sample of spurious sources, achieving an accuracy of ~95%. Manual investigation of a random sample of classified sources confirmed these accuracy levels and showed that the Random Forest machine learning algorithm is highly effective at automatically classifying 3XMM sources. Here we present the catalog of classified 3XMM variable sources. We also present three previously unidentified unusual sources that were flagged as outlier sources by the algorithm: a new candidate supergiant fast X-ray transient, a 400s X-ray pulsar, and an eclipsing 5hr binary system coincident with a known Cepheid.
- ID:
- ivo://CDS.VizieR/J/MNRAS/358/30
- Title:
- Automated classification of ASAS variables
- Short Name:
- J/MNRAS/358/30
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- With the advent of surveys generating multi-epoch photometry and the discovery of large numbers of variable stars, the classification of these stars has to be automatic. We have developed such a classification procedure for about 1700 stars from the variable star catalogue of the All-Sky Automated Survey 1-2 (ASAS 1-2) by selecting the periodic stars and by applying an unsupervised Bayesian classifier using parameters obtained through a Fourier decomposition of the light curve. For irregular light curves we used the period and moments of the magnitude distribution for the classification. In the case of ASAS 1-2, 83 per cent of variable objects are red giants. A general relation between the period and amplitude is found for a large fraction of those stars. The selection led to 302 periodic and 1429 semiperiodic stars, which are classified in six major groups: eclipsing binaries, 'sinusoidal curves', Cepheids, small amplitude red variables, SR and Mira stars. The type classification error level is estimated to be about 7 per cent.
- ID:
- ivo://CDS.VizieR/J/MNRAS/414/2602
- Title:
- Automated classification of HIP variables
- Short Name:
- J/MNRAS/414/2602
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- We present an evaluation of the performance of an automated classification of the Hipparcos periodic variable stars into 26 types. The sub-sample with the most reliable variability types available in the literature is used to train supervised algorithms to characterize the type dependencies on a number of attributes. The most useful attributes evaluated with the random forest methodology include, in decreasing order of importance, the period, the amplitude, the V-I colour index, the absolute magnitude, the residual around the folded light-curve model, the magnitude distribution skewness and the amplitude of the second harmonic of the Fourier series model relative to that of the fundamental frequency.
- ID:
- ivo://CDS.VizieR/J/AJ/158/25
- Title:
- Automated triage and vetting of TESS candidates
- Short Name:
- J/AJ/158/25
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- NASA's Transiting Exoplanet Survey Satellite (TESS) presents us with an unprecedented volume of space-based photometric observations that must be analyzed in an efficient and unbiased manner. With at least ~1000000 new light curves generated every month from full-frame images alone, automated planet candidate identification has become an attractive alternative to human vetting. Here we present a deep learning model capable of performing triage and vetting on TESS candidates. Our model is modified from an existing neural network designed to automatically classify Kepler candidates, and is the first neural network to be trained and tested on real TESS data. In triage mode, our model can distinguish transit-like signals (planet candidates and eclipsing binaries) from stellar variability and instrumental noise with an average precision (the weighted mean of precisions over all classification thresholds) of 97.0% and an accuracy of 97.4%. In vetting mode, the model is trained to identify only planet candidates with the help of newly added scientific domain knowledge, and achieves an average precision of 69.3% and an accuracy of 97.8%. We apply our model on new data from Sector 6, and present 288 new signals that received the highest scores in triage and vetting and were also identified as planet candidates by human vetters. We also provide a homogeneously classified set of TESS candidates suitable for future training.
- ID:
- ivo://CDS.VizieR/J/A+A/494/739
- Title:
- Automatic classification of OGLE variables
- Short Name:
- J/A+A/494/739
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- Scientific exploitation of large variability databases can only be fully optimized if these archives contain, besides the actual observations, annotations about the variability class of the objects they contain. Supervised classification of observations produces these tags, and makes it possible to generate refined candidate lists and catalogues suitable for further investigation. We aim to extend and test the classifiers presented in a previous work against an independent dataset. We complement the assessment of the validity of the classifiers by applying them to the set of OGLE light curves treated as variable objects of unknown class. The results are compared to published classification results based on the so-called extractor methods. Two complementary analyses are carried out in parallel. In both cases, the original time series of OGLE observations of the Galactic bulge and Magellanic Clouds are processed in order to identify and characterize the frequency components. In the first approach, the classifiers are applied to the data and the results analyzed in terms of systematic errors and differences between the definition samples in the training set and in the extractor rules. In the second approach, the original classifiers are extended with colour information and, again, applied to OGLE light curves. We have constructed a classification system that can process huge amounts of time series in negligible time and provide reliable samples of the main variability classes. We have evaluated its strengths and weaknesses and provide potential users of the classifier with a detailed description of its characteristics to aid in the interpretation of classification results. Finally, we apply the classifiers to obtain object samples of classes not previously studied in the OGLE database and analyse the results. We pay specific attention to the B-stars in the samples, as their pulsations are strongly dependent on metallicity.
