x ray variability of 104 active galactic nuclei xmm newton power spectrum density profiles
Download
Skip this Video
Download Presentation
X-ray variability of 104 active galactic nuclei XMM-Newton power-spectrum density profiles

Loading in 2 Seconds...

play fullscreen
1 / 30

X-ray variability of 104 active galactic nuclei XMM-Newton power-spectrum density profiles - PowerPoint PPT Presentation


  • 63 Views
  • Uploaded on

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.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' X-ray variability of 104 active galactic nuclei XMM-Newton power-spectrum density profiles' - masato


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
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
  • Introduction
  • Sample and Data
  • Data Analysis
  • Results
  • Discussion
introduction1
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 data1
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)
2 10 kev luminosity
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
PSD estimation
  • For a given PSD model P(ν;θ), likelihood function:
  • I: observed P: model
  • Confidence intervals:
two models
Two models
  • A. Simple power law:
  • B. Bending power law:
select model
Select model
  • LRT: Likelihood ratio test
  • Not well calibrated
  • Accurate calibration: computation expensive
qpos check
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
result 1 variability
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
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
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
Result 4 – bend frequency
  • Mean value: log(v_b) = -3.47 ± 0.10
result 5 bend amplitude
Result 5 – bend amplitude
  • Papadakis(2004): A=ν×F(ν) roughly constant at bend frequency
result 6 leakage
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
result summary
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
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 relation1
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
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
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)
ad