1. Studying AGN with high- resolution X-ray spectroscopy Jelle Kaastra SRON Nahum Arav, Ehud Behar, Stefano Bianchi, Josh Bloom, Alex Blustin, Graziella Branduardi-Raymont, Massimo Cappi, Elisa Costantini, Mauro Dadina, Rob Detmers, Jacobo Ebrero, Peter Jonker, Chris Klein, Jerry Kriss, Piotr Lubinski, Julien Malzac, Missagh Mehdipour, Stéphane Paltani, Pierre-Olivier Petrucci, Ciro Pinto, Gabriele Ponti, Eva Ratti, Katrien Steenbrugge, Cor de Vries

2. 2 Introduction: The influence of AGN outflows Dispersal heavy elements into IGM & ICM Ionisation structure IGM  evolution host galaxy How created? Structure? Mass & energy? Confinement? Crucial to understanding central engine: –Accretion process –Energy budget

3. 3 AGN outflows in a nutshell Photo-ionised gas v = -100 to -1000 km/s Seen through line and continuum absorption Spectrum  ionic column densities  ionization parameter ξ=L/nr² Kaastra et al. 2000

4. 4 Photoionisation modelling Radiation impacts a volume (layer) of gas Different interactions of photons with atoms cause ionisation, recombination, heating & cooling In equilibrium, ionisation state of the plasma determined by: –spectral energy distribution incoming radiation –chemical abundances –ionisation parameter ξ=L/nr 2 with L ionising luminosity, n density and r distance from ionising source; ξ essentially ratio photon density / gas density

5. Question 1: Structure outflow Is it more like rather uniform density clouds in pressure equilibrium? Or is it more like coronal streamers, with lateral density stratification? 5

6. Absorption measure distribution 6 Ionisation parameter ξ Emission measure Column density Discrete components Continuous distribution Temperature

7. 7 Separate components in pressure equilibrium? Not all components in press. eq. (same Ξ) Division into ξ comps often poorly defined  Continuous N H (ξ) distribution? Others fit discrete components What's going on? Steenbrugge et al. 2005

8. 8 Question 2: Where is the gas? Photo-ionization modeling  ξ=L/nr² L obtained from spectrum  only the product nr² known, not r or n Is gas accelerating, decelerating?

9. Density estimates: line ratios C III has absorption lines near 1175 Å from metastable level Combined with absorption line from ground (977 Å) this yields n  n = 3x10 4 cm -3 in NGC 3783 (Gabel et al. 2004)  r~1 pc Only applies for some sources, low ξ gas X-rays have similar lines, but sensitive to higher n (e.g. O V, Kaastra et al. 2004); no convincing case yet 9

10. 10 Density estimates: reverberation If L increases for gas at fixed n and r, then ξ=L/nr² increases  change in ionisation balance  column density changes  transmission changes Gas has finite ionisation/recombination time t r (density dependent as ~1/n)  measuring delayed response yields t r  n  r

11. 11 Reverberation: NGC 3783 RGS data (Behar et al. 2003): no change in Warm absorber  n 10 pc. EPIC data (Reeves et al. 2003): change in Warm absorber (larger columns)  n>10 8 cm -3, r<0.02 pc.

12. 12 Observation campaign Mrk 509 Core: 10 x 60 ks XMM, spaced 4 days (RGS, EPIC & OM all used!) Simultaneous Integral 10 x 120 ks Followed by 180 ks Chandra LETGS, simultaneous with 10 orbits COS (HST) Preceded with Swift (UVOT, XRT) monitoring Supplemented with ground-based (WHT, Pairitel) photometry & grism Period: 4 Sept – 13 Dec 2009 (100 days) 7 papers submitted/accepted, 8 in progress

13. General data analysis issues Excellent quality  many new steps developed RGS: full usage multi-pointing mode, refinements combining spectra with variable hot pixels, λ-scale, effective area, reducing response 2Gb  8 Mb, rebinning…) See Kaastra et al. 2011; see auxilary programs in SPEX distribution www.sron.nl/spexwww.sron.nl/spex PN: Triggered by our campaign improved gain cal OM: extended wavelength range optical grism HST/COS: extensive efforts to improve data analysis for this high-quality spectrum SPEX: allows simultaneous fitting high-res UV & X-ray spectra 13

14. Broad-band spectrum & variability (Mehdipour et al., Petrucci et al., talks this afternoon) 14

15. . Broad and neutral Fe K emission well correlated with continuum emission on few days time- scales. No relativistic profile Origin outer disc or inner BLR Fe-K line & variability (Ponti et al.; talk Petrucci et al.) 3-10 keV flux Line Intensity

16. 16 OM Grism spectrum

17. 17 UV-optical variability (OM, Swift) Source was in outburst (0.1 dex in UV) right in the middle of ourt campaign!

