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X-ray variability of 104 active galactic nuclei XMM-Newton power-spectrum density profilesPowerPoint Presentation

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

### Thanks! power-spectrum density profiles

Authors: O.Gonzalez-Martin, S. Vaughan

Speaker: Xuechen Zheng

2014.5.13

Outline power-spectrum density profiles

- Introduction
- Sample and Data
- Data Analysis
- Results
- Discussion

Introduction power-spectrum density profiles

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 power-spectrum density profiles

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 power-spectrum density profiles

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 power-spectrum density profiles

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

Two models power-spectrum density profiles

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

Select model power-spectrum density profiles

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

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 power-spectrum density profiles

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 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 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)

- Model A ---- α=2.01±0.01(T) 2.06±0.01(S) 1.77±0.01(H)

Result 4 – bend frequency power-spectrum density profiles

- Mean value: log(v_b) = -3.47 ± 0.10

Result 5 – bend amplitude power-spectrum density profiles

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

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 power-spectrum density profiles

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 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 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 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 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)

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