kaspi et al 2002

Constraints on the Velocity Distribution in the NGC 3783 Warm Absorber

T. Kallman, Laboratory for X-ray Astrophysics, NASA/GSFC

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Kaspi et al 2002

900 ksec Chandra HETG observation of NGC3783>100 absorption featuresblueshifted, v~800 km/sbroadened, vturb~300 km/semission in some componentsfit to 2 photoionization model componentsPiecewise continuumNot a full global fit

Kaspi et al 2002

900 ksec Chandra HETG observation of NGC3783>100 absorption featuresblueshifted, v~800 km/sbroadened, vturb~300 km/semission in some componentsfit to 2 photoionization model componentsPiecewise continuumNot a full global fit

Krongold et al 2004

>100 absorption featuresblueshifted, v~800 km/sbroadened, vturb~300 km/semission in some componentsfit to 2 photoionization model componentsFe M shell UTA fitted using Gaussian approximationFull global model

Krongold et al 2004

>100 absorption featuresblueshifted, v~800 km/sbroadened, vturb~300 km/semission in some componentsfit to 2 photoionization model componentsFe M shell UTA fitted using Gaussian approximationFull global model

Krongold et al 2004

fit to 2 photoionization model componentsIonization parameter and temperature are consistent with coexistence in the same physical regionDisfavored existence of intermediate ionization gas due to shape of Fe M shell UTABut used simplified atomic model for UTA

Curve of thermal equilibrium for photoionized gas

Flux/pressure

Chelouche and Netzer

2005

• Combined model for dynamics and spectrum

• Assumes ballistic trajectories

• Favors clumped wind

Blustin et al. (2004)

Fitted the XMM RGS spectrum using global modelAlso find evidence for two componentsOmit CaInclude line-by-line treatment of M shell UTA, but still miss someClaim evidence for higher ionization parameter materialrequire large overabundance of iron

Work so far on fitting warm absorber spectra has concentrated on the assumption of a small number of discrete components

This places important constraints on the flow dynamics, if it is true There is no obvious a priori reason why outflows should favor a

small number or range of physical conditions In this talk I will test models in which the ionization distribution is

continuous rather than discrete, and discuss something about what it means

Previous tests of this have invoked simplified models for the Fe M shell UTA which may affect the result

Proga and Kallman 2004

Disk winds have smooth density distributions on the scales which can be Calculated…

Work so far on fitting warm absorber spectra has concentrated on the assumption of a small number of discrete components

This places important constraints on the flow dynamics, if it is true It is not obvious why outflows should favor a small number or range

of physical conditions I will examine the robustness of this result and discuss some

implications

Outline

Lines are not symmetricblue wing is more extended in lines from ions which occur at smaller ionization parameter (eg. O VIII L vs. S XVI)Suggests that higher velocity material is less ionized

(Ramirez et al. 2005)

I will not discuss details about Line Profile Shapes, widths and blueshifts

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2~64905/8192

As a start, fit to a continuum plus Gaussian absorption lines. Choose a continuum consisting of a double power law plus cold absorption(1=1.5, 2=3, log(NH)=22.5; This is a cheat because it already takes into account some of the absorbtion, but does not strongly affect the results)

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2~64602/8192

Absorption lines are placed randomly and strength and width adjusted to improve the fit.

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2~59716/8192

Absorption lines are placed randomly and strength and width adjusted to improve the fit.

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2~51773/8192

Absorption lines are placed randomly and strength and width adjusted to improve the fit.

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2~43787/8192

Absorption lines are placed randomly and strength and width adjusted to improve the fit.

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2~28526/8192

Absorption lines are placed randomly and strength and width adjusted to improve the fit.

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2~16070/8192

Absorption lines are placed randomly and strength and width adjusted to improve the fit.

2~9915/8192

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Absorption lines are placed randomly and strength and width adjusted to improve the fit.

requires ~950 lines, Ids for ~100

2~9915/8192 300

km/s<v/c<2000 Represents best

achievable 2 (by me) Allows line Ids Shows distribution of

line widths, offsets

Results of notch model:

requires ~950 lines, Ids for ~100

2~9915/8192 300

km/s<v/c<2000 Represents best

achievable 2 (by me) Allows line Ids Shows distribution of

line widths, offsets

Ionization parameter of maximum ion abundance vs. line wavelength for identified lines

Favored region

Photoionization Models Full global model Pure absorption (maybe misses something important?) Single velocity component Xstar version 2.1l

Inner M shell 2-3 UTAs (FAC; Gu); >400 lines explicitly calculated

Chianti v. 5 data for iron L Iron K shell data from R matrix (Bautista, Palmeri, Mendoza et

al) Available from xstar website, as are ready-made tables

Not in current release version, 2.1kn4 Xspec ‘analytic model’ warmabs

Not fully self consistent: assumes uniform ionization absorber, but this is small error for low columns.

