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1261. Australia Telescope-PMN Catalog of Southern Radio Sources
ID:
ivo://nasa.heasarc/atpmncat
Title:
Australia Telescope-PMN Catalog of Southern Radio Sources
Short Name:
ATPMNCAT
Date:
21 Feb 2025
Publisher:
NASA/GSFC HEASARC
Description:
This table contains a source catalog of 9040 radio sources derived from high-resolution observations of a selection of 8385 Parkes-MIT-NRAO (PMN) sources made with the Australia Telescope Compact Array (ATCA). The catalog lists flux density and structural measurements at 4.8 and 8.6 GHz, derived from observations of all PMN sources in the declination range -87 degrees < Dec < -38.5 degrees (exclusive of galactic latitudes |b| < 2 degrees) with PMN flux density S<sub>4850</sub> > 70mJy (> 50 mJy south of Dec = -73 degrees). The authors assess the quality of the data, which was gathered in the 1992-1994 time period, and the resulting source parameters. They describe the population of catalogued sources, and compare it to samples from complementary catalogs. In particular, they find 122 radio sources with highly probable association with gamma-ray sources observed by the orbiting Fermi Large Area Telescope (LAT), and 11 more radio sources with possible associations with Fermi LAT sources. This Australia Telescope PMN (ATPMN) catalog lists the source measurements of flux density, position and structure of a selection of sources from the PMN catalog. Each catalog entry corresponds to a discrete source observed by the ATCA. In many cases, a single PMN source yields several ATPMN sources. Apart from the name of the parent PMN source, there is no indication of physical association: multiple sources in the one field may be aligned by chance, or may be components of the one object. This catalog contains the following information for each source: position; the flux density at 4.8 and 8.6 GHz; uncertainties in each flux density; the source size modelled as an ellipse (major axes, minor axes, position angle) of the best fit for a Gaussian brightness distribution; the spectral index computed between 4.8 and 8.6 GHz; the uncertainty in the spectral index; a code denoting the epoch of the observation. In the table as given in the original reference, the positions were given with varying degrees of precision, from 0.001 to 1 second of time in RA and from 0.01 to 1 arcsecond in Declination. The authors state in Section 4 of the reference paper that the error in a position coordinate is less than 10 times the final digit given in the coordinate. The positions as displayed in this table do not reflect this system: e.g., a Dec value displayed as '-79 58 34.00' may have been given in the original table as '-79 58 34.00' or '-79 58 34.0' or '-79 58 34'. To recover this information about positional precision the HEASARC has created two additional parameters ra_accuracy and dec_accuracy which list the number of digits after the decimal point given in the original table for the RA and Dec, respectively. Thus, if ra_accuracy = 3, the RA was given to a precision of 0.001 s in the original table, implying that the actual error in RA was less than 10 * 0.001 = 0.01 s. This table was created by the HEASARC in July 2012 based on <a href="https://cdsarc.cds.unistra.fr/ftp/cats/J/MNRAS/422/1527">CDS Catalog J/MNRAS/422/1527</a> file atpmncat.dat. This is a service provided by NASA HEASARC .
1262. Australia Telescope PMN follow-up survey
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.
1263. Autoclassification of the variable 3XMM sources
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.
1264. Automated classification of ASAS variables
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.
1265. Automated classification of HIP variables
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.
1266. Automated triage and vetting of TESS candidates
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.
1267. Automatic galaxy detection & classification
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.
1268. Automatic stellar spectral classification
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.
1269. Autoregressive planet search for Kepler stars
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.
1270. Autoregressive planet search: irregular time series
ID:
ivo://CDS.VizieR/J/AJ/158/59
Title:
Autoregressive planet search: irregular time series
Short Name:
J/AJ/158/59
Date:
21 Oct 2021
Publisher:
CDS
Description:
Sensitive signal processing methods are needed to detect transiting planets from ground-based photometric surveys. Caceres et al. (2019AJ....158...58C) show that the autoregressive planet search (ARPS) method - a combination of autoregressive integrated moving average (ARIMA) parametric modeling, a new transit comb filter (TCF) periodogram, and machine learning classification - is effective when applied to evenly spaced light curves from space-based missions. We investigate here whether ARIMA and TCF will be effective for ground-based survey light curves that are often sparsely sampled with high noise levels from atmospheric and instrumental conditions. The ARPS procedure is applied to selected light curves with strong planetary signals from the Kepler mission that have been altered to simulate the conditions of ground-based exoplanet surveys. Typical irregular cadence patterns are used from the Hungarian-made Automated Telescope Network-South (HATSouth) survey. We also evaluate recovery of known planets from HATSouth. Simulations test transit signal recovery as a function of cadence pattern and duration, stellar magnitude, planet orbital period, and transit depth. Detection rates improve for shorter periods and deeper transits. The study predicts that the ARPS methodology will detect planets with >~0.1% transit depth and periods ~<40 days in HATSouth stars brighter than ~15 mag. ARPS methodology is therefore promising for planet discovery from ground-based exoplanet surveys with sufficiently dense cadence patterns.

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