MOS spectral redistribution function has evolved temporarily and spatially

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Steve Sembay ([email protected])Mallorca 26/10/06

MOS spectral redistribution function has evolved temporarily and spatially

whilst in orbit. We do not have an accurate physical description of the effect

which can be used to model the observed changes.

Problem #1

Observations of astronomical objects with known spectral parameters can

be used analytically to adjust the parameters of the rmf for all

detector/epoch/region/pattern combinations.

Solution #1

Problem #2

Extremely time consuming as this is largely a manual process requiring analysis of

potentially hundreds of spectral fits to fully characterise the rmf evolution.

Automated Spectral Response FittingSolution #2

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function myfunct, p ; p(n)

newccf = rmf_param_adjust(p, oldccf)

rmf = rmfgen(newccf)

chisq = xspec_fit(spectrum, model, rmf)

return, chisq

end

pro rmfmin, p, chimin

result = tnmin(‘myfunct’, p, bestmin=chimin, /autoderivative)

newccf = rmf_param_adjust(result, oldccf)

return

end

E = 1.49 keV

model ga σ = 0.0

N = free

σnew(1.49) = p x σold(1.49)

Code development by Jenny

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;+ ; NAME: ; TNMIN ; ; AUTHOR: ; Craig B. Markwardt, NASA/GSFC Code 662, Greenbelt, MD 20770 ; [email protected] ; UPDATED VERSIONs can be found on my WEB PAGE: ; http://cow.physics.wisc.edu/~craigm/idl/idl.html; ; PURPOSE: ; Performs function minimization (Truncated-Newton Method) ; ; MAJOR TOPICS: ; Optimization and Minimization ;; CALLING SEQUENCE:; parms = TNMIN(MYFUNCT, X, FUNCTARGS=fcnargs, NFEV=nfev, ; MAXITER=maxiter, ERRMSG=errmsg, NPRINT=nprint, ; QUIET=quiet, XTOL=xtol, STATUS=status,; FGUESS=fguess, PARINFO=parinfo, BESTMIN=bestmin, ; ITERPROC=iterproc, ITERARGS=iterargs, niter=niter) ;; DESCRIPTION:; ; TNMIN uses the Truncated-Newton method to minimize an arbitrary IDL ; function with respect to a given set of free parameters. Blah…….

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function myfunct, p ; p(n)

newccf = rmf_param_adjust(p, oldccf)

rmf = rmfgen(newccf)

chisq = xspec_fit(spectrum, model, rmf)

return, chisq

end

pro rmfmin, p, chimin

result = tnmin(‘myfunct’, p, bestmin=chimin, /autoderivative)

newccf = rmf_param_adjust(result, oldccf)

return

end

Slow ~ secs to mins

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Results from 3 sets of tests:

1) Fitting the Al calibration line(s) at epoch Rev 110-169

2) Fitting the low energy continuum of 3C273 at epoch 0-109

3) Fitting the continuum of RXJ1856 at epoch 744+

Mono-pixel spectra used throughout

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Test #1: Resolution at Al Kα

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“Red” wing is due to incomplete charge collection rather than intrinsic broadening

Al Kα = Kα1(1.4867) + 0.5 x Kα2(1.4863)

In our analytic rmf model we model this by splitting the profile

“Red” side = Gaussian(σ) + Gaussian(R x σ)

“Blue” side = Gaussian(σ)

Normalisations

match at join

In our automatic fitting we adjust the values of σ (~30) and

R (~1.5) at 1.487 keV by “fudge” factors, p, such that

σnew = p1 x σold

Rnew = p2 x Rold

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E = 1.5574, σ = 0.0, N = 0.01N0

Spectral Model: (ga + ga + ga) Fit Range: 1.4-1.56 keV


E = 1.4863, σ = 0.0, N = 0.5N0

E = 1.4867, σ = 0.0, N0 = free

P1 = 1.0

P2 = 1.0

Χ2ν = 18.4

P1 = 1.047

P2 = 0.894

Χ2ν = 7.74

Proc. Time = 1.72 hrs

α1

α2

β1

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MOS1 Mg XI line in Zeta Puppis, Rev 0156, RGS Model

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MOS1 ratio imageEnergy

Epoch

TU Mode

TU Mode

Test #2: Low energy continuum of 3C 273 (Rev 0094)Continuum (“hard power law + soft excess”) fits to 3c 273

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Test #2: Low energy continuum of 3C 273 (Rev 0094)

1.0

0.0

f(d)

d

f(d) = α + βd d < d0

= 1.0 d ≥ d0

d0

α

Eobs(d) = Ein(d) x f(d)

Integrate over d to get profile α

β

Surface Loss Function

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Alpha Parameter v Energy, Epoch Rev 0-109

Fudge factor defined at 350, 500, 650 eV

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nH = 1.79 x 1020 cm-2

Γ = 1.644, N0 = 0.0194

Γ = Free, N0 = Free

Spectral Model: phabs * (po + po) Fit Range: 0.1-1.0 keV

Rev 0094

Χ2ν = 3.01 Χ2

ν = 1.97

Proc. Time: 3.9 Hrs

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Alpha Parameter v Energy, Epoch Rev 0-109

Fudge factor defined at 350, 500, 650 eV

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Fit model to PN and fold through MOS1 (old and new rmf)


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RGS model (renormalised) fit to MOS1 in 0.1-0.55 keV band


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Test #3: BB fits to RXJ1856 (Rev 0798)

MOS1 Core

nH = 1.4(0.1)E20

kT = 61.4(0.2) eV

MOS1 Wings

nH = 1.1(0.2)E20

kT = 60.3(0.4) eV

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MOS2 Core

nH = 0.93(0.1)E20

kT = 62.0(0.3) eV

MOS2 Wings

nH = 0.98(0.2)E20

kT = 59.7(0.5) eV

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nH = 0.75 x 1020 cm-2

kT = 62 eV, N0 = Free

Spectral Model: phabs * bb Fit Range: 0.1-1.0 keV

Fudge Factors:

alpha surface loss parameters at…..250, 350, 450, 550 eV

Parameters reported by Vadim/Frank

for pn at previous Cal. meeting

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Fit to MOS1 data with new rmf

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Fit to MOS2 data with new rmf

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Fit to MOS1 3c 273 data with old/new rmf

nH = 2.6(0.2)e20 cm-2

nH = 2.1(0.2)e20 cm-2

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Fit to MOS2 3c 273 data with old/new rmf

nH = 2.4(0.2)e20 cm-2

nH = 1.9(0.2)e20 cm-2

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Conclusions

• Algorithm works!

• Timely results can be gained even using a single

CPU, but we intend to port code to the multi(250) processor

central computing facility at Leicester

• Smoothing out the residuals in early MOS 3c 273 data

improves cross-calibration with pn and probably rgs

• Forcing spectral agreement between MOS and pn in

RXJ1856 improves residuals in contemporary

MOS2 3c 273 data

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Test #2: Low energy continuum of 3C 273

MOS spectral redistribution function has evolved temporarily and spatially

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