Authors: O.Gonzalez-Martin, S. Vaughan Speaker: Xuechen Zheng

 Introduction  Sample and Data  Data Analysis  Results  Discussion

 1 、 PSD: BH-XRB vs. AGN  Similarities: power law, bend frequency  BH-XRB: ‘state’– PSD shape  QPOs problem  2 、 Main purpose:  AGN PSD properties

 From XMM-Newton public archives until Feb. 2009:  Z <0.4  Observation duration T >40 ksec  Classification, redshift, mass, bolometric luminosity: literature  Sample: 209 observations and 104 distinct AGN(61 Type-1, 21 Type-2, 15 NLSy1, 7 BLLACs)

Example.

 2-10 kev luminosity  fitting using absorbed power-law model  Required only reasonable estimates  LLAGN luminosity agree with other literature  Type- 1 Seyferts, QSOs, NLSy1: high discrepancies  soft-excess long-term variability

 For a given PSD model P( ν ; θ ), likelihood function:  I: observed P: model  Confidence intervals:

 A. Simple power law:  B. Bending power law:

 LRT: Likelihood ratio test  Not well calibrated  Accurate calibration: computation expensive

 1 、 Largest outlier vs. Chi-squared distribution for periodogram  Candidate: p<0.01  2 、 Similar test to smoothing periodogram (top-hat filter)   QPOs broader than frequency resolution  p-value not correctly calibrated, crude but efficient

 75 out of 104 AGN show variability  No variability: 12 of 14 LINERs, 2 of 11 Type-2 Seyferts, 12 of 54 Type-1 Seyferts, 2 of 3 QSOs, 1 of 7 BLLACs

 Low number of bins in the PSD above Poisson noise  some sources unable to constrained parameters  Model B: 17  vs. Papadakis et al.(2010): bump or QPOs?  16 Type-1, 1 S2

 QPOs: only one candidate  Slope:  Model A ---- α =2.01±0.01(T) 2.06±0.01(S) 1.77±0.01(H)  K-S test  distributions statistically indistinguishable  Model B ---- α =3.08±0.04(T) 3.03±0.01(S) 3.15±0.08(H)

 Mean value: log(v_b) = ± 0.10

 Papadakis(2004): A= ν ×F( ν ) roughly constant at bend frequency

 Leakage bias: reduce sensitivity to bends and QPOs   model A α ≈ 2: possibly be affected  ‘End matching’(Fougere 1985) reduce leakage bias   remove linear trend: first and last point equal   model A indices higher than before but lower than high frequency index in bend PSDs

 1 、 72% of the sample show variability, most LINERs do not vary  2 、 17 sources (16 Type-1 Seyferts)  model B; others  model A  3 、 slope discrepancy between model A and B  4 、 only one QPO (hard to detect)

 Equation 1:  A = 1.09 ±0.21 C = ±0.29  SSE :11.14 for 19 dof  Equation 2:  A = 1.34 ±0.36 B = ±0.28  C = ±0.36  SSE: for 18 dof

 Cygnus X-1: test relation on BH-XRB  vs. McHardy et al.(2006):  Weak dependence of T_b on L  Use smaller mass  dependence recover( B = ±0.30)  Maybe due to uncertainties

 McHardy et al.(2006): correlation between T and optical line widths(V)  Lines: H β, Pa β  Correlation coefficient: r =  D = 2.9 ±0.7 E = -10.2±2.3  SSE: for 19 dof

 Model B high frequency slope steep:  May be similar to BH-XRB‘soft’states  XMM-Newton and RXTE  Selection effect  Majority of sample show no bend:  Massive object have lower v_b  Leakage bias  selection effect  Bends:  M_bh, L  expected T_b  17 source bends within frequency range(13 detected)