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, 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', Ann. Inst. Henri Poincaré Comb. Phys. Interact. 10 (2023), no. 4, pp. 635–685 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 a suitable C++ compiler is 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 the paths to the compiler binaries in the Makefile probably 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 was built correctly, run the file /tests/test_suite.py. That means running
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 them 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 must install jupyter-notebook. Run
jupyter notebook tutorial_2L_3pt.ipynb
in the top directory of this repository to open the tutorial notebook.
You might prefer a simple script file instead of a jupyter notebook. The file simple_example_2L_3pt.py contains the example from the tutorial notebook. The comments in this file completely explain the usage of feyntrop using the Python interface.
Moreover, the examples/ folder in this repository contains various more complicated examples for Feynman integral computations using feyntrop with the Python interface. In this folder, you 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 with a low-level command-line interface without Python. This interface 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,
"seed" : 0
}
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, v1,w1, and so on are pairs of vertices corresponding to edges and nu0, nu1, ... are the corresponding edge weights.
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. Hence, the matrix must be symmetric and have as many rows and columns as 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 containing 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 "seed" is the seed for the random number generators. For most practical purposes, the seed can be set to 0.
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 parts of the respective coefficient in the expansion.
The other fields store the sampling and the preprocessing time.
If you are not interested in the logging information, use 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 behavior 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 integrating the input Feynman integral problem 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 two threads.