Spectral modeling and diagnostics in various astrophysical environments Jelle Kaastra SRON.

Spectral modeling and diagnostics in various

astrophysical environmentsJelle Kaastra

SRON

Topics

• Multi-temperature structure

• Resonance scattering in groups of galaxies

• Foreground absorption

• Photoionised outflows from AGN

Several examples using SPEX

(www.sron.nl/spex)

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I. Multi-temperature structure

A warning against over-simplification

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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-T 1T

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Complex temperature structure I(de Plaa et al. 2006)

• Sérsic 159-3, central 4 arcmin

• Better fits 1Twdemgdem

• Implication for Fe: 0.360.350.24

• Implication for O: 0.360.300.19

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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

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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)

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Resonance scattering & turbulence

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

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

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Results

• NGC 5813:

vturb = 140-540 km/s (15-45% of pressure)

• NGC 5044:

vturb >320 km/s (> 40% turbulence)

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III Foreground absorption

Nasty correction factors are interesting!

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Interstellar X-ray absorption

• High-quality RGS spectrum X-ray binary GS1826-238 (Pinto et al. 2010)

• ISM modeled here with pure cold gas

• Poor fit

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Adding warm+hot gas, dust

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Adding warm & hot gas

Adding dust

Oxygen complexity

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Interstellar dust

• SPEX (www.sron.nl/spex)

currently has 51 molecules with fine structure near K- & L-edges

• Database still growing (literature, experiments; Costantini & De Vries)

• Example: near O-edge (Costantini et al. 2012)

1722 Ang 23.7 Ang

Tra

nsm

issi

on

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: 4U1820-30, (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, pyroxeneWith Mg

Glassy structure +FeS

Cosmic rays+radiation

Sulfur evaporation GEMS

IV Photoionised outflows from AGN

The need for complete models

and excellent data

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Why study AGN outflows?

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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, Lkin depend on r

– Important for feedback

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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 spectraRGS 600 ks, Detmers et al. 2011 (paper III)

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Absorption Measure Distribution

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Ionisation parameter ξ

Em

issi

on m

easu

re

Col

umn

dens

ity Discrete components

Continuousdistribution

Temperature

Discrete ionisation components?Detmers et al. 2011

• Fitting RGS spectrum with 5 discrete absorber components (A-E)

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Continuous AMD model?Detmers et al. 2011

• 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 & NH

Discrete components

C

D

B

E

Pressure equilibrium? No!

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Pressure Pressure

Tem

pera

ture

Differences photo-ionisation models

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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 tr (density dependent as ~1/n) measuring delayed response yields

trnr

Time-dependent calculation

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Hard X

Soft X

Total

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

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Conclusions

• We showed 4 examples of different & challenging astrophysical modeling

• All depend on availability reliable atomic data

• The SPEX code (www.sron.nl/spex) allows to do this spectral modeling & fitting

• Code & its applications continuing development (since start 1970 by Mewe)

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