A GPU-accelerated unbinned maximum likelihood fit using a Markov Chain Monte Carlo, intended for calculating confidence intervals or limits.
sxmc requires the following libraries:
It also uses hemi, which is included as a
git submodule. After cloning sxmc, run:
$ git submodule init
$ git submodule update
sxmc runs much faster with the help of a CUDA-enabled GPU. To build with GPU
support, set CUDA_ROOT to point to your installation of the CUDA tools:
$ CUDA_ROOT=/usr/local/cuda make
If no GPU is available, sxmc will simply loop instead of running things in
parallel. To build without GPU support:
$ make
You still need to have the CUDA headers installed, but no libraries or hardware are required.
By default, sxmc is built in debug mode. For much higher performance when
using GPU support, pass the OPTIMIZE=1 flag to make:
$ make OPTIMIZE=1
The code is fully documented for Doxygen. To view HTML documentation online, see here. To build HTML and LaTeX documentation yourself, run
$ make doc
The output is placed into the doc directory.
-
Create ROOT data files: The data used to build the PDFs is stored in TNtuples. The branch names match those used in the configuration file.
-
Configure fit: Set up the fit parameters and signal PDFs using a JSON-format configuration file. An example is provided in
config/. -
To run fits:
$ ./bin/sxmc config/your_file.json output_dir