18. ISM absorption (talk Pinto et al. on Monday) 18

19. Abundances (Steenbrugge et al., poster G45) 19

20. COS & FUSE UV Absorption lines in Mrk 509 (Kriss et al., talk this afternoon) O VI,Lyβ, & Lyγ from FUSE (Kriss et al. 2000) 14 velocity components seen in COS UV spectrum C IV doublet split by only 500 km/s, so grey regions can’t be used for optical-depth Red lines: velocities X-ray absorbers XMM-Newton/RGS Blue lines: velocities X-ray absorbers Chandra/LETGS 20

21. LETGS + COS data (Ebrero et al., poster G14) X-rays: outflow with 3 distinct ionization phases in form of multi-velocity wind. UV spectra: absorption system with 13 kinematic components Analysis kinematic properties & column densities absorbers  UV-absorbing gas co- located with & embedded in lower density high-ionization X-ray absorbing gas 21

22. 22 Stacked RGS spectrum Galactic O I edge Several narrow absorption lines Detection 31 individual ions Tight upper limits column densities 18 other ions Two main velocity components (+40 and -300 km/s) Highly ionised ions in general higher velocity

23. 23 No O I from host galaxy O I host galaxy (not detected, N H <5x10 18 cm -2 )

24. 24 X-ray analysis in more detail Fit spectra using a power law + modified blackbody (or even a spline) continuum Where needed, add emission lines: BLR or NLR X-ray lines (no relativistic lines needed) Fit warm absorber using a model  ionic or total column densities Using photo-ionisation model, derive absorption measure distribution N H (ξ) Spectral fits done with SPEX, global fits

25. 25 Sample high-resolution spectra

26. 26 Example of broad emission lines O VIII Lyα O VII triplet

27. Photoionisation modelling RGS spectrum (Detmers et al. 2011) Use time-averaged SED Test dependence on SED Proto-solar abundances Multiple ionisation, 3 velocity components Same ion may be present in more than one velocity/ionisation component! 27 Mehdipour et al. 2011

28. 28 Discrete versus continuous absorption measure distribution? Column densities well approximated by sum of 5 components (see table) Higher column for higher ionisation parameter But what about a continuous model? AB C D E

29. 29 Continuous model Fitted columns with continuous (spline) model Surprise: comps C & D pop-up as discrete components! Upper limits FWHM 35 & 80 % Component B (& A) too poor statistics to prove if continuous Component E also poorer determined: correlation ξ and N H. C D B E

30. Where is the gas? Needs t-dependent model For each of the 5 components: if L increases, ξ must increase (as ξ=L/nr 2 ) From 10 continuum model fits  predicted ξ change for each of 10 observations, compared to average spectrum If gas responds immediately, transmission outflow changes immediately 30

31. 31 Expected response absorbers for 0.1 dex luminosity increase

32. Time-dependent photo- ionisation Time evolution ion concentrations n i : dn i /dt = A ij (t) n j A ij (t) contains t- dependent ionisation & recombination rates 32

33. Location photoionised gas (work in progress) Component A: Rotating, low ionisation disk + high velocity outflow, 3 kpc (direct imaging [O III] 5007, Philips 1986) Component C: >10 pc (lack of response) Component E: > 1 pc? (lack of response?) See also Kriss (talk this afternoon) and poster Ebrero 33

34. 34 Mass loss through the wind v (km/s)-100-1000 ξ=1ξ=10.0010.0001 ξ=10001.00.1

35. 35 Conclusions Deep, multi-wavelength monitoring campaigns (AGN) are rewarding: High quality spectra, not limited by statistics “Continuous” light curves, allowing to monitor the variations