CHARACTERIZATION OF AGN VARIABILITY IN THE OPTICAL AND NIR REGIMES

Transcription

1 Paula Sánchez Paulina Lira - Regis Cartier - Nicolás Miranda CHARACTERIZATION OF AGN VARIABILITY IN THE OPTICAL AND NIR REGIMES

2 AGN VARIABILITY Which is the mechanism responsible for AGN variability? How is the observed variability related with physical quantities of AGNs? L BOL vs σ 2 rms from Simm et al MCG from Lira et al. 2015

3 AGN VARIABILITY It is very hard to detect NIR even in luminous AGNs! cc Centroid Distributions Figure from: Chris Harrison PhD Thesis (2014). Lag behind B (days) MCG cross correlations from Lira et al Double peaks in the Optical-NIR cros correlations, probably from the disk and the torus!

4 AGN CLASSIFICATION Optical BL NL XR I II N H <= cm 2 N H > cm 2

5 VARIABILITY ANALYSIS Variable Object? χ 2 Probability Excess Variance 2 = NX obs i=1 (m i m) 2 2 m i 2 rms = 2 LC m 2 2 m P ( 2 ) > rms err( 2 rms) > 0

6 VARIABILITY ANALYSIS Structure Function SF( t) = r 2 m i,j q 2 i + 2 j t SF( ) =A 1yr

7 OPTICAL ANALYSIS THE QUEST - LA SILLA AGN VARIABILITY SURVEY eso.org

8 ESO-SCHIMDT - QUEST DATA Images of Stripe82, ECDFS, XMM-LSS, ELAIS-S1, and COSMOS fields, taken between 2011 and QUEST camera: 112 CCDs (7.5 deg 2 ), Q band ~ (g + r) SDSS Details in Cartier et al ~ 20TB of raw data!! COSMOS Stripe82 eso.org XMM-LSS ELAIS-S1 ECDFS

9 OPTICAL VARIABILITY ANALYSIS Analysis for AGN with spectroscopic classification from optical counterpart catalog of the XMM-COSMOS field (Brusa et al. 2010) Regis Cartier PhD Thesis, details in Cartier et al % of variable AGN in the Q band SF parameters NL BL % variable A BL AGN NL AGN γ

10 CANDIDATES SELECTION

11 CANDIDATES SELECTION NON-QSO QSO A g - r γ u - g Observing run during December to confirm our candidates

12 NIR ANALYSIS ULTRAVISTA SURVEY eso.org

13 Paula Sánchez Hidden Monsters 2016 ULTRAVISTA DATA (McCracken et al. 2012) Time resolved images of the COSMOS field, taken between December 2009 and June Instrument: VIRCAM Y,J,H and Ks bands ultravista.org

14 NIR LIGHT CURVES Ks H J Y Ks H J Y BL AGN at z~0.35 JD BL AGN at z~ years of data!!! Ks H J Ks Y H J Y SED from Glass 1992 z~0.35 z~1 JD

15 NIR VARIABILITY ANALYSIS Analysis for AGN with spectroscopic classification from optical counterpart catalog of the Chandra-COSMOS field (Marchesi et al. 2015) Optical classification XR classification

16 NIR VARIABILITY ANALYSIS Light curves of variable NL AGNs NL and I AGN at z~0.34 NL and II AGN at z~0.44

17 NIR VARIABILITY ANALYSIS Optical Classification XR Classification SF( t) = r 2 m i,j q 2 i + 2 j t Y Y SF( ) =A 1yr A A γ γ Ks Ks A A γ γ

18 NIR VARIABILITY ANALYSIS A and γ parameters of the Structure function Y J Ks A A A γ γ γ

19 NIR VARIABILITY ANALYSIS J band A SF γ SF p=0.71 p=0.11

20 SUMMARY Variability-based AGN selection finds AGN populations missed by other optical selection techniques. From the variability analysis, there is evidence that the region emitting in the XRs is more compact than the region emitting in the optical. BL objects show stronger variability than NL sources. Most of the NL AGNs are located in a region of the A-γ space similar to the one occupied by galaxies, however some of them show a variability behavior closer to BL objects. This sources can be true-type NL AGNs, transitional AGNs, or exceptions to the unified model. The amplitude of the variability A determined by the Structure Function shows a positive correlation with redshift, which implies a positive correlation between frequency and amplitude of the variability. However, the structure of the variability, represented by γ, is independent of the frequency of the emission.