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Characterizing activity in AGN with X-ray variability

Characterizing activity in AGN with X-ray variability. Rick Edelson. Snippets of history. Optical discovery & study came first Seyfert classification based on emission lines First observations only possible in optical Still most accessible, well-studied waveband

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Characterizing activity in AGN with X-ray variability

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  1. Characterizing activity in AGNwith X-ray variability Rick Edelson

  2. Snippets of history • Optical discovery & study came first • Seyfert classification based on emission lines • First observations only possible in optical • Still most accessible, well-studied waveband • Flat IR thru X-ray SEDs, e.g. Elvis (1987) • Mushotzky (2004, astro/ph0405144) review • Concl: most effective AGN surveys in X-rays • Essentially all “Radiating Supermassive Black Holes” (AGN) show detectable hard X-ray activity

  3. X-rays are best activity indicator: 1) Reach deepest into heart of the AGN • Rapid var → emission from inner lt-hrs • Natural probe of central engine 2) No confusing emission components • Other local components and external sources generally don’t emit strongly in X-rays • Other ls provide info on orientation, etc. • Produced lt-days to lt-years out

  4. Principal Component Analysis • “PCA” first applied to AGN by Boroson & Green (1992, ApJS, 80,109) • Optical data on 92 opt/UV-selected quasars • “Principal Eigenvector”: strong correlation of Hb width and Fe II strength, other line params • Secondary strongly correlated with luminosity • Principal eigenvector linked to X-ray slope • Boller, Brandt & Fink (1996, A&A, 305, 53) • X-ray softness correlated with Hb width

  5. Boller et al. (1996) correlation of Hb FWHM and X-ray G

  6. X-ray variability in Radiating Supermassive Black Holes • Non-statistical indications of “extreme” variability in X-ray soft sources • IRS 13224: Boller et al. (1997, MN, 289, 393) • Akn 564: Edelson et al. (2002, ApJ, 568, 610) • Statistical link w/X-ray var. amplitude (sxs) • Turner et al. (1999, ApJ, 524, 667) andO’Neill et al. (2005, MNRAS, 358, 1405) • Correlated “excess variance” w/ various properties for ~day-long ASCA light curves • Found corr. w/ luminosity, optical params.

  7. 35 days of X-ray coverage of Akn 564. Note strong X-ray variability; UV/optical varied 15% peak-peak in this period.

  8. Sixteen single-orbit light curves (1 point on previous graph) in which Akn 564 varies by factor of 2 within 3000 sec.

  9. Why X-ray Varibility Classification? • AGN “stick out” the most in the X-rays • X-rays give best access to nuclear region • Bulk of lower-energy from lt-weeks–years out • Optical emission lines formed lt-days out • X-rays come from inner lt-hours • Variability indicates activity time/size scale • Test this by correlating X-ray variability with traditional eigenvectors of activity

  10. XMM and X-ray variability • Rapid X-ray variability is a powerful tracer of activity in Radiating SMBHs • XMM provides best opportunity to exploit it • LEO light curves (ASCA, Swift) are interrupted this destroys key info on 3-10 ks timescale • XMM can detect var. on <100 sec timescales • Chandra also uninterrupted, but lower sens. • Sensitive, uninterrupted XMM light curves ideal probes of critical short timescales

  11. XMM Variability Study • w/ Simon Vaughan, Ken Pounds • XMM Variability Sample • 29 Sy1s w/ >30 ks obs, good bkgd, opt. data • Measured Excess Variance (sxs) • Measured 4 ks time scale: shortest ever • Errors on individual estimate of order unity • Averaged multiple (10-100) estimates to beat down errors • Confirmed that sxs stable in different periods

  12. XMM light curves of sources w/ a range of variability levels. Note the tabulated quantity is Fvar = sqrt(sxs2). Fvar = 41% Fvar = 22% Fvar < 1.7% Fvar < 1.7% Fvar = 19% Fvar = 11%

  13. Variability Study Results • Used ASURV to correlate 4 parameters: • X-ray excess variance (sxs) • X-ray slope (G) • Hb FWHM • Luminosity (0.2-10 keV) • Strongest correlations involved Hb • sxs vs. Hb FWHM (p < 0.01%) • G vs. Hb FWHM (p = 0.26%) • sxs vs. Lx weaker than expected (p = 1.6%)

  14. Multi-parameter correlations. The strongest correlations are shown on the left. p < 0.01% p = 0.52% p = 1.6% p = 0.26% p = 6.7% p = 22%

  15. Implications • Short time scale X-ray variability better correlated w/ Hb FWHM than luminosity • X-ray variability most likely linked to mass of supermassive black hole → Hb FWHM is a better mass indicator than luminosity → Efficiency is not constant • Improved X-ray, optical data; censored PCA methods key to further progress

  16. State of X-ray Astronomy • Right now lots of X-ray satellites: XMM, Chandra, RXTE, Suzuki & Swift • Con-X, XEUS mega-missions planned for the 2020s • Doubtful they will proceed fully as hoped • No missions are planned for the interim • We will lose the ability to see in the X-rays starting in about 10 years • This would be a disaster for AGN studies

  17. Conclusions • Rapid X-ray variability most strongly correlated with Hb FWHM (an indicator of SMBH mass) • X-rays are allowing the deepest probes of the central environment • Access to the X-rays will be lost in next ~10 years unless we act quickly

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