Notes for XSPEC Users
Users coming from XSPEC typically have a few questions about getting started with 3ML
Why use 3ML over XSPEC if I am just fitting X-ray data?
XSPEC is an amazing tool built by dedicated, friendly experts. If XSPEC is your favorite tool and you are only fitting x-ray data, then there are some marginal benefits:
3ML is a lighter install and natively in Python rather than a Python wrapper around XSPEC
3ML does many operations in parallel on HPC environments with little effort
No parsing of text files to save results! 3ML has a serializable fit results format (FITS or HDF5) that is portable and machine readable.
The modularity of 3ML means that the types of data it can fit will grow quickly
Do I need a new plugin for my instrument?
If it is an X-ray instrument that has PHA1 data, BAK files, RSPs and ARFs, nope! This is handled by the OGIPLike plugin.
Think of OGIPLike as an XSPEC-like object. Feed your data in and pass it to to the JointLikelihood or BayesianAnalysis objects. You need one plugin per observation.
OGIPLike is simply provides a wrapper around DispersionSpectrumLike that reads standard OGIP files. We are strict about following the OGIP standard.
Can I use XSPEC models?
astromodels provides and interface to XSPEC models. See details in the modeling section.
We are currently building our own set of standard models in XSPEC. We already have APEC, PhAbs, Wabs, Tbabs etc. So you can try those out first.
Can I trust the results of the fits?
With each build of 3ML, we always test the code automatically against XSPEC to ensure fitting (up to factor) and RSP convolution give the same results.
You can always try yourself as the file types are the same. Expect differences in fit results that could be due to the underlying fitting engines.
How do I fake a dummy response to fit optical data or a background model?
DON’T DO THAT!
Since 3ML is not limited to a rigid data format, we have custom plugins for photometric data. You simply need to provide the filter name and magnitude. See the docs for more details.
We have the ability to model background spectra along with source spectrum. Check out the background modeling n the docs.
How do I choose the likelihood statistic for my fit?
It is possible, but if your PHA files are formatted correctly, we probe them and choose the correct likelihood for your data.
We do support rebinning of data, but not for the purposes of moving from the Poisson regime to the so-called $chi^2$ regime. This is an incorrect and bad practice.
How do I plot unfolded data points along with my fit?
You can’t and you shouldn’t