X-ray variability of 104 active galactic nuclei XMM-Newton

power-spectrum density profiles

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

2014.5.13

Outline

Introduction Sample and Data Data Analysis Results Discussion

INTRODUCTION

Introduction

1、 PSD: BH-XRB vs. AGN Similarities: power law, bend frequency BH-XRB: ‘state’– PSD shape QPOs problem

2、Main purpose: AGN PSD properties

SAMPLE AND DATA

Sample and Data

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.

DATA ANALYSIS

2 – 10 keV luminosity

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

PSD estimation

For a given PSD model P(ν;θ), likelihood function:

I: observed P: model Confidence intervals:

/2

1

2 logN

jj

j j

IS P

P

min

1.0 68.3%S S S

Two models

A. Simple power law:

B. Bending power law:

P N C

11

1 1b

P N C

Select model

LRT: Likelihood ratio test

Not well calibrated Accurate calibration: computation

expensive

2ln null

alternative

LD

L

QPOs check

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

RESULTS

Result 1 - variability

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

Result 2 -- Model selection

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

Result 3

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)

Result 4 – bend frequency

Mean value: log(v_b) = -3.47 ± 0.10

Result 5 – bend amplitude

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

Result 6 -- Leakage

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

DISCUSSION

Result summary

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)

Scaling relation

Equation 1: A = 1.09 ±0.21 C = -1.70 ±0.29 SSE :11.14 for 19 dof

Equation 2: A = 1.34 ±0.36 B = -0.24 ±0.28 C = -1.88 ±0.36 SSE: 10.69 for 18 dof

Scaling relation

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 = -0.70 ±0.30) Maybe due to uncertainties

BLR vs. variability

McHardy et al.(2006): correlation between T and optical line widths(V)

Lines: Hβ, Paβ Correlation coefficient: r = 0.692

D = 2.9 ±0.7 E = -10.2±2.3 SSE: 13.47 for 19 dof

PSD shapes

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)

Thanks!