Nanga Parbat is a fitting framework aimed at the determination of the non-perturbative component of TMD distributions.
You can obtain NangaParbat directly from the github repository:
https://github.com/MapCollaboration/NangaParbat
For the last development branch you can clone the master code:
git clone [email protected]:MapCollaboration/NangaParbat.gitIf you instead want to download a specific tag:
git tag -l
git checkout tags/tag_nameIn order to install the code a number of external but relatively standard libraries are required. Here is the list:
Most of these libraries can be installed through standard package managers such as Homebrew on MacOS and apt-get on Linux.
NangaParbat also has a lite version, that does not require all the dependencies listed above. The mandatory dependencies are yaml-cpp, eigen3, LHAPDF6, APFEL++.
During the installation, NangaParbat detects if one or more libraries among GSL, ROOT or ceres-solver are missing and installs and compiles only the part of the code that is possible to run.
The code can be compiled using the following procedure:
cd NangaParbat
mkdir build && cd build
cmake -DCMAKE_INSTALL_PREFIX=/your/installation/path/ ..
make && make installBy the default, if no prefix specification is given, the program will be installed in the /usr/local folder. If you want (or need) to use a different path, remember to export the NangaParbat /lib folder into the LD_LIBRARY_PATH. More configuration options can be accessed by typing:
ccmake .Despite the core of the code is written in C++, the main functionalities can be accessed through the command-line-interface utilities. If the installation was successful, it is possible to run a fit of TMD PDFs just by typing from the main folder:
python3 cli/fit.pyand following the instructions. Any such fit relies on the dataset, the interpolation tables, and the TMD parameterisations currently present in the code. It is however possible to generate new interpolation tables using the cli/tables.py utility that gives the possibility change the theory settings (perturbative order, collinear PDF set, etc.). Including more parameterisations and new experimental datasets is also possible but requires some additional work on the core of the code. Feel free to contact us should you want to extend the code in this respect.
Once the fit is complete (including a number of Monte Carlo replicas), it is possible to create a summary report of the fit by running cli/report.py. This will create a document (in markdown and html format) that collects the main statistical features of the fit, histograms, data-theory comparison plots, and TMD plots.
Some further general documentation can be found at the following links:
Code documentation generated with Doxygen can be found here.
The reports linked below have been generated using the CLI cli/report.py.
Fit of the TMD PDFs of the proton to Drell-Yan data as published in [JHEP 07 (2020) 117]:
Simultaneous fit of the TMD PDFs of the proton and of the TMD FFs of ligh hadrons to Drell-Yan and SIDIS data as published in [JHEP 10 (2022) 127]:
- fit at N3LL.
Fit of the TMD PDFs of the pion to Drell-Yan data as published in [Phys.Rev.D 107 (2023) 1, 014014]:
- fit at N3LL.
TMD and structure functions grids compatible with the NangaParbat specifications are made available through the TMDlib library. Grids of the TMD PDFs and FFs, and the SIDIS F_UUT structure functions from the PV17 fit can also be found here.
In tools/ there are some test codes that can be run by the user in
order to interpolate the TMD and structure function grids provided at
the link above. This part of NangaParbat is compiled also in the lite
version.
The interpolation of the grids can be done with
TMDGridInterpolation.cc for TMDs and with
StructGridInterpolation.cc for structure functions, while
GridsConvolution.cc does the convolution between a TMD PDF grid
and a TMD FF grid. The input files, where the user can choose the
kinematical points for the interpolation and the convolution, are in
tools/inputs/.
- Extraction of Pion Transverse Momentum Distributions from Drell-Yan data; Matteo Cerutti, Lorenzo Rossi, Simone Venturini, Alessandro Bacchetta, Valerio Bertone, Chiara Bissolotti, and Marco Radici [Phys.Rev.D 107 (2023) 1, 014014].
- Unpolarized transverse momentum distributions from a global fit of Drell-Yan and semi-inclusive deep-inelastic scattering data; Alessandro Bacchetta, Valerio Bertone, Chiara Bissolotti, Giuseppe Bozzi, Matteo Cerutti, Fulvio Piacenza, Marco Radici, Andrea Signori [JHEP 10 (2022) 127].
- Transverse-momentum-dependent parton distributions up to N3LL from Drell-Yan data; Alessandro Bacchetta, Valerio Bertone, Chiara Bissolotti, Giuseppe Bozzi, Filippo Delcarro, Fulvio Piacenza, Marco Radici [JHEP 07 (2020) 117].
- Valerio Bertone: [email protected]
- Alessandro Bacchetta: [email protected]
- Chiara Bissolotti: [email protected]
- Matteo Cerutti: [email protected]
- Lorenzo Rossi: [email protected]
NangaParbat has been developed with the support of the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement No. 647981, 3DSPIN).
