Kaspi et al 2002
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Transcript of 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