1. Use of neural networks for the identification of new z ≥ 3.6 radio QSOs from FIRST-SDSS DR5 R. Carballo Dpto. Matemática Aplicada y Ciencias de la Computación, UC J.I. González-Serrano Instituto de Física de Cantabria (CSIC-UC) C.R. Benn Isaac Newton Group, La Palma F. Jiménez-Luján Dpto. Física Moderna, UC ; IFCA MNRAS submitted

2. Scientific aims for the selection of a sample of radio-loud QSOs at z ≥ 3.6 –Radio QSOs represent only 10% of whole population. Compared to RQ, the initial photometric samples of QSO candidates are less contaminated by stars. –RL QSOs have its own interest. What is their situation with respect to the whole QSO population ? Is the radio-loudness distribution (radio to optical flux ratio) bimodal? Which processes mark the radio-loud character? Black-hole mass, mass of the host galaxy RL QSOs and BALs (radio emission as a test for orientation scenarios) How does the fraction of RL QSOs relates to redshift, optical luminosity?. –Optical luminosity function of RL QSOs at z ≥ 3.6 (bright end)

3. Initial sample: FIRST - SDSS Phot DR5 pairs (DR5 Julio 2006) S(1.4 GHz) ≥ 1 mJy SDSS r ≤ 20.2 photometric quality SDSS r unresolved radio-optical separation ≤ 1.5” Overlapping area FIRST-SDSS 7391 deg 2 Number of FIRST-SDSS matches 8663 Data 8 variables r, u-g,g-r,r-i,i-z, sep, S tot, S peak / S tot Spectroscopic classification from SDSS Spec DR5 with SDSS spectra L 4248 52 QSOs z ≥ 3.6 3754 QSOs z < 3.6 230 stars 59 galaxies 153 unknown without SDSS spectra U 4415

4. Redshift distribution for the 3806 FIRST-SDSS QSOs

5. ug r i z redshift-colour relation for QSOs

6. FIRST-SDSS pairs with SDSS-DR5 spectra L 4248 52 QSOs z ≥ 3.6 3754 QSOs z < 3.6 230 stars 59 galaxies 153 unknown FIRST-SDSS pairs without SDSS-DR5 spectra U 4415 What QSO types?: BALs and absorbed at Lyα included “Narrow-line” (Type II) QSOs excluded Lobe-dominated QSOs excluded (4% of all radio morphologies) Supervised learning: classifier Application of the classifier Classifier: separation in two classes

7. Search for the best hyperplane in the d-dimensional space of input variables separating the sample in two classes: C 1 high-z QSO and C 2 remaining sources Input vector x Output y(x) Є [0,1] Target values if x Є C 1 then y(x)=1 else y(x)=0 w are the parameters of the model Logistic Discriminant

8. The error function is the mean square error Software: Neural Network Matlab Toolbox

9. Train-Test approach: leave-one-out Training set 4247 Test 1 The 4248 sources are used as test → 4248 trained NNs Class assignment: x belongs to class C1 if y(x) ≥ y c

10. Efficiency versus completeness for labelled sample y c : 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7,0.8, 0.9 from right to left r, u-g, g-r, r-i, i-z, sep Results Efficiency 50 / 81 62 ± 9 % contaminants: 1 star + 2 galaxies + 28 QSOs ( 19 with 3.2 ≤ z ≤ 3.6) Completeness 50 / 52 96 ± 4 %

11. The sample of 58 high-z QSO candidates 31 Outside DR5 spectr. area27 Inside DR5 spectr. area Application of the classifier to the 4415 unlabelled sources

12. Spectroscopic check: candidates Efficiency 24/40 = 60 ± 12 % (sample with fainter mags) a mags Previous surveys of RL QSOs at these redshifts (literature) eff < 20 % –NED NASA Extragalactic Database # 4: 3 High-z QSOs + QSO z = 3.3 –Follow-up spectroscopy at WHT ISIS, La Palma. 2007 April and July # 27: 17 High-z QSOs + 5 QSO 3.2 < z < 3.6 + 5 other –SDSS DR6 spectroscopy. 2007 July # 9: 4 High-z QSOs + QSO z = 3.4 + 4 other (+5 ISIS+1NED) (+1 ISIS+1NED)

