.
One may argue that the VIPERSZflag is related to the redshift value, not to the identification of the galaxy itself. However, it should be noted that a majority of sources with low VIPERSZflag are absorption line systems with noisy, low signal-to-noise spectra. Galaxies with such spectra can be particularly easily misclassified as stars during the spectroscopic measurement process, either automatic or human-supervised. For instance, typical features of an elliptical galaxy at z ~ 1, around the Balmer break, can be confused with characteristic features of an M-type star. To confirm the redshift measurements or flag validation, it is possible to use SED templates for the photometric redshift estimation, and to compare spectroscopic redshifts with photometric ones, but SED-fitting for sources with poor photometry can be degenerate possibly leading to biased results. In such cases, an independent confirmation that the position of an object in the five-dimensional colour space is actually typical of galaxy, and actually increases also probability that its redshift has been assigned correctly.
The number of stars and AGNs in the sample of objects with VIPERSZflag equal to one and two is very low (a few objects in the brightest apparent luminosity bin, and a few dozen for objects with i′ magnitude lower than 21 mag). This fact results from an initial star/galaxy separation performed by VIPERS. In the VIPERS database, the stars, which remained after the colour–colour preselection, are not typical, and they occupy a similar area to galaxies on the colour–colour plots. Then, that we can reconfirm the identity of a significant fraction of them can already be regarded as a success. The spectra of stars with VIPERSZflag = 1 or 2, which were classified as galaxies by our classifier, will have to be re-examined since some of them might also be genuine galaxies.
We should consider that the VIPERS galaxy sample is not pure and includes some AGN types such as those with narrow-line features, even for objects with VIPERSZflag > 3. During the standard redshift measurements process only the broad line AGNs are being recognised and flagged. This implies that that our galaxy training sample alco contains, in addition to a pure sample of normal galaxies, specific types of AGNs, otherwise difficult to recognise in VIPERS spectra during a standard redshift measurements process. The VIPERS hunt for the specific types of AGNs lurking within the heap of collected sources is still going on, so we are forced to work with the data composed of both galaxies and AGNs, at least for now. The contamination of galaxies and AGNs is most prominent for the faintest bin ( ), where more than 20% (30%) of objects classified as galaxies with VIPERSZflag equal to two (one) are identified as AGNs by an optical+NIR SVM classifier.
We look forward to using SVM methods to add more information on spectral lines and source morphologies as a very promising tool to improve classification for fainter sources and to refine further classes of objects that the software can discriminate.
As a test of efficiency of the SVM VIPERS classifier, we compared our algorithm with the star/galaxy separtion of the VVDS data performed by Coupon (Coupon et al. 2009; Guzzo et al. 2013). We computed the incompleteness of our galaxy selection as the ratio of true galaxies/AGNs lost after SVM classification, and we defined contamination as a number of stars mis-classified by SVM as galaxies/AGNs.
Coupon et al. (2009) base their star/galaxy classification on the most secure spectroscopic sample from the VVDS F02 and VVDS F22 fields. The method adopted for the star/galaxy separation was a combination of a geometric method for objects brighter than i′ = 21 mag (half-light radius parameter, rh, defined as the radius containing half of the object’s flux, which was provided by the CFHTLS database), and a combination of geometric and photometric methods for objects fainter than i′ = 21 mag, fitting u∗,g′,r′,i′, and z′ bands by a set of the SED templates with the Le Phare photometric redshift code (Arnouts et al. 1999; Ilbert et al. 2006). For a detailed description of this method we refer the reader to Guzzo et al. (2013).
We performed a star/non-star (where non-star for our classifier means galaxy+AGN) selection using the VVDS Deep F02 survey matched with the CFHTLS photometric catalogue (T0005 data release). We decided to perform star/galaxy classification in the VVDS Deep F02 only, because the stellar training sample used for our classifier was built from stars from the VVDS Wide F22 field. Only a part of AGNs from the VIPERS survey was used to train our algorithm. As a result, our stellar/galaxy separation in this field would be treated preferentially, which could bias the results.
and then into i′ apparent magnitude-binned samples. We stress that the ratio of AGNs is not known within the galaxy sample in this case.
