Recent results suggest that the first steps towards planet formation may be already taking place in protoplanetary discs during the first 100000yr after stars form. It is therefore crucial to unravel the physical and chemical structures of such discs in their earliest stages while they are still embedded in their natal envelopes and compare them with more evolved systems. The purpose of this paper is to explore the structure of a line-rich Class I protobinary source, Oph-IRS 67, and analyse the differences and similarities with Class 0 and Class II sources. We present a systematic molecular line study of IRS 67 with the Submillimeter Array (SMA) on 1-2" (150-300AU) scales. The wide instantaneous band-width of the SMA observations (~30GHz) provide detections of a range of molecular transitions that trace different physics, such as CO isotopologues, sulphur-bearing species, deuterated species, and carbon-chain molecules. We see significant differences between different groups of species. For example, the CO isotopologues and sulphur-bearing species show a rotational profile and are tracing the larger-scale circumbinary disc structure, while CN, DCN, and carbon-chain molecules peak at the southern edge of the disc at blue-shifted velocities. In addition, the cold gas tracer DCO^+^ is seen beyond the extent of the circumbinary disc. The detected molecular transitions can be grouped into three main components: cold regions far from the system, the circumbinary disc, and a UV-irradiated region likely associated with the surface layers of the disc that are reached by the UV radiation from the sources. The different components are consistent with the temperature structure derived from the ratio of two H_2_CO transitions, that is, warm temperatures are seen towards the outflow direction, lukewarm temperatures are associated with the UV-radiated region, and cold temperatures are related with the circumbinary disc structure. The chemistry towards IRS 67 shares similarities with both Class 0 and Class II sources, possibly due to the high gas column density and the strong UV radiation arising from the binary system. IRS 67 is, therefore, highlighting the intermediate chemistry between deeply embedded sources and T-Tauri discs.
Broadband photometry offers a time and cost effective method to reconstruct the continuum emission of celestial objects. Thus, photometric redshift estimation has supported the scientific exploitation of extragalactic multiwavelength surveys for more than twenty years. Deep fields have been the backbone of galaxy evolution studies and have brought forward a collection of various approaches in determining photometric redshifts. In the era of precision cosmology, with the upcoming Euclid and LSST surveys, very tight constraints are put on the expected performance of photometric redshift estimation using broadband photometry, thus new methods have to be developed in order to reach the required performance. We present a novel automatic method of optimizing photometric redshift performance, the classification-aided photometric redshift estimation (CPz). The main feature of CPz is the unified treatment of all classes of objects detected in extragalactic surveys: galaxies of any type (passive, starforming and starbursts), active galactic nuclei (AGN), quasi-stellar objects (QSO), stars and also includes the identification of potential photometric redshift catastrophic outliers. The method operates in three stages. First, the photometric catalog is confronted with star, galaxy and QSO model templates by means of spectral energy distribution fitting. Second, three machine-learning classifiers are used to identify 1) the probability of each source to be a star, 2) the optimal photometric redshift model library set-up for each source and 3) the probability to be a photometric redshift catastrophic outlier. Lastly, the final sample is assembled by identifying the probability thresholds to be applied on the outcome of each of the three classifiers. Hence, with the final stage we can create a sample appropriate for a given science case, for example favoring purity over completeness. We apply our method to the near- infrared VISTA public surveys, matched with optical photometry from CFHTLS, KiDS and SDSS, mid-infrared WISE photometry and ultra-violet photometry from the Galaxy Evolution Explorer (GALEX). We show that CPz offers improved photometric redshift performance for both normal galaxies and AGN without the need for extra X-ray information.
Classification and vsini of Vega-type and PMS stars
Short Name:
J/A+A/378/116
Date:
21 Oct 2021
Publisher:
CDS
Description:
File table1.dat contains the log of the spectroscopic observations of the stars in the EXPORT sample taken with the Isaac Newton Telescope during the 1998 International Time Campaigns at the Canary Islands' Observatories. File table2.dat contains the log of the spectroscopic observations of the stars in the EXPORT sample taken with the William Herschel Telescope during the 1998 International Time Campaigns at the Canary Islands' Observatories. File table6.dat contains the results of the spectral classification and the projected rotational velocities for the stars in the EXPORT sample with comparisons with results from previous work.
An artificial neural network (ANN) scheme has been employed that uses a supervised back-propagation algorithm to classify 2000 bright sources from the Calgary database of Infrared Astronomical Satellite (IRAS) spectra in the region 8-23{mu}m. The database has been classified into 17 predefined classes based on the spectral morphology. We have been able to classify over 80% of the sources correctly in the first instance. The speed and robustness of the scheme will allow us to classify the whole of the Low Resolution Spectrometer database, containing more than 50,000 sources, in the near future.
