arXiv subjects

A. Solarz

Publications and source records attributed to A. Solarz.

14 recordsLinked to original sources

arXiv2020

Spectroscopic observations of the machine-learning selected anomaly catalogue from the AllWISE Sky Survey

We present the results of a programme to search and identify the nature of unusual sources within the All-sky Wide-field Infrared Survey Explorer (WISE) that is based on a machine-learning algorithm for anomaly detection, namely one-class support vector machines (OCSVM). Designed to detect sources deviating from a training set composed of known classes, this algorithm was used to create a model for the expected data based on WISE objects with spectroscopic identifications in the Sloan Digital Sky Survey (SDSS). Subsequently, it marked as anomalous those sources whose WISE photometry was shown to be inconsistent with this model. We report the results from optical and near-infrared spectroscopy follow-up observations of a subset of 36 bright ($g_{AB}$<19.5) objects marked as 'anomalous' by the OCSVM code to verify its performance. Among the observed objects, we identified three main types of sources: i) low redshift (z~0.03-0.15) galaxies containing large amounts of hot dust (53%), including three Wolf-Rayet galaxies; ii) broad-line quasi-stellar objects (QSOs) (33%) including low-ionisation broad absorption line (LoBAL) quasars and a rare QSO with strong and narrow ultraviolet iron emission; iii) Galactic objects in dusty phases of their evolution (3%). The nature of four of these objects (11%) remains undetermined due to low signal-to-noise or featureless spectra. The current data show that the algorithm works well at detecting rare but not necessarily unknown objects among the brightest candidates. They mostly represent peculiar sub-types of otherwise well-known sources.

astro-ph.GA↗

arXiv2019

Nowcasting the turbulence at the Paranal Observatory

At Paranal Observatory, the least predictable parameter affecting the short-term scheduling of astronomical observations is the optical turbulence, especially the seeing, coherence time and ground layer fraction. These are critical variables driving the performance of the instruments of the Very Large Telescope (VLT), especially those fed with adaptive optics systems. Currently, the night astronomer does not have a predictive tool to support him/her in decision-making at night. As most service-mode observations at the VLT last less than two hours, it is critical to be able to predict what will happen in this time frame, to avoid time losses due to sudden changes in the turbulence conditions, and also to enable more aggressive scheduling. We therefore investigate here the possibility to forecast the turbulence conditions over the next two hours. We call this "turbulence nowcasting", analogously with weather nowcasting, a term already used in meteorology coming from the contraction of "now" and "forecasting". We present here the results of a study based on historical data of the Paranal Astronomical Site Monitoring combined with ancillary data, in a machine learning framework. We show the strengths and shortcomings of such an approach, and present some perspectives in the context of the Extremely Large Telescope.

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arXiv2019

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.

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arXiv2018

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.

astro-ph.IM↗

arXiv2018

AKARI NEP-Deep: galaxy clustering through the AKARI IRC filters

We present a preliminary analysis of clustering of galaxies luminous in the near- and mid-infrared as seen by seven various ilters of the AKARI IRC instrument from 2 $\mu$m to 24 $\mu$m in the the AKARI NEP-Deep field. We compare populations of galaxies detected in different filters and their clustering properties. We conclude that different AKARI filters allow to trace different populations composed mainly of star-forming galaxies located in different environments. In particular, the mid-infrared filters at redshift z $\sim$ 0.8 and higher trace a population of strongly evolving galaxies located in massive haloes which might have ended as elliptical galaxies today.

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arXiv2017

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.

astro-ph.GA↗

arXiv2017

Overview of North Ecliptic Pole Deep multi-wavelength Survey (NEP-Deep)

The recent updates of the North Ecliptic Pole deep (0.5~deg$^2$, NEP-Deep) multi-wavelength survey covering from X-ray to radio-wave is presented. The NEP-Deep provides us with several thousands of 15~$\mu$m or 18~$\mu$m selected sample of galaxies, which is the largest sample ever made at this wavelengths. A continuous filter coverage in the mid-infrared wavelength (7, 9, 11, 15, 18, and 24~$\mu$m) is unique and vital to diagnose the contributions from starbursts and AGNs in the galaxies out to $z$=2.The new goal of the project is to resolve the nature of the cosmic star formation history at the violent epoch (e.g. $z$=1--2), and to find a clue to understand its decline from $z$=1 to present universe by utilizing the unique power of the multi-wavelength survey. The progress in this context is briefly mentioned.

