Data Mining & Exploration
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Classification of photometric Quasars
In this page, you will find a description of the methods for the production of photometric candidate QSO catalogues from the SDSS DR10 (Brescia et al. 2015) and an old catalogue extracted from the SDSS DR7 (see D'Abrusco et al. 2009.)
DR10 CANDIDATE QUASAR CATALOGUE (2015)
We applied the MLPQNA (Multi Layer Perceptron with Quasi Newton Algorithm) method to the optical data of the Sloan Digital Sky Survey - Data Release 10, investigating whether photometric data alone suffice to disentangle different classes of objects as they are defined in the SDSS spectroscopic classification.
MLPQNA proved to be quite effective in the three-class (QSO/AGN, GALAXY, STAR) separation. In disentangling quasars from stars and galaxies, our method achieved an overall efficiency of 91.31%, a QSO completeness of 90.49% and a purity of 86.90%. Afterwards, since our goal was to reach the highest level of purity in the produced catalogue, we performed a further statistical analysis of the test set, by assessing the variation of purity vs completeness as a function of the increasing confidence threshold used to evaluate the QSO candidates from the trained MLPQNA model output. At the end we reached a purity of about 95% in the blind test set.
The resulting 95% pure catalogue of candidate quasars consists of about 3.6 million objects.
All details of the extraction method are available in Brescia et al. 2015 (published by MNRAS). PLEASE CITE THIS ARTICLE EVERY TIME YOU USE THE QSO CATALOGUE
The content of the catalogue files (FITS format) is described in the table below.
| Column | Name | Description |
| 1 | objID | DR10 object ID |
| 2 | ra | Right Ascension (J2000) |
| 3 | dec | Declination (J2000) |
| 4 | psfMag_u | PSF magnitude in u band |
| 5 | psfMag_g | PSF magnitude in g band |
| 6 | psfMag_r | PSF magnitude in r band |
| 7 | psfMag_i | PSF magnitude in i band |
| 8 | psfMag_z | PSF magnitude in z band |
| 9 | modelMag_u | model magnitude in u band |
| 10 | modelMag_g | model magnitude in g band |
| 11 | modelMag_r | model magnitude in r band |
| 12 | modelMag_i | model magnitude in i band |
| 13 | modelMag_z | model magnitude in z band |
| 14 | specObjID | DR10 spectral object ID |
| 15 | subclass | DR10 spectral object subclass type |
| 16 | z | DR10 spectroscopic redshift |
| 17 | qualityFlag | Croom et al. 2009 quality flag (1 high, 0 normal) |
DR7 CANDIDATE QUASAR CATALOGUE (2008)
The inspiring principle of this work is the application of statistical and data-mining techniques to obtain a clustering of astronomical sources inside a photometric parameter space and fully characterize the distribution of different types of sources inside this parameter space. This concept has been applied to the problem of the selection of QSOs candidates from broadband photometric data by exploiting the availability of large spectroscopic bases of knowledge (BoK: i.e., samples of sources with a reliable classification).
The procedure for the extraction of candidates can be summarized as follows:
- A BoK consisting of a sample of stellar sources with spectroscopic classification is clustered inside the colour parameter space. This BoK is drawn from the catalogue of photometric sources from where, at the end of the process, the new QSOs candidates will be extracted.
- Several possible partitions of the distribution of sources of the BoK inside the colour space are produced by a combination of two clustering algorithm: PPS and NEC.
- The members of each cluster of each different partition are labelled using the BoK classification.
- Amongst all the possible partitions in the colour space, the one allowing the best separation between clusters populated mainly by confirmed QSOs ("successful" clusters) and clusters populated mainly by contaminants is considered.
- The new candidates QSOs are selected as the photometric sources which are associated, in the colour space, to the "successful" clusters by a suitable distance definition.
The details of the method and algorithms can be found in the paper D'Abrusco et al. 2009.
In order to ease the download of the massive dataset, the catalogue is split in distinct files each corresponding to a different "stripe", i.e. a patch of the sky defined (in the SDSS jargon) as "a line of constant eta, bounded on the north and south by the edges of the two strips that make up the stripe, and bounded on the east and west by lines of constant lambda. Because both strips and stripes are defined in "observed" space, they are rectangular areas which overlap as one approaches the poles."
The content of the catalogue files (FITS format) is described in the table below.
| Column | Name | Description |
| 1 | catalogueID | Candidate ID |
| 2 | objID | SDSS ID of the source |
| 3 | ra | Right Ascension (J2000) |
| 4 | dec | Declination (J2000) |
| 5 | psfMag_u | PSF magnitude in u band |
| 6 | psfMag_g | PSF magnitude in g band |
| 7 | psfMag_r | PSF magnitude in r band |
| 8 | psfMag_i | PSF magnitude in i band |
| 9 | psfMag_z | PSF magnitude in z band |
| 10 | clusterID | Cluster ID |
Click on the name of the file and save it to your hard-drive. Enjoy!
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