Xspec11 only at present, not quite ready for prime time

Comparison of model properties

X* release (2.1kn4) X* beta (2.1l) warmabs others

Xspec interface tables tables analytic various

Atomic data KB01 K04, chianti5 K04,chianti5 various

‘real’ slab y y n various

Energy resolved ? ? y various

Self-consistent SED y y n ?

nlte y y y ?

radiative transfern n n ?

dynamics n n n n

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single Component Fit, log=2.4

2~27811/8192, voff=700 km/s vturb=300 km/s

Missing lines near 16-17A

2 Component Fit, log=2.4, 0.1

2~18516/8192, voff=700 km/s vturb=300 km/s

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Better, Some discrepancies, Missing lines

Fitting absorption only is fraught, due to influence of scattering/reemission

Si VII-XI K lines

Al XIII Al XII

Fe XX-XXII

Fe XXII

Fe XXI

Comparison with previous work

Netzer Krongold Blustin Me

Log(U) -0.6,-1.2 0.76 0.45

Log1) 3.7,3.1 2.25 2.4 2.2

Log(N1) 22.2 22.2 22.45 21.4

Log(U) -2.4 -0.78 -1.55

Log2) 0.69 0.72 0.3 0.2

Log(N2) 21.9 21.6 20.73 20.4

2 component fit:

2 component fit:

What if the distribution is continuous instead?

2~20071/8192, voff=700 km/s vturb=300 km/s

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Continuous distribution, 0.1<log2.4

Over-predicts the absorptionDue to intermediate iron ions

Continuous distribution, 0.1<log2.4

What happens if we iteratively search for the best fit, starting with a continuous distribution?

The best fit turns out to be the same 2 component fit…

What about extended power law distribution?

2~21826/8192, voff=700 km/s vturb=300 km/s

Power law distribution, extended to -0.8<log<3.2

There can be some gas at Higher ionization parameter. Fit is slightly worse

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A summary of 2

Notch 11945

single 27811

2 component 18516

continuous 20071

extended 22871

best 17445

What does this mean? Why do we preferentially see only a few components in ?

First, consider the most likely conditions…

Radius (timescales scale the same with radius)

vvir=2GM/R)1/2~300 km/s => R~10 17 v300-2 M6cm

density

=L/nR2 ~102 erg cm/s => n~ 108 L44 R17-2 2

-1 cm-3

Physical thickness

N=n R ~1022 cm-2 => R~1014 n8-1 N22 cm

So R/R <<1. Absorber is thin compared with radius

Now consider the timescales…

Cooling timescale

tcool=kT/(n<v><>) ~104 n8-1 T5 s

Flow timescale

tflow=R /v ~10 6.5 R 14 v300 s

So it looks like thermal equilibrium should be OK.

Sound crossing time

texp=R /cs ~10 7.5 R 14 s

Pressure equilibrium is questionable

If the gas is in photoionization equilibrium: temperature vs.

Equilibrium temperature vs.

Ionization parameters don’t quite line when calculated this way…

Krongold et al 2004

Curve of thermal equilibrium for photoionized gas

Flux/pressure

Krongold et al. 2002

SEDs

Krongold et al. 2002

SEDs

me

Finding stable temperatures at observed ,T requires some fine tuning of the SED

Another way of think about this: cooling curve is multi-valued, and appears to correspond with derived ionization parameters

If so, then the absence of higher material is associated with rapid increase in cooling For >3. Implies some non-photoionization heating.Mass loss estimates based on observed and v would be valid

What if the gas is not in ionization equilibrium…. If the heating timescale is short compared with the flow time,then

what we observe is gas in a transient state on its way to being fully ionized

The net heating rate increases steeply for >3, so the high ionization state may represent conditions where the gas accumulates, but is not in equilibrium

This would require N22< 10-2.5 T5 v300-1

Tiny filaments?

What we see is superposition of many,

All in a transient state on their way to being highly ionized

Would occur if the dynamics do not provide enough density for equilibrium at T<<TIC

Mass loss rate is then determined by the heating time, not the flow time

Contours of constant heating (black) and cooling (blue) timescale vs and T for density 108

~104 s

shorter

longer

Which is a better model: this

Or this?

Summary

In spite of a priori expectations, continuous distribution of ionization conditions is an inferior fit to (some) high quality X-ray spectra

Implies some special meaning for inferred ionization parameters:

equilibrium: favored locus in (,T). Requires fine tuning, but does not appear to work in detail, since inferred ,T is not stable

Observed ,T could be stable if some other source of heating is acting

non-equilibrium: high is where the gas spends the most time before heating runs away toward Compton equilibrium. Requires very thin clouds

● Extra slides

What does this mean?

Why would warm absorber gas favor certain ionization parameters/densities?

Thermal properties? But what is the mechanism?

Time dependence? If so, tcool > tflow

tcool~n2<v><>, tflow~d/v

requires d18T5<10-9 n10v7. How?

equilibrium?

2 phase idea?

Is this the assymptotic state for the outflow?

Source variability?

Krongold et al 2005

● Claim detection of variability in UTA 10● No variability in high ionization component

● Claim that this implies clumping of low ionization component, since in smooth flow flux change would produce change in location of absn, not strength or ionizaation.

● But this only applies if gradient is small. This result would be better phrased as implying that gradient is large compared with change in location of ionization surfaces.

2 component fit:

What about a continuous distribution?

What about extended power law distribution?

Result:

We end up with the same 2 components

Now let’s see what happens when we iteratively fit for the ionization parameter distribution. Trial:

2~20071/8192, voff=700 km/s vturb=300 km/s

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Continuous distribution, 0.1<log2.4

Over-predicts the absorptionDue to intermediate iron ions

Another way of think about this: cooling curve is multi-valued, and appears to correspond with derived ionization parameters

Log()=1

1.9

2.8

Log()=1

1.9

2,8

3.7

4.6

Red=cooling rateBlack=heating rate

Another way of think about this: cooling curve is multi-valued, and appears to correspond with derived ionization parameters