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J/A+A/611/A97   Photometric quasar candidates in Stripe 82 (Pasquet-Itam+, 2018)

Deep learning approach for classifying, detecting and predicting photometric redshifts of quasars in the Sloan Digital Sky Survey stripe 82. Pasquet-Itam J., Pasquet J. <Astron. Astrophys. 611, A97 (2018)> =2018A&A...611A..97P (SIMBAD/NED BibCode)
ADC_Keywords: QSOs ; Photometry, SDSS Keywords: methods: data analysis - techniques: photometric - techniques: image processing - quasars: general - surveys Abstract: We have applied a convolutional neural network (CNN) to classify and detect quasars in the Sloan Digital Sky Survey Stripe 82 and also to predict the photometric redshifts of quasars. The network takes the variability of objects into account by converting light curves into images. The width of the images, noted w, corresponds to the five magnitudes ugriz and the height of the images, noted h, represents the date of the observation. The CNN provides good results since its precision is 0.988 for a recall of 0.90, compared to a precision of 0.985 for the same recall with a random forest classifier. Moreover 175 new quasar candidates are found with the CNN considering a fixed recall of 0.97. The combination of probabilities given by the CNN and the random forest makes good performance even better with a precision of 0.99 for a recall of 0.90. For the redshift predictions, the CNN presents excellent results which are higher than those obtained with a feature extraction step and different classifiers (a K-nearest-neighbors, a support vector machine, a random forest and a Gaussian process classifier). Indeed, the accuracy of the CNN within |{DELTA}z|<0.1 can reach 78.09%, within |{DELTA}z|<0.2 reaches 86.15%, within |{DELTA}z|<0.3 reaches 91.2% and the value of root mean square (rms) is 0.359. The performance of the KNN decreases for the three |{DELTA}z| regions, since within the accuracy of |{DELTA}z|<0.1, |{DELTA}z|<0.2, and |{DELTA}z|<0.3 is 73.72%, 82.46%, and 90.09% respectively, and the value of rms amounts to 0.395. So the CNN successfully reduces the dispersion and the catastrophic redshifts of quasars. This new method is very promising for the future of big databases such as the Large Synoptic Survey Telescope. Description: We present a list of 175 new quasar candidates detected by a convolutional neural network (CNN) in the Sloan Digital Sky Survey Stripe 82 with a fixed recall of 0.97. The imaging data used in our work consists of objects solely from the publicly available variable source catalog (UWVSC; Ivezic et al. (2007, Cat. J/AJ/134/973), Sesar et al. (2007AJ....134.2236S)) constructed by researchers at the University of Washington. This catalog contains 67,507 unresolved, variable candidates with g≤20.5mag, at least 10 observations in both g and r bands, and a light curve with a root-mean-scatter (rms)>0.05mag and chi2 per degree of freedom >3 in both g and r bands. The CNN takes the variability of objects into account by converting light curves into images. The CNN provides good results since its precision is 0.988 for a recall of 0.90, compared to a precision of 0.985 for the same recall with a random forest classifier.The combination of probabilities given by the CNN and the random forest makes good performance even better with a precision of 0.99 for a recall of 0.90. For each a 7-character long integer ID, the candidate equatorial coordinates (decimal degrees, J2000), a flag which indicates the label given by the random forest method (0 = quasar, 1 = other),the r-band magnitude (corrected for ISM extinction) and the u-g, g-r, r-i, i-z SDSS colors (corrected for ISM extinction) are given. File Summary:
FileName Lrecl Records Explanations
ReadMe 80 . This file qso.dat 61 175 Quasar candidates
See also: J/AJ/134/973 : SDSS Stripe 82 star catalogs (Ivezic+, 2007) Byte-by-byte Description of file: qso.dat
Bytes Format Units Label Explanations
1- 7 I7 --- ID Stripe 82 identification number 9- 18 F10.6 deg RAdeg Right ascension (J2000.0) 20- 29 F10.6 deg DEdeg Declination (J2000.0) 31 A1 --- RF [0/1] label given by the Random Forest (0=quasar, 1=other) (1) 33- 37 F5.2 mag rmag SDSS r-median magnitude (2) 39- 43 F5.2 mag u-g SDSS u-g color index (2) 45- 49 F5.2 mag g-r SDSS g-r color index (2) 51- 55 F5.2 mag r-i SDSS r-i color index (2) 57- 61 F5.2 mag i-z SDSS i-z color index (2)
Note (1): label as follows: 0 = quasar 1 = other Note (2): corrected for ISM extinction.
Acknowledgements: Johanna Pasquet Itam, pasquet(at)cppm.in2p3.fr
(End) Patricia Vannier [CDS] 14-Nov-2017
The document above follows the rules of the Standard Description for Astronomical Catalogues.From this documentation it is possible to generate f77 program to load files into arrays or line by line

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