astro-ph.GA↗

arXiv2017

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.

astro-ph.IM↗

arXiv2016

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.

astro-ph.GA↗

arXiv2016

WISE x SuperCOSMOS photometric redshift catalog: 20 million galaxies over 3pi steradians

We cross-match the two currently largest all-sky photometric catalogs, mid-infrared WISE and SuperCOSMOS scans of UKST/POSS-II photographic plates, to obtain a new galaxy sample that covers 3pi steradians. In order to characterize and purify the extragalactic dataset, we use external GAMA and SDSS spectroscopic information to define quasar and star loci in multicolor space, aiding the removal of contamination from our extended-source catalog. After appropriate data cleaning we obtain a deep wide-angle galaxy sample that is approximately 95% pure and 90% complete at high Galactic latitudes. The catalog contains close to 20 million galaxies over almost 70% of the sky, outside the Zone of Avoidance and other confused regions, with a mean surface density of over 650 sources per square degree. Using multiwavelength information from two optical and two mid-IR photometric bands, we derive photometric redshifts for all the galaxies in the catalog, using the ANNz framework trained on the final GAMA-II spectroscopic data. Our sample has a median redshift of z_{med} = 0.2 but with a broad dN/dz reaching up to z>0.4. The photometric redshifts have a mean bias of |delta_z|~10^{-3}, normalized scatter of sigma_z = 0.033 and less than 3% outliers beyond 3sigma_z. Comparison with external datasets shows no significant variation of photo-z quality with sky position. Together with the overall statistics, we also provide a more detailed analysis of photometric redshift accuracy as a function of magnitudes and colors. The final catalog is appropriate for `all-sky' 3D cosmology to unprecedented depths, in particular through cross-correlations with other large-area surveys. It should also be useful for source pre-selection and identification in forthcoming surveys such as TAIPAN or WALLABY.

astro-ph.CO↗

arXiv2015

Clustering of the AKARI NEP Deep Field 24 $\mu$m selected galaxies

We present a method of selection of 24~$\mu$m galaxies from the AKARI North Ecliptic Pole (NEP) Deep Field down to $150 \mbox{ }\mu$Jy and measurements of their two-point correlation function. We aim to associate various 24 $\mu$m selected galaxy populations with present day galaxies and to investigate the impact of their environment on the direction of their subsequent evolution. We discuss using of Support Vector Machines (SVM) algorithm applied to infrared photometric data to perform star-galaxy separation, in which we achieve an accuracy higher than 80\%. The photometric redshift information, obtained through the CIGALE code, is used to explore the redshift dependence of the correlation function parameter ($r_{0}$) as well as the linear bias evolution. This parameter relates galaxy distribution to the one of the underlying dark matter. We connect the investigated sources to their potential local descendants through a simplified model of the clustering evolution without interactions. We observe two different populations of star-forming galaxies, at $z_{med}\sim 0.25$, $z_{med}\sim 0.9$. Measurements of total infrared luminosities ($L_{TIR}$) show that the sample at $z_{med}\sim 0.25$ is composed mostly of local star-forming galaxies, while the sample at $z_{med}\sim0.9$ is composed of luminous infrared galaxies (LIRGs) with $L_{TIR}\sim 10^{11.62}L_{\odot}$. We find that dark halo mass is not necessarily correlated with the $L_{TIR}$: for subsamples with $L_{TIR}= 10^{11.15} L_{\odot}$ at $z_{med}\sim 0.7$ we observe a higher clustering length ($r_{0}=6.21\pm0.78$ $[h^{-1} \mbox{Mpc}]$) than for a subsample with mean $L_{TIR}=10^{11.84} L_{\odot}$ at $z_{med}\sim1.1$ ($r_{0}=5.86\pm0.69$ $h^{-1} \mbox{Mpc}$). We find that galaxies at $z_{med}\sim 0.9$ can be ancestors of present day $L_{*}$ early type galaxies, which exhibit a very high $r_{0}\sim 8$~$h^{-1} \mbox{Mpc}$.

