Michael Parker

This page contains the xspec table models used for fitting the variance spectra of X-ray sources.

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The models can be divided into two classes: additive models, which account for the primary processes driving the observed variability (such as variable absorption or changes in the coronal powerlaw emission), and multiplicative models, which modify the additive variability to account for secondary effects, such as damping from less variable emission processes (for example, distant reflection).

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Models are calculated with the Monte-Carlo method described in Parker et al 2020. These models are simple, cover a limited parameter space, and rely on basic assumptions of variability. Use at your own risk! We strongly suggest reading the guidelines below, in full, before use.

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General guidelines:

• Additive models have a normalization parameter. This parameter should generally be fixed to 1, otherwise the variance parameters are meaningless. However, if you do not care about the units of the normalisation/variance, it may be faster in some cases to fix the variance and fit the normalisation, as this does not require interpolation in the table model.

• No Galactic absorption is required, or any other constant multiplicative component.

• Do not use these models with count spectra. That’s not what they’re for.

• Because the models are relatively unsophisticated compared to conventional X-ray models, we recommend adding a small 1-2% systematic error. This is probably not necessary for low quality RMS spectra, or for very simple (i.e. powerlaw) spectra.

• Note that some versions of xspec have a bug when adding systematic error that may cause them to not fit. This can be avoided by running an initial fit without systematic error, before adding it in and re-fitting.

• When you use these models, please cite the relevant papers.

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New models:
New models will be added as we generate them. If you need a specific model that we do not provide, please get in touch.

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Variance Spectra in XSPEC:

These models all require a pre-calculated variance spectrum to be loaded into XSPEC. The procedure for this is fairly simple, once you have a variance spectrum (see e.g. Vaughan et al., 2003, for an explanation of how to calculate one). Once you have a variance spectrum, the simplest way to load it into XSPEC is using the flx2xsp/ftflx2xsp tool, which converts a text file into an XSPEC compatible .pha file and a unit diagonal response matrix.

 

Note that in general standard response matrices are not appropriate for variance spectra, as the variance is not equivalent to counts and will not redistribute in the same way. In most cases an energy-dependent Gaussian smoothing (using the gsmooth model in XSPEC) will give a good approximation of the energy resolution of the detector, and most other effects are normalised out when calculating an Fvar spectrum.