Context: The stellar populations in the central region of the Galaxy are poorly known because of the high visual extinction and very great source density in this direction. Aims: To use recent infrared surveys for studying the dusty stellar objects in this region. Methods: We analyse the content of a ~20x20arcmin^2 field centred at (l,b)=(-0.27,-0.06) observed at 7 and 15 microns as part of the ISOGAL survey. These ISO observations are more than an order of magnitude better in sensitivity and spatial resolution than the IRAS observations. The sources are cross-associated with other catalogues to identify various types of objects. We then derive criteria to distinguish young objects from post-main sequence stars. Results: We find that a sample of about 50 young stellar objects and ultra-compact HII regions emerges, out of a population of evolved AGB stars. We demonstrate that the sources colours and spatial extents, as they appear in the ISOGAL catalogue, possibly complemented with MSX photometry at 21 microns, can be used to determine whether the ISOGAL sources brighter than 300mJy at 15 microns (or [15]<4.5mag) are young objects or late-type evolved stars.
The Chandra Carina Complex Project (CCCP) provides a sensitive X-ray survey of a nearby starburst region over >1deg^2^ in extent. Thousands of faint X-ray sources are found, many concentrated into rich young stellar clusters. However, significant contamination from unrelated Galactic and extragalactic sources is present in the X-ray catalog. We describe the use of a Naive Bayes classifier to assign membership probabilities to individual sources, based on source location, X-ray properties, and visual/infrared properties. For the particular membership decision rule adopted, 75% of CCCP sources are classified as members, 11% are classified as contaminants, and 14% remain unclassified. The resulting sample of stars likely to be Carina members is used in several other studies, which appear in this special issue devoted to the CCCP.
We present the results of an isophotal shape analysis of three samples of galaxies in the Coma cluster. Quantitative morphology, together with structural and photometric parameters, is given for each galaxy. Special emphasis has been placed on the detailed classification of early-type galaxies. The three samples are: i) a sample of 97 early-type galaxies brighter than m_B_=17.00 falling within one degree from the center of the Coma cluster; these galaxies were observed with CCD cameras, mostly in good to excellent resolution conditions; ii) a magnitude complete sample of 107 galaxies of all morphological types down to m_B_=17.00 falling in a circular region of 50arcmin diameter, slightly offcentered to the North-West of the cluster center; the images for this and the next sample come from digitized photographic plates; iii) a complete comparison sample of 26 galaxies of all morphological types down to m_R_=16.05 (or m_B_=~17.5), also in a region of 50arcmin diameter, but centered 2.6degrees West of the cluster center. The reliability of our morphological classifications and structural parameters of galaxies, down to the adopted magnitude limits, is assessed by comparing the results on those galaxies for which we had images taken with different instrumentation and/or seeing conditions, and by comparing our results with similar data from other observers.
The main goal of this work was to further investigate the classification of emission-line galaxies from the "Spectrophotometric Catalogue of HII galaxies" by Terlevich et al. (1991, Cat. J/A+AS/91/285) in a homogeneous and objective way, using the three line-ratio diagrams of Veilleux & Osterbrock (1987ApJS...63..295V). The re-measurements of the most important nebular lines and a revised classification are presented for 314 narrow-emission-line galaxies (represented by 405 spectra) from Terlevich's et al. (1991, Cat. J/A+AS/91/285) catalogue. The revised catalogue contains 267 HII galaxies, 25 Seyfert2 galaxies, 3 LINERs, 4 "revised" galaxies, 13 "transition" galaxies and 2 "ambiguous" galaxies.
Classification of Fermi blazar cand. from the 4FGL
Short Name:
J/ApJ/887/134
Date:
21 Oct 2021
Publisher:
CDS
Description:
The recently published fourth Fermi Large Area Telescope source catalog (4FGL) reports 5065 gamma-ray sources in terms of direct observational gamma-ray properties. Among the sources, the largest population is the active galactic nuclei (AGNs), which consists of 3137 blazars, 42 radio galaxies, and 28 other AGNs. The blazar sample comprises 694 flat-spectrum radio quasars (FSRQs), 1131 BL Lac-type objects (BL Lacs), and 1312 blazar candidates of an unknown type (BCUs). The classification of blazars is difficult using optical spectroscopy given the limited knowledge with respect to their intrinsic properties, and the limited availability of astronomical observations. To overcome these challenges, machine-learning algorithms are being investigated as alternative approaches. Using the 4FGL catalog, a sample of 3137 Fermi blazars with 23 parameters is systematically selected. Three established supervised machine-learning algorithms (random forests (RFs), support vector machines (SVMs), artificial neural networks (ANNs)) are employed to general predictive models to classify the BCUs. We analyze the results for all of the different combinations of parameters. Interestingly, a previously reported trend the use of more parameters leading to higher accuracy is not found. Considering the least number of parameters used, combinations of eight, 12 or 10 parameters in the SVM, ANN, or RF generated models achieve the highest accuracy (Accuracy ~91.8%, or ~92.9%). Using the combined classification results from the optimal combinations of parameters, 724 BL Lac type candidates and 332 FSRQ type candidates are predicted; however, 256 remain without a clear prediction.
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.