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arXiv2013

The VIMOS Public Extragalactic Redshift Survey (VIPERS). A Support Vector Machine classification of galaxies, stars and AGNs

The aim of this work is to develop a comprehensive method for classifying sources in large sky surveys and we apply the techniques to the VIMOS Public Extragalactic Redshift Survey (VIPERS). Using the optical (u*, g', r', i') and NIR data (z', Ks), we develop a classifier, based on broad-band photometry, for identifying stars, AGNs and galaxies improving the purity of the VIPERS sample. Support Vector Machine (SVM) supervised learning algorithms allow the automatic classification of objects into two or more classes based on a multidimensional parameter space. In this work, we tailored the SVM for classifying stars, AGNs and galaxies, and applied this classification to the VIPERS data. We train the SVM using spectroscopically confirmed sources from the VIPERS and VVDS surveys. We tested two SVM classifiers and concluded that including NIR data can significantly improve the efficiency of the classifier. The self-check of the best optical + NIR classifier has shown a 97% accuracy in the classification of galaxies, 97 for stars, and 95 for AGNs in the 5-dimensional colour space. In the test on VIPERS sources with 99% redshift confidence, the classifier gives an accuracy equal to 94% for galaxies, 93% for stars, and 82% for AGNs. The method was applied to sources with low quality spectra to verify their classification, and thus increasing the security of measurements for almost 4 900 objects. We conclude that the SVM algorithm trained on a carefully selected sample of galaxies, AGNs, and stars outperforms simple colour-colour selection methods, and can be regarded as a very efficient classification method particularly suitable for modern large surveys.

astro-ph.CO↗

arXiv2013

VIPERS: An Unprecedented View of Galaxies and Large-Scale Structure Halfway Back in the Life of the Universe

The VIMOS Public Extragalactic Redshift Survey (VIPERS) is an ongoing ESO Large Programme to map in detail the large-scale distribution of galaxies at 0.5 < z <1.2. With a combination of volume and sampling density that is unique for these redshifts, it focuses on measuring galaxy clustering and related cosmological quantities as part of the grand challenge of understanding the origin of cosmic acceleration. VIPERS has also been designed to guarantee a broader legacy, allowing detailed investigations of the properties and evolutionary trends of z~1 galaxies. The survey strategy exploits the specific advantages of the VIMOS spectrograph at the VLT, aiming at a final sample of nearly 100,000 galaxy redshifts to iAB = 22.5 mag, which represents the largest redshift survey ever performed with ESO telescopes. In this introductory article we describe the survey construction, together with early results based on a first sample of ~55,000 galaxies.

astro-ph.CO↗

arXiv2012

Star-galaxy separation in the AKARI NEP Deep Field

Context: It is crucial to develop a method for classifying objects detected in deep surveys at infrared wavelengths. We specifically need a method to separate galaxies from stars using only the infrared information to study the properties of galaxies, e.g., to estimate the angular correlation function, without introducing any additional bias. Aims. We aim to separate stars and galaxies in the data from the AKARI North Ecliptic Pole (NEP) Deep survey collected in nine AKARI / IRC bands from 2 to 24 {\mu}m that cover the near- and mid-infrared wavelengths (hereafter NIR and MIR). We plan to estimate the correlation function for NIR and MIR galaxies from a sample selected according to our criteria in future research. Methods: We used support vector machines (SVM) to study the distribution of stars and galaxies in the AKARIs multicolor space. We defined the training samples of these objects by calculating their infrared stellarity parameter (sgc). We created the most efficient classifier and then tested it on the whole sample. We confirmed the developed separation with auxiliary optical data obtained by the Subaru telescope and by creating Euclidean normalized number count plots. Results: We obtain a 90% accuracy in pinpointing galaxies and 98% accuracy for stars in infrared multicolor space with the infrared SVM classifier. The source counts and comparison with the optical data (with a consistency of 65% for selecting stars and 96% for galaxies) confirm that our star/galaxy separation methods are reliable. Conclusions: The infrared classifier derived with the SVM method based on infrared sgc- selected training samples proves to be very efficient and accurate in selecting stars and galaxies in deep surveys at infrared wavelengths carried out without any previous target object selection.

astro-ph.IM↗