NGSVMestimated is a total number of objects classified by SVM as a galaxies or AGNs.
(Galaxy+AGN)/star selection results: the incompleteness and contamination of the VIPERS galaxy sample (VVDS-Deep F02 field) expected from the star-galaxy separation process adopted in VIPERS, and from the SVM opt+NIR classifier.
Table 10 shows the results of incompleteness and contamination of two classifiers applied to the VVDS-Deep F02 sample: one based on the SEDs and geometrical properties of sources (Guzzo et al. 2013) and the other based on the SVM method. Presented values are the expected once for incompleteness and contamination of the star/galaxy separation in the W1 VIPERS field.
The incompleteness for both methods is similar (2.07% from Guzzo et al. 2013 vs. 2.13% and 2.05 for SVM). The stellar contamination in the SVM method is slightly higher (0.87% vs. 2.39% and 2.00% for SVM algorithm). Comparing these results with the results of the self-check of our classifier (see Table 4), we conclude that the high stellar contamination for these bins might be related to misclassifications between AGNs and stars9. Unfortunately, this conclusion cannot be compared directly with the Coupon et al. (2009) method because theirs does not classify AGNs.
We checked the real stellar contamination in the W1 field after VIPERS spectroscopic measurements. In total, 264 from the 23 360 objects in the apparent magnitude bin preclassified as galaxies using the SEDs+rh method from the PDR-1 catalogue were spectroscopically classified as a stars with. It means that the real contamination on W1 VIPERS filed for object fainter than mag is equal to (10)This value is between the contamination factor calculated from Guzzo et al. (2013) and the one given by our SVM opt+NIR classifier. Taking this difference into account, we conclude that the results obtained by both methods are similar and very close to the real values obtained from the spectroscopic observations.
We performed a classification of 264 objects preclassified as galaxies through the SEDs+rh method, and spectroscopicaly classified as a stars. In total 122 sources from this sample (46.6%) were correctly classified as stars by our algorithm. Taking only sources with very high confidence level of spectroscopic classification into account ( ; 123 sources), we found that our algorithm shows 74.8% of accuracy in correctly classifying 92 of those objects as stars.
It confirms that our SVM classification, based on spectroscopically measured objects from the VIPERS and VVDS surveys, can provide an efficient star/no-star classifier. This method is also very fast. The only time-consuming part of the SVM-based method is the tuning of the classifier, but once the classifier is trained, all the following classifications are very fast and can be done without any additional supervision.
Application of the SVM algorithm can deliver an excellent (with accuracy level for self-check test higher that 98% for galaxies, 94% for AGNs, and 93% for stars) classification for three classes of objects, after a careful selection of the training sample. For our analysis we constructed two classifiers, with and without near infrared data using a multidimensional colour hyperspace. A part of the AGN and star samples were extracted from the VVDS survey. We have found a significant improvement in the SVM classification (8% in the total accuracy of the classifier) adding an NIR colour parameters to our feature vectors.
For the optical+NIR classifier, we obtained very good agreement (93.60%, 81.80%, and 92.52% for galaxies, AGNs, and stars, respectively) with the VIPERS spectroscopic sample with flag confidence level of z measurements equal to 95%. What makes our approach to SVM classification more suitable is that the enormous amount of excellent quality data, means that we could create the classifier, which was trained on the part of the most secure sources, and then test it against the remaining secure objects to create the most efficient pattern recognition system. The VIPERS survey gathered a large number of sources (55 358) with very good spectroscopic measurements, which then were strictly analysed to obtain the most secure redshifts. This allowed for the choice of the best sample, which could be used as a basis for the new methods of automatic classification.
SVM classifiers are mostly used in the literature for separating two classes of sources (e.g. stars and galaxies). The only recent application of the SVM to the galaxy/AGNs/stars classification was performed by Saglia et al. (2012), who trained and used his classifier for the Pan-STARRS1 data. Comparing the accuracies