# Presentation: 700154

## Lower resolution X-ray spectroscopy

- Keith Arnaud
- NASA/GSFC & UMCP

- X-ray Astronomy School 2002

## Practical X-ray spectroscopy

- Most X-ray spectra are of moderate or low resolution (eg Chandra ACIS or XMM-Newton EPIC).
- However, the spectra generally cover a bandpass of more than 1.5 decades in energy.
- Moreover, the continuum shape often provides important physical information.
- Therefore, unlike in the optical, most uses of X-ray spectra have involved a simultaneous analysis of the entire spectrum rather than an attempt to measure individual line strengths.

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## 3C 273

- Martin Elvis

- Optical Spectrum

- Proportional counter        e.g. ROSAT PSPC

- CCD                    e.g. Chandra ACIS

- Grating

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## Can we start with these...

- and deduce this ?

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## Can we start with this...

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## and deduce this

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## The Basic Problem

- Suppose we observe D(I) counts in channel I (of N) from some source. Then :

- D(I) = T ∫ R(I,E) A(E) S(E) dE

-  T is the observation length (in seconds)
-  R(I,E) is the probability of an incoming photon of energy E being registered in channel I (dimensionless)
-  A(E) is the energy-dependent effective area of the telescope and detector system (in cm2)
-  S(E) is the source flux at the front of the telescope (in photons/cm2/s/keV

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## An example R(I,E)

- fluourescence

- escape

- photopeak

- fluorescence

- escape

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## The Basic Problem II

- D(I) = T ∫ R(I,E) A(E) S(E) dE

- We assume that T, A(E) and R(I,E) are known and want to solve this integral equation for S(E). We can divide the energy range of interest into M bins and turn this into a matrix equation :

- Di= T ∑ Rij Aj Sj

- where Sj is now the flux in photons/cm2/s in energy bin J. We want to find Sj.

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## The Basic Problem III

- Di =  T ∑ Rij Aj Sj

- The obvious tempting solution is to calculate the inverse of Rij, premultiply both sides and rearrange :

- (1/T Aj) ∑(Rij)-1Di =  Sj

- This does not work ! The Sj derived in this way are very sensitive to slight changes in the data Di. This is a great method for amplifying noise.

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## A (brief) Mathematical Digression

- This should not have come as a surprise to anyone with any data analysis experience. This is known as the “remote sensing problem” and arises in many areas of astronomy as well as eg geophysics and medical imaging.
- In mathematics the integral is known as a Fredholm equation of the first kind. Tikhonov showed that such equations can be solved using “regularization” - applying a priori knowledge to damp the noise.
- A familiar example is maximum entropy but there are a host of others. Some of these have been tried on X-ray spectra - none have had any impact on the field.

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## Forward-fitting

- The standard method of analyzing X-ray spectra is “forward-fitting”. This comprises the following steps...
-  Calculate a model spectrum.
-  Multiply the result by an instrumental response matrix.
-  Compare the result with the actual observed data by calculating some statistic.
-  Modify the model spectrum and repeat till the best value of the statistic is obtained.

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## Forward-fitting algorithm

- Calculate Model

- Convolve with detector response

- Compare to data

- Change model parameters

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## This only works if the model spectrum can be expressed in a reasonably small number of parameters so that the model can be varied in some sensible fashion.

- The aim of the forward-fitting is then to obtain the best-fit and confidence ranges of these parameters (cf Peter’s talks).

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## Spectral fitting programs

-  XSPEC - part of HEAsoft. General spectral fitting program with many models available.
-  Sherpa - part of CIAO. Multi-dimensional fitting program which includes the XSPEC model library and can be used for spectral fitting.
-  SPEX - from SRON in the Netherlands. Spectral fitting program specialising in collisional plasmas and high resolution spectroscopy.
-  ISIS - from the MIT Chandra HETG group. Mainly intended for the analysis of grating data.

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## Models

- All models are wrong, but some are useful - George Box

- X-ray spectroscopic models are usually built up from individual components. These can be thought of as two basic types -additive (an emission component e.g. blackbody, line,...) or multiplicative (something which modifies the spectrum e.g. absorption).

- Model = M1 * M2 * (A1 + A2 + M3*A3) + A4

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## Additive Models

- Basic additive (emission) models include :
-  blackbody
-  thermal bremsstrahlung
-  power-law
-  collisional plasma
-  Gaussian or Lorentzian lines
- There are many more models available covering specialised topics such as accretion disks, comptonized plasmas, non-equilibrium ionization plasmas, multi-temperature collisional plasmas...

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## Multiplicative (and Other) Models

-  photoelectric absorption due to our Galaxy
-  photoelectric absorption due to ionized material
-  high energy exponential roll-off.
-  edge with 1/E3 roll-off.
- XSPEC also has a couple of other types of model components (convolution, mixing) which are used like a multiplicative model but perform more complicated operations on the current model.

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## Roll Your Own Models

- In addition, there is also a standard format for files containing model spectra so these too can be fit to data without having to add new routines to XSPEC.

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## Finding the best-fit

- Finding the best-fit means minimizing the statistic value. There are many algorithms available to do this in a computationally efficient fashion (see Numerical Recipes). Most methods used to find the best-fit are local i.e. they use some information around the current parameters to guess a new set of parameters. All these methods are liable to get stuck in a local minimum.
- Watch out for this !
- The more complicated your model and the more highly correlated the parameters then the more likely that the algorithm will not find the absolute best-fit.

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## Finding the best-fit II

- Sometimes you can spot that you are stuck in a local minimum by using the XSPEC error or steppar commands. These both step through parameter values, error in the vicinity of the current best-fit and steppar over a user-defined grid, and thus can stumble across a better fit. Crude but sometimes effective.

- You can do this in a semi-automated fashion by using a local minimization algorithm and following this with the error command with the ability to restart if a new minimum is found during the search.

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## Global Minimization

- A new technique called Markov Chain Monte Carlo, which provides an intelligent sampling of parameter space, looks promising but it is not yet widely available (i.e. I’ve not added it to XSPEC - yet).

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## Markov Chain Monte Carlo

- The MCMC method then consists of setting up a Markov Chain such that the stationary distribution is the distribution of interest. The Markov Chain values then provide a sampling of the distribution which we can use for integration or characterization.

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## Markov Chain Monte Carlo II

- Suppose that our target distribution is p(X). We are at Xt in the chain.
-  Sample a candidate point Y from a proposal distribution q(Xt).
-  Accept Y with a probability p(Y)q(X|Y)/p(X)/q(Y|X).
-  If the candidate is accepted set Xt+1=Y otherwise Xt+1=Xt.

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## Markov Chain Monte Carlo III

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## Final Advice and Admonitions

-  Remember that the purpose of spectral fitting is to attain understanding, not fill up tables of numbers.
-  Don’t bin up your data - especially in a way that is dependent on the data values (eg group min 15).
-  Don’t misuse the F-test.
-  Try to test whether you really have found the best-fit.

- X-ray Astronomy School 2002