X ray variability of 104 active galactic nuclei xmm newton power spectrum density profiles
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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.

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X ray variability of 104 active galactic nuclei xmm newton power spectrum density profiles

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
Outline power-spectrum density profiles

  • Introduction

  • Sample and Data

  • Data Analysis

  • Results

  • Discussion


Introduction
Introduction power-spectrum density profiles


Introduction1
Introduction power-spectrum density profiles

  • 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 power-spectrum density profiles


Sample and data1
Sample and Data power-spectrum density profiles

  • 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)


E power-spectrum density profilesxample.


Data analysis
Data analysis power-spectrum density profiles


2 10 kev luminosity
2 – 10 keV luminosity power-spectrum density profiles

  • 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
PSD estimation power-spectrum density profiles

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

  • I: observed P: model

  • Confidence intervals:


Two models
Two models power-spectrum density profiles

  • A. Simple power law:

  • B. Bending power law:


Select model
Select model power-spectrum density profiles

  • LRT: Likelihood ratio test

  • Not well calibrated

  • Accurate calibration: computation expensive


Qpos check
QPOs check power-spectrum density profiles

  • 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
Results power-spectrum density profiles


Result 1 variability
Result 1 - variability power-spectrum density profiles

  • 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
Result 2 -- Model selection power-spectrum density profiles

  • 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
Result 3 power-spectrum density profiles

  • 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
Result 4 – bend frequency power-spectrum density profiles

  • Mean value: log(v_b) = -3.47 ± 0.10


Result 5 bend amplitude
Result 5 – bend amplitude power-spectrum density profiles

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


Result 6 leakage
Result 6 -- Leakage power-spectrum density profiles

  • 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
Discussion power-spectrum density profiles


Result summary
Result summary power-spectrum density profiles

  • 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
Scaling relation power-spectrum density profiles

  • 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 relation1
Scaling relation power-spectrum density profiles

  • 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
BLR vs. variability power-spectrum density profiles

  • 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
PSD shapes power-spectrum density profiles

  • 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

Thanks! power-spectrum density profiles


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