#### Beta Profile [Equation of the Week]

X-ray telescopes generally work by reflecting photons at grazing incidence. As you can imagine, even small imperfections in the mirror polishing will show up as huge roadbumps to the incoming photons, and the higher their energy, the easier it is for them to scatter off their prescribed path. So X-ray telescopes tend to have sharp peaks and fat tails compared to the much more well-behaved normal-incidence telescopes, whose PSFs (Point Spread Functions) can be better approximated as Gaussians.

X-ray telescopes usually also have gratings that can be inserted into the light path, so that photons of different energies get dispersed by different angles, and whose actual energies can then be inferred accurately by measuring how far away on the detector they ended up. The accuracy of the inference is usually limited by the width of the PSF. Thus, a major contributor to the LRF (Line Response Function) is the aforementioned scattering.

A correct accounting of the spread of photons of course requires a full-fledged response matrix (RMF), but as it turns out, the line profiles can be fairly well approximated with Beta profiles, which are simply Lorentzians modified by taking them to the power *β * –

where B*(1/2,β-1/2)* is the Beta function, and *N* is a normalization constant defined such that integrating the Beta profile over the real line gives the area under the curve as *N*. The parameter *β* controls the sharpness of the function — the higher the *β*, the peakier it gets, and the more of it that gets pushed into the wings. Chandra LRFs are usually well-modeled with *β~2.5*, and XMM/RGS appears to require Lorentzians, *β~1*.

The form of the Lorentzian may also be familiar to people as the Cauchy Distribution, which you get for example when the ratio is taken of two quantities distributed as zero-centered Gaussians. Note that the mean and variance are undefined for that distribution.

## TomLoredo:

“The form of the Lorentzian may also be familiar to people as the Cauchy Distribution…” This covers the beta=1 case. More generally it looks like Student’s t distribution, of which Cauchy is a special case.

Thanks, Vinay, for pointing out someplace unexpected where this distribution appears!

06-06-2008, 1:44 pm