13. Spectroscopic check: non candidates 4415 U – 58 candidates = 4357 non candidates NED: 382 QSOs but none with z ≥ 3.6 DR6: 898 with spectrum (some already included NED) most of them QSOs (765 / 898 = 85% ) and none with z ≥ 3.6 No evidence of having missed high-z QSOs → high completeness

14. The high-z QSO sample. How did the NNs helped us? 8663 FIRST-SDSS DR5 phot 4248 yes SDSS DR5 spec 52 Hz QSO 4415 no SDSS DR5 spec 58 Hz QSO candidates 24 confirmed (out of 40) 7 expected (out of 18 unobserved) From 52 to 76 (52+24) → Sample increased by a factor 1.46

15. SDSS spectroscopic completeness for the selection of high-z QSOs SDSS DR5 photometric area covered by DR5 and DR6 spec 52 high-z QSO s DR5 spec + 10 DR6 spec 7 high-z QSOs (NED, this work) 15 without spectrum → 4 expected high-z QSOs (62) / (62+11) = 62 / 73 = 85% for r ≤ 20.2 and 3.6 ≤ z ≤ 4.6

16. Conclusions and future work 1.A logistic discriminant trained on FIRST-SDSS DR5 sources with available DR5 spectra allowed an efficient and highly complete selection of 3.6 ≤ z ≤ 4.6 QSOs. Within this survey, efficiency and completeness were 62% and 96% respectively. 2.The application of the classifier to 4415 DR5 photometric sources without DR5 spectrum yielded 58 candidates. Spectroscopic identifications of 40 of them (NED, observations at the WHT, DR6) yielded efficiency 24/40 = 60%. More than 1000 sources classed as non high-z QSOs have spectrum from NED or DR6 and none of them is a high-z QSO, implying a high completeness. 3.We increased the high-z QSO sample from 52 to 76, i.e. a factor 1.46. 17 spectra from this work, 11 not included at DR6. The optical luminosity function of RL QSOs (at the bright end) will be computed from this sample. 4.From the new identifications we estimate the incompleteness of SDSS for the spectroscopic selection of 3.6 ≤ z ≤ 4.6 FIRST-QSOs to be around 15%, for r ≤ 20.2.

18. HzQ Other U

19. x 1 x 2 x 3 x 4 x 5 x 6 x d y d 1 Neural Networks: Schematics of a Multilayer Perceptron with a hidden layer and a single output d:p:1

20. SDSS DR5 Area deg 2 ## with DR5 spectrum (Spectroscopic targets. Bias) # without DR5 spectrum DR5 Phot Within DR5 spectroscopic survey 5553 6307 4248 (67%) 2059 Outside DR5 specroscopic survey 1883 2356 Total7391 86634248 L4415 U Application of the classifier to the unlabelled sources

21. Results () WHT and DR6 apart NED WHTespect. DR6Total QSOs z>=3.6 3 17 (12) 4 (plus 1 NED and 5 ISIS) (9) 24 QSOs at other z 1 z = 3.3 3 z = 3.4 - 3.6 2 z = 3.15 - 3.4 4 z = 1.05 -1.35 1 z = 3.4 (plus 1 NED and 1 ISIS z=3.4) 11 Galaxies 2 z = 0.45-0.60 2 Unknown 1 2 3 Total 4 27 9 (plus 2 from NED and 6 ISIS) 40

22. Efficiency versus completeness for labelled sample and 8 sets of variables y c : 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7,0.8, 0.9 from right to left Input variables: r, u-g, g-r, r-i, i-z sep radio flux peak-to-total ratio comp 96% eff 62% r,u-g,g-r,r-i,i-z, separation Results