Search for unusual objects in the WISE Survey

Automatic source detection and classification tools based on machine learning (ML) algorithms are growing in popularity due to their efficiency when dealing with large amounts of data simultaneously and their ability to work in multidimensional parameter spaces. In this work, we present a new, automated method of outlier selection based on support vector machine (SVM) algorithm called one-class SVM (OCSVM), which uses the training data as one class to construct a model of 'normality' in order to recognize novel points. We test the performance of OCSVM algorithm on \textit{Wide-field Infrared Survey Explorer (WISE)} data trained on the Sloan Digital Sky Survey (SDSS) sources. Among others, we find $\sim 40,000$ sources with abnormal patterns which can be associated with obscured and unobscured active galactic nuclei (AGN) source candidates. We present the preliminary estimation of the clustering properties of these objects and find that the unobscured AGN candidates are preferentially found in less massive dark matter haloes ($M_{DMH}\sim10^{12.4}$) than the obscured candidates ($M_{DMH}\sim 10^{13.2}$). This result contradicts the unification theory of AGN sources and indicates that the obscured and unobscured phases of AGN activity take place in different evolutionary paths defined by different environments.Comment: 4 figures, 6 page

Automated novelty detection in the WISE survey with one-class support vector machines

Wide-angle photometric surveys of previously uncharted sky areas or wavelength regimes will always bring in unexpected sources whose existence and properties cannot be easily predicted from earlier observations: novelties or even anomalies. Such objects can be efficiently sought for with novelty detection algorithms. Here we present an application of such a method, called one-class support vector machines (OCSVM), to search for anomalous patterns among sources preselected from the mid-infrared AllWISE catalogue covering the whole sky. To create a model of expected data we train the algorithm on a set of objects with spectroscopic identifications from the SDSS DR13 database, present also in AllWISE. OCSVM detects as anomalous those sources whose patterns - WISE photometric measurements in this case - are inconsistent with the model. Among the detected anomalies we find artefacts, such as objects with spurious photometry due to blending, but most importantly also real sources of genuine astrophysical interest. Among the latter, OCSVM has identified a sample of heavily reddened AGN/quasar candidates distributed uniformly over the sky and in a large part absent from other WISE-based AGN catalogues. It also allowed us to find a specific group of sources of mixed types, mostly stars and compact galaxies. By combining the semi-supervised OCSVM algorithm with standard classification methods it will be possible to improve the latter by accounting for sources which are not present in the training sample but are otherwise well-represented in the target set. Anomaly detection adds flexibility to automated source separation procedures and helps verify the reliability and representativeness of the training samples. It should be thus considered as an essential step in supervised classification schemes to ensure completeness and purity of produced catalogues.Comment: 14 pages, 15 figure

Radio-Infrared Correlation for Local Dusty Galaxies and Dusty AGNs from the AKARI All Sky Survey

We use the new release of the AKARI Far-Infrared all sky Survey matched with the NVSS radio database to investigate the local ($z<0.25$) far infrared-radio correlation (FIRC) of different types of extragalactic sources. To obtain the redshift information for the AKARI FIS sources we crossmatch the catalogue with the SDSS DR8. This also allows us to use emission line properties to divide sources into four categories: i) star-forming galaxies (SFGs), ii) composite galaxies (displaying both star-formation and active nucleus components), iii) Seyfert galaxies, and iv) low-ionization nuclear emission-line region (LINER) galaxies. We find that the Seyfert galaxies have the lowest FIR/radio flux ratios and display excess radio emission when compared to the SFGs. We conclude that FIRC can be used to separate SFGs and AGNs only for the most radio-loud objects.Comment: 9 pages, accepted to PAS

Catalog of quasars from the Kilo-Degree Survey Data Release 3

We present a catalog of quasars selected from broad-band photometric ugri data of the Kilo-Degree Survey Data Release 3 (KiDS DR3). The QSOs are identified by the random forest (RF) supervised machine learning model, trained on SDSS DR14 spectroscopic data. We first cleaned the input KiDS data from entries with excessively noisy, missing or otherwise problematic measurements. Applying a feature importance analysis, we then tune the algorithm and identify in the KiDS multiband catalog the 17 most useful features for the classification, namely magnitudes, colors, magnitude ratios, and the stellarity index. We used the t-SNE algorithm to map the multi-dimensional photometric data onto 2D planes and compare the coverage of the training and inference sets. We limited the inference set to r<22 to avoid extrapolation beyond the feature space covered by training, as the SDSS spectroscopic sample is considerably shallower than KiDS. This gives 3.4 million objects in the final inference sample, from which the random forest identified 190,000 quasar candidates. Accuracy of 97%, purity of 91%, and completeness of 87%, as derived from a test set extracted from SDSS and not used in the training, are confirmed by comparison with external spectroscopic and photometric QSO catalogs overlapping with the KiDS footprint. The robustness of our results is strengthened by number counts of the quasar candidates in the r band, as well as by their mid-infrared colors available from WISE. An analysis of parallaxes and proper motions of our QSO candidates found also in Gaia DR2 suggests that a probability cut of p(QSO)>0.8 is optimal for purity, whereas p(QSO)>0.7 is preferable for better completeness. Our study presents the first comprehensive quasar selection from deep high-quality KiDS data and will serve as the basis for versatile studies of the QSO population detected by this survey.Comment: Data available from the KiDS website at http://kids.strw.leidenuniv.nl/DR3/quasarcatalog.php and the source code from https://github.com/snakoneczny/kids-quasar