- ID:
- ivo://CDS.VizieR/J/MNRAS/463/2939
- Title:
- Automatic galaxy detection & classification
- Short Name:
- J/MNRAS/463/2939
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- We present a study on galaxy detection and shape classification using topometric clustering algorithms. We first use the DBSCAN algorithm to extract, from CCD frames, groups of adjacent pixels with significant fluxes and we then apply the DENCLUE algorithm to separate the contributions of overlapping sources. The DENCLUE separation is based on the localization of pattern of local maxima, through an iterative algorithm, which associates each pixel to the closest local maximum. Our main classification goal is to take apart elliptical from spiral galaxies. We introduce new sets of features derived from the computation of geometrical invariant moments of the pixel group shape and from the statistics of the spatial distribution of the DENCLUE local maxima patterns. Ellipticals are characterized by a single group of local maxima, related to the galaxy core, while spiral galaxies have additional groups related to segments of spiral arms. We use two different supervised ensemble classification algorithms: Random Forest and Gradient Boosting. Using a sample of ~=24000 galaxies taken from the Galaxy Zoo 2 main sample with spectroscopic redshifts, and we test our classification against the Galaxy Zoo 2 catalogue. We find that features extracted from our pipeline give, on average, an accuracy of ~=93 per cent, when testing on a test set with a size of 20 per cent of our full data set, with features deriving from the angular distribution of density attractor ranking at the top of the discrimination power.
- ID:
- ivo://CDS.VizieR/J/A+A/392/1129
- Title:
- Automatic observation rendering (AMORE)
- Short Name:
- J/A+A/392/1129
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- A new method, AMORE (AutoMatic Observation REnderer) - based on a genetic algorithm optimizer, is presented for the automated study of colour-magnitude diagrams. The method combines several stellar population synthesis tools developed in the last decade by or in collaboration with the Padova group. Our method is able to recover, within the uncertainties, the parameters - distance, extinction, age, metallicity, index of a power-law initial mass function and the index of an exponential star formation rate - from a reference synthetic stellar population. No a priori information is inserted to recover the parameters, which is done simultaneously and not one at a time. Examples are given to demonstrate and to better understand biases in the results, if one of the input parameters is deliberately set fixed to a non-optimum value.
- ID:
- ivo://CDS.VizieR/J/A+A/538/A76
- Title:
- Automatic stellar spectral classification
- Short Name:
- J/A+A/538/A76
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- As part of a project aimed at deriving extinction-distances for thirty-five planetary nebulae, spectra of a few thousand stars were analyzed to determine their spectral type and luminosity class. We present here the automatic spectral classification process used to classify stellar spectra. This system can be used to classify any other stellar spectra with similar or higher signal-to-noise ratios. Spectral classification was performed using a system of artificial neural networks that were trained with a set of line-strength indices selected among the spectral lines most sensitive to temperature and the best luminosity tracers. The training and validation processes of the neural networks are discussed and the results of additional validation probes, designed to ensure the accuracy of the spectral classification, are presented.
- ID:
- ivo://CDS.VizieR/J/AJ/158/58
- Title:
- Autoregressive planet search for Kepler stars
- Short Name:
- J/AJ/158/58
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- The 4 yr light curves of 156717 stars observed with NASA's Kepler mission are analyzed using the autoregressive planet search (ARPS) methodology described by Caceres et al. (2019AJ....158...57C). The three stages of processing are maximum-likelihood ARIMA modeling of the light curves to reduce stellar brightness variations, constructing the transit comb filter periodogram to identify transit-like periodic dips in the ARIMA residuals, and Random Forest classification trained on Kepler team confirmed planets using several dozen features from the analysis. Orbital periods between 0.2 and 100 days are examined. The result is a recovery of 76% of confirmed planets, 97% when period and transit depth constraints are added. The classifier is then applied to the full Kepler data set; 1004 previously noticed and 97 new stars have light-curve criteria consistent with the confirmed planets, after subjective vetting removes clear false alarms and false positive cases. The 97 Kepler ARPS candidate transits mostly have periods of P<10 days; many are ultrashort period hot planets with radii <1% of the host star. Extensive tabular and graphical output from the ARPS time series analysis is provided to assist in other research relating to the Kepler sample.
