Spectral modeling and diagnostics in various astrophysical environments Jelle Kaastra SRON

Topics Multi-temperature structure Resonance scattering in groups of galaxies Foreground absorption Photoionised outflows from AGN Several examples using SPEX ( 2

I. Multi-temperature structure A warning against over-simplification 3

4 The Fe bias 1T models sometimes too simple: e.g. in cool cores Using 1T gives biased abundances (“Fe- bias, Buote 2000) Example: core M87 (Molendi & Gastaldello 2001) Multi-T1T

5 Complex temperature structure I (de Plaa et al. 2006) Sérsic 159-3, central 4 arcmin Better fits 1T  wdem  gdem Implication for Fe: 0.36  0.35  0.24 Implication for O: 0.36  0.30  0.19

6 Inverse iron bias: how does it work? Simulation: 2 comp, T=2 & T=4 keV, equal emission measure Best fit 1-T gives T=2.68 keV Fitted Fe abundance 11 % too high Due to different emissivity for Fe-L, Fe-K

7 Complex temperature structure II (Simionescu et al. 2008) Example: Hydra A Central 3 arcmin: Full spectrum: Gaussian in log T (σ=0.2) 1T fits individual regions: also Gaussian Confirmed by DEM analysis (blue & purple)

II Resonance scattering in groups of galaxies The importance of accurate atomic data (Fe XVII) 8

Resonance scattering & turbulence 9

Resonance scattering (NGC 5813, de Plaa et al. 2012) 10

Measured and predicted line ratios (de Plaa et al. 2012) 11

Results NGC 5813: v turb = km/s (15-45% of pressure) NGC 5044: v turb >320 km/s (> 40% turbulence) 12

III Foreground absorption Nasty correction factors are interesting! 13

Interstellar X-ray absorption High-quality RGS spectrum X-ray binary GS (Pinto et al. 2010) ISM modeled here with pure cold gas Poor fit 14

Adding warm+hot gas, dust 15 Adding warm & hot gas Adding dust

Oxygen complexity 16

Interstellar dust SPEX ( currently has 51 molecules with fine structure near K- & L-edgeswww.sron.nl/spex Database still growing (literature, experiments; Costantini & De Vries) Example: near O-edge (Costantini et al. 2012) Ang 23.7 Ang Transmission

Absorption edges: more on dust optimal view O & Fe Fe 90%, O 20% in dust (Mg-rich silicates rather than Fe-rich: Mg:Fe 2:1 in silicates) Metallic iron + traces oxydes Shown: 4U , (Costantini et al. 2012)

Are we detecting GEMS? GEMS= glass with embedded metal & sulphides (e.g. Bradley et al. 2004) interplanetary origin, but some have ISM origin  invoked as prototype of a classical silicate Mg silicate Metallic iron FeS Crystal olivine, pyroxene With Mg Glassy structure + FeS Cosmic rays+radiation Sulfur evaporation GEMS

IV Photoionised outflows from AGN The need for complete models and excellent data 20

Why study AGN outflows? 21 Accretion Outflows Feeding the monster: delicate balance between inflow & outflow onto supermassive black hole Co-evolution of black hole & host galaxy Key to understand galaxy formation

Main questions outflows What is the physical state of the gas? –Uniform density clouds in pressure equilibrium? –Or like coronal streamers, lateral density stratification? Where is the gas? –Where is it launched? Disk, torus? –Mass loss, L kin depend on r –Important for feedback 22

23 Observation campaign Mrk 509 (Kaastra et al. 2011) Monitoring campaign covering 100 days Excellent 600 ks time-averaged spectrum Observatories involved: –XMM-Newton (UV, X-ray) –INTEGRAL (hard X-ray) –HST/COS (UV) –Swift (monitoring) –Chandra (softest X-rays) –2 ground-based telescopes

Sample spectra RGS 600 ks, Detmers et al (paper III) 24

Absorption Measure Distribution 25 Ionisation parameter ξ Emission measure Column density Discrete components Continuous distribution Temperature

Discrete ionisation components? Detmers et al Fitting RGS spectrum with 5 discrete absorber components (A-E) 26

27 Continuous AMD model? Detmers et al Fit columns with continuous (spline) model C & D discrete components! FWHM <35% & <80% B (& A) too poor statistics to prove if continuous E harder determined: correlation ξ & N H  Discrete components C D B E

Pressure equilibrium? No! 28 Pressure Temperature

Differences photo-ionisation models 29

30 Density estimates: reverberation If L increases for gas at fixed n and r, then ξ=L/nr² increases  change in ionisation balance  ionic 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

Time-dependent calculation 31 Hard X Soft X Total

Results: where is the outflow? (Kaastra et al. 2012) 32

Conclusions We showed 4 examples of different & challenging astrophysical modeling All depend on availability reliable atomic data The SPEX code ( allows to do this spectral modeling & fitting Code & its applications continuing development (since start 1970 by Mewe) 33