Total infrared luminosity estimation from local galaxies in AKARI all sky survey

We aim to use the a new and improved version of AKARI all sky survey catalogue of far-infrared sources to recalibrate the formula to derive the total infrared luminosity. We cross-match the faint source catalogue (FSC) of IRAS with the new AKARI-FIS and obtained a sample of 2430 objects. Then we calculate the total infrared (TIR) luminosity $L_{\textrm{TIR}}$ from the Sanders at al. (1996) formula and compare it with total infrared luminosity from AKARI FIS bands to obtain new coefficients for the general relation to convert FIR luminosity from AKARI bands to the TIR luminosity.Comment: 4 pages, 4 figure

Novelty detection in search of anomalous objects within the WISE all sky survey

Instrumentatio

Morphometric parameters of cardiac implantable electronic device (CIED) pocket walls observed on device replacement

Background: The final stage of a conventional de-novo cardiac implantable electronic device (CIED) implantation procedure with transvenous lead insertion involves the formation of a pocket by tissue separation superficial to the pectoralis major muscle in the right or left infraclavicular region, where the device is subsequently placed. Over time, a scar “capsule” is formed around the CIED as a result of normal biological remodelling. Materials and methods: The purpose of this study was to analyse the structure and present the variations of CIED capsules observed during device replacement. The nature and extent of this local tissue remodelling, which had occurred from the time of device implantation to its replacement in 2016 (10 ± 3.1 years), was analysed in 100 patients (mean age 77.1 ± 14.5 years), including 45 women and 55 men. Results: The most prevalent types of “capsules” (70% of cases) were those with similar thickness of both walls or a slightly thicker posterior (&lt; 1.0 mm) than anterior wall (&lt; 0.5 mm). The second most common capsule type (23% of cases) was characterised by a significantly thicker posterior wall of scar tissue (&gt; 1.0 mm). The third group of capsules was characterised by various degrees of wall calcification (7% of cases). Conclusions: The extent and nature of scar tissue structure in the CIED pocket walls seem to correlate with the relative position of cardiac lead loops with respect to the device itself; where the more extensive scarring is likely to result from pocket wall irritation in the capsule formation phase due to lead movements underneath the device. The group of cases with calcified capsules was characterised by “old” device pockets (&gt; 13 years) and the oldest population (patients in their 80s and 90s)

Catalog of quasars from the Kilo-Degree Survey Data Release 3

We present a catalog of quasars selected from broad-band photometric ugri data of the Kilo-Degree Survey Data Release 3 (KiDS DR3). The QSOs are identified by the random forest (RF) supervised machine learning model, trained on Sloan Digital Sky Survey (SDSS) DR14 spectroscopic data. We first cleaned the input KiDS data of entries with excessively noisy, missing or otherwise problematic measurements. Applying a feature importance analysis, we then tune the algorithm and identify in the KiDS multiband catalog the 17 most useful features for the classification, namely magnitudes, colors, magnitude ratios, and the stellarity index. We used the t-SNE algorithm to map the multidimensional photometric data onto 2D planes and compare the coverage of the training and inference sets. We limited the inference set to r 0.8 is optimal for purity, whereas pQSO > 0.7 is preferable for better completeness. Our study presents the first comprehensive quasar selection from deep high-quality KiDS data and will serve as the basis for versatile studies of the QSO population detected by this survey

Contribuição dos humanos, cães e gatos à transmissão do Trypanosoma cruzi na Região do Chaco Argentino

Foi determinada a prevalência da infecção por T. cruzi nos humanos, cães e gatos, pertencentes a 47 rancherías em três povoados rurais; Guanaco Muerto (Córdoba), La Invernada e Amamás (Santiago del Estero), mediante reações seroldgicas e xenodiagnóstico. Poram examinadas 245 pessoas, 123 cães e 14 gatos. A taxa de prevalência na população foi entre 58,7% (GM) e 49,6% (LI). Foram detectados 76% de cães infectados, o que resultou significativamente superior aos 51% encontrados nos humanos. As porcentagens de cães (64,2%) e gatos (63,6%) com parasitemia foram significativamente superiores à correspondente aos humanos (12,5%). Se bem que 79% dos gatos estavam infectados, sua pequena quantidade e seus hábitos de perambulação determinam que sua participação na transmissão doméstica do T. cruzi seja restrita. Não obstante existir em média um maior número de humanos que de cães em cada lar, tanto de sujeitos sãos como infectados (6,5 vs. 3,3 e 3,4 vs. 2,4, respectivamente), foram detectados também na média mais cães que humanos com parasitemia em cada casa (2,1 vs. 1,0). As altas porcentagens de cães infectados e com parasitemia, além do hábito de repouso intra-domiciliário o qual ocasiona estreito contacto entre eles e os barbeiros determinam que os cães sejam os principais provedores de parásitos à disposição para a transmissão, e os hospedeiros mais importantes para /nanutenção da doença de Chagas na Região do Chaco Argentino.Trypanosoma cruzi prevalence rates of human, dog and cat populations from 47 households of 3 rural localities of the phytogeographical Chaqueña area of Argentina were determined both by serological and xenodiagnostic procedures. Human prevalence rates were uniform and ranged from 49.6 to 58.7%. Overall prevalence rate in dogs (75.0%) was significantly higher than in humans (51.0%). The overall proportion of parasitemic individuals assessed by xenodiagnosis was significantly higher in either dog (64.2%) or cat (63.6%) populations than among humans (12.5%). Although both the average number of resident as well as infected individuals per household was higher for people than for dogs (6.5 vs. 3.3, and 3.4 vs. 2.4, respectively), the reverse was recorded when parasitemic individuals were considered (1.0 vs. 2.1). Results are discussed in relation to dog between dogs and people, and dogs and bugs. In the light of present data, dogs must be considered as the major donors of parasites to vector bugs and thus, principal contributors to transmission in this region of Argentina