feyntrop is a computer program to evaluate Feynman integrals. The core C++ integration code, written mainly by Michael Borinsky, is an update of the proof-of-concept implementation tropical-feynman-quadrature, which was published with the paper ‘Tropical Monte Carlo quadrature for Feynman integrals’. feyntrop can be used through a high-level python interface, written by Henrik Munch.
The whole feyntrop project is managed as a github repository. Comments, bug-reports and pull-requests are very welcome.
If feyntrop is helpful in your research, please cite, M. Borinsky, H. J. Munch, F. Tellander: ‘Tropical Feynman integration in the Minkowski regime’, Computer Physics Communications, 292 (2023), 108874 arXiv:2302.08955 as well as M. Borinsky: ‘Tropical Monte Carlo quadrature for Feynman integrals’, Annales de l’Institut Henri Poincaré D (2023) to appear (online first) arXiv:2008.12310.
The implementation internally uses Eigen, OpenMP, the xoshiro256+ random number generator and the JSON for Modern C++ library.
To download feyntrop use the command
git clone https://github.com/michibo/feyntrop.git
cd feyntrop
To (re)compile feyntrop call
make clean && make
If the compilation fails, check if you have a suitable C++ compiler installed (see below for macOS problems).
The default macOS C++ compiler does not support OpenMP. So on macOS, the compilation of feyntrop might also fail with an error mentioning the -fopenmp flag. If you use homebrew, installing an OpenMP compatible C++ compiler via the command
brew install libomp
might help. If that does not help, then probably the paths to the compiler binaries in the Makefile need to be adjusted for your local environment. See the Makefile for some hints on how to do this.
To run the tests, make sure to have python installed.
To ensure that feyntrop has been built correctly, please run the file /tests/test_suite.py. That means, run
cd tests
python test_suite.py
This python script uses feyntrop to compute examples between 1-2 loops and 2-5 points, and then compares against pre-computed values. Ratios between newly computed and pre-computed coefficients in the epsilon expansion will be printed, which should all be close to 1.
If you cannot or don’t want to use python, you can also directly test the C++ compiled code by running
./feyntrop < low_level_input.json
In the top-level directory. The output should be the value of the Feynman integral of the wheel graph with three spokes. This output roughly looks as follows (see the Low-level interface section below for details of the format):
{"IGtr":84.0,"integral":[[[7.215238614660525,0.00203586844683068],[0.0,0.0]],[[-57.629482716637696,0.018239280410844466],[0.0,0.0]],[[240.79344300578586,0.09697082078903732],[0.0,0.0]]],"seconds preprocessing":0.001007578,"seconds sampling":2.0064038810000002}
To run the tutorial (which uses the high-level interface) in notebook form, you have to have jupyter-notebook installed. Run
jupyter notebook tutorial_2L_3pt.ipynb
in the top directory of this repository to open the tutorial notebook.
Examples can be found in the examples/ folder in this repository.
In this folder, one can for instance run the 5 loop 2-point example from the paper with the command
python 5L_2pt.py
feyntrop can also be used without python. For instance, this might be convenient in a high-performance computing environment. To use this interface, create a file similar to the low_level_input.json file in this repository. Here is the content of this file:
{
"graph" : [ [ [0, 1], 1 ], [ [0, 2], 1 ], [ [0, 3], 1 ], [ [1, 2], 1 ], [ [2, 3], 1], [ [3, 1], 1 ] ],
"dimension" : 4,
"scalarproducts" : [ [ -3, 1, 1, 1 ],
[ 1, -3, 1, 1 ],
[ 1, 1, -3, 1 ],
[ 1, 1, 1, -3 ] ],
"masses_sqr" : [ 0, 1, 2, 3, 4, 5 ],
"num_eps_terms" : 3,
"lambda" : 0,
"N" : 10000000
}
The field "graph" encodes the Feynman graph. It is a list of edges of the form
[ [ [v0, w0], nu0 ], [ [v1, w1], nu1 ], [ [v2, w2], nu2 ],... ]
where v0,w0 and so on are pairs of vertices corresponding to an edge and nu0 is the corresponding edge weight.
The field "scalarproducts" is a matrix of scalar products. The (v,w)-th entry of the matrix is the scalar product of p_u * p_v where p_u is the incoming momentum into vertex u. The matrix must hence be symmetric and have as many rows and columns as there are vertices. (Vertices without incoming momentum can be represented by setting the respective row and column equal to 0.) Due to momentum conservation, the rows and columns of the matrix must sum to 0.
The field "masses_sqr" is a list of masses, which contains one mass for each edge. (Of course, the masses might be 0.)
The field "lambda" is the deformation parameter, "dimension" is the spacetime dimension, "num_eps_terms" is the order in the epsilon expansion that should be computed and "N" is the number of points that shall be sampled.
The content of the JSON file must be piped into the feyntrop executable file, which is created in the top-directory of this repository by the make command. For instance, like this:
./feyntrop < low_level_input.json
Among some logging information (via stderr), this command produces the output (via stdout) in JSON format
{"IGtr":84.0,"integral":[[[7.215238614660525,0.00203586844683068],[0.0,0.0]],[[-57.629482716637696,0.018239280410844466],[0.0,0.0]],[[240.79344300578586,0.09697082078903732],[0.0,0.0]]],"seconds preprocessing":0.001007578,"seconds sampling":2.0064038810000002}
where the field "IGtr" is the tropicalized Feynman integral (in this case equal to the Hepp bound),
the field "integral" contains the epsilon expansion of the integral (without the usual gamma-function prefactor), I = I0 + eps * I1 + eps^2 * I2 + ...,
in the form
[ [ [ Re(I0), Delta(Re(I0)) ], [Im(I0), Delta(Im(I0))] ], [ [ Re(I1), Delta(Re(I1)) ], [Im(I1), Delta(Im(I1))] ], ... ]
where Delta is the respective error term (i.e. one expected standard deviation) and
where Re and Im denote the real and imaginary part of the respective coefficient in the expansion.
The other fields give store the sampling and the preprocessing time.
If you are not interested in the logging information, use, for instance, the command
./feyntrop < low_level_input.json 2> /dev/null
instead.
By default, feyntrop uses the maximal number of available CPUs in the sampling step. This can be changed using the environment variable OMP_NUM_THREADS.
For instance, the command
OMP_NUM_THREADS=2 ./feyntrop < low_level_input.json 2> /dev/null
performs the sampling step for the integration of the input from low_level_input.json with only two threads.
A similar option can be used with the python interface. For instance,
OMP_NUM_THREADS=2 python 5L_2pt.py
runs the 5 loop 2-point integral example with 2 threads.