Photometric redshifts for Quasars in multi band Surveys

MLPQNA stands for Multi Layer Perceptron with Quasi Newton Algorithm and it is a machine learning method which can be used to cope with regression and classification problems on complex and massive data sets. In this paper we give the formal description of the method and present the results of its application to the evaluation of photometric redshifts for quasars. The data set used for the experiment was obtained by merging four different surveys (SDSS, GALEX, UKIDSS and WISE), thus covering a wide range of wavelengths from the UV to the mid-infrared. The method is able i) to achieve a very high accuracy; ii) to drastically reduce the number of outliers and catastrophic objects; iii) to discriminate among parameters (or features) on the basis of their significance, so that the number of features used for training and analysis can be optimized in order to reduce both the computational demands and the effects of degeneracy. The best experiment, which makes use of a selected combination of parameters drawn from the four surveys, leads, in terms of DeltaZnorm (i.e. (zspec-zphot)/(1+zspec)), to an average of DeltaZnorm = 0.004, a standard deviation sigma = 0.069 and a Median Absolute Deviation MAD = 0.02 over the whole redshift range (i.e. zspec <= 3.6), defined by the 4-survey cross-matched spectroscopic sample. The fraction of catastrophic outliers, i.e. of objects with photo-z deviating more than 2sigma from the spectroscopic value is < 3%, leading to a sigma = 0.035 after their removal, over the same redshift range. The method is made available to the community through the DAMEWARE web application.Comment: 38 pages, Submitted to ApJ in February 2013; Accepted by ApJ in May 201

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

Who’s in Charge, Anyway – A Proposal for Community-Based Legal Services (with Raymond H. Brescia and Robin Golden)

For over one hundred years, some of our country\u27s most dedicatedlawyers have struggled to provide legal services to poor people.The road has not been an easy one. Richard Nixon vetoed alegal services bill over the issue of presidential appointments, thensigned the Legal Services Corporation Act just before resigning.Nixon\u27s Vice-President, Spiro Agnew, was a vocal opponent of federally-funded legal services. Ronald Reagan submitted eight consecutivebudgets seeking to eliminate all federal funding for theLegal Services Corporation ( LSC ). Simultaneously, he appointeda hostile LSC board of directors. Bill Clinton\u27s election,however, brought new hope to advocates. Hillary Clinton is a formerpresident of the LSC Board. The early Clinton budgets includedan increase in LSC funding, but they were countereddramatically by the severe cuts and restrictions imposed by the1994 Republican-controlled Congress

Shapley Supercluster Survey: Construction of the photometric catalogues and i-band data release

The Shapley Supercluster Survey is a multi-wavelength survey covering an area of ∼23 deg² (∼260 Mpc² at z = 0.048) around the supercluster core, including nine Abell and two poor clusters, having redshifts in the range 0.045–0.050. The survey aims to investigate the role of the cluster-scale mass assembly on the evolution of galaxies, mapping the effects of the environment from the cores of the clusters to their outskirts and along the filaments. The optical (ugri) imaging acquired with OmegaCAM on the VLT Survey Telescope is essential to achieve the project goals providing accurate multi-band photometry for the galaxy population down to m∗ + 6. We describe the methodology adopted to construct the optical catalogues and to separate extended and point-like sources. The catalogues reach average 5σ limiting magnitudes within a 3 arcsec diameter aperture of ugri = [24.4,24.6,24.1,23.3] and are 93 per cent complete down to ugri = [23.8,23.8,23.5,22.0] mag, corresponding to ∼m∗ r + 8.5. The data are highly uniform in terms of observing conditions and all acquired with seeing less than 1.1 arcsec full width at half-maximum. The median seeing in r band is 0.6 arcsec, corresponding to 0.56 kpc h⁻¹ 70 at z = 0.048. While the observations in the u, g and r bands are still ongoing, the i-band observations have been completed, and we present the i-band catalogue over the whole survey area. The latter is released and it will be regularly updated, through the use of the Virtual Observatory tools. This includes 734 319 sources down to i = 22.0 mag and it is the first optical homogeneous catalogue at such a depth, covering the central region of the Shapley supercluster

Steps towards a map of the nearby universe

We present a new analysis of the Sloan Digital Sky Survey data aimed at producing a detailed map of the nearby (z < 0.5) universe. Using neural networks trained on the available spectroscopic base of knowledge we derived distance estimates for about 30 million galaxies distributed over ca. 8,000 sq. deg. We also used unsupervised clustering tools developed in the framework of the VO-Tech project, to investigate the possibility to understand the nature of each object present in the field and, in particular, to produce a list of candidate AGNs and QSOs.Comment: 3 pages, 1 figure. To appear in Nucl Phys. B, in the proceedings of the NOW-2006 (Neutrino Oscillation Workshop - 2006), R. Fogli et al. ed

Data Deluge in Astrophysics: Photometric Redshifts as a Template Use Case

Astronomy has entered the big data era and Machine Learning based methods have found widespread use in a large variety of astronomical applications. This is demonstrated by the recent huge increase in the number of publications making use of this new approach. The usage of machine learning methods, however is still far from trivial and many problems still need to be solved. Using the evaluation of photometric redshifts as a case study, we outline the main problems and some ongoing efforts to solve them.Comment: 13 pages, 3 figures, Springer's Communications in Computer and Information Science (CCIS), Vol. 82

Inside Catalogs: A Comparison of Source Extraction Software

The scope of this article is to compare the catalog extraction performances obtained using the new combination of SExtractor with PSFEx against the more traditional and diffuse application of DAOPHOT with ALLSTAR; therefore, the paper may provide a guide for the selection of the most suitable catalog extraction software. Both software packages were tested on two kinds of simulated images, having a uniform spatial distribution of sources and an overdensity in the center, respectively. In both cases, SExtractor is able to generate a deeper catalog than DAOPHOT. Moreover, the use of neural networks for object classification plus the novel SPREAD_MODEL parameter push down to the limiting magnitude the possibility of star/galaxy separation. DAOPHOT and ALLSTAR provide an optimal solution for point-source photometry in stellar fields and very accurate and reliable PSF photometry, with robust star/galaxy separation. However, they are not useful for galaxy characterization and do not generate catalogs that are very complete for faint sources. On the other hand, SExtractor, along with the new capability to derive PSF photometry, turns out to be competitive and returns accurate photometry for galaxies also. We can report that the new version of SExtractor, used in conjunction with PSFEx, represents a very powerful software package for source extraction with performances comparable to those of DAOPHOT. Finally, by comparing the results obtained in the cases of a uniform and of an overdense spatial distribution of stars, we notice for both software packages a decline for the latter case in the quality of the results produced in terms of magnitudes and centroids