nnpdf / theories

Contains all ingredientes (grid, operator card, dataset definition) necessary to regenerate any theory using the pineko structure.

NNPDF: An open-source machine learning framework for global analyses of parton distributions

The NNPDF collaboration determines the structure of the proton using Machine Learning methods. This is the main repository of the fitting and analysis frameworks. In particular it contains all the necessary tools to reproduce the NNPDF4.0 PDF determinations.

Documentation

The documentation is available at https://docs.nnpdf.science/

Install

See the NNPDF installation guide for the conda package, and how to build from source.

Please note that the conda based workflow described in the documentation is the only supported one. While it may be possible to set up the code in different ways, we won't be able to provide any assistance.

We follow a rolling development model where the tip of the master branch is expected to be stable, tested and correct. For more information see our releases and compatibility policy.

Cite

This code is described in the following paper:

@article{NNPDF:2021uiq,
    author = "Ball, Richard D. and others",
    collaboration = "NNPDF",
    title = "{An open-source machine learning framework for global analyses of parton distributions}",
    eprint = "2109.02671",
    archivePrefix = "arXiv",
    primaryClass = "hep-ph",
    reportNumber = "Edinburgh 2021/13, Nikhef-2021-020, TIF-UNIMI-2021-12",
    doi = "10.1140/epjc/s10052-021-09747-9",
    journal = "Eur. Phys. J. C",
    volume = "81",
    number = "10",
    pages = "958",
    year = "2021"
}

If you use the code to produce new results in a scientific publication, please follow the Citation Policy, particularly in regards to the papers relevant for QCD NNLO and EW NLO calculations incorporated in the NNPDF dataset.

Contribute

We welcome bug reports or feature requests sent to the issue tracker. You may use the issue tracker for help and questions as well.

If you would like contribute to the code, please follow the Contribution Guidelines.

theories's Issues

FTDY instability issues

Let's collect here (or in an associated wiki page) the information about the situation of the FTDY grids in the MHOU fits (see here) I would like to know whether this is something that we can fix in vrap.

I've had a quick look at the following grid from theory 400 DYE866P.pineappl.lz4 (so it does include only NLO)

When doing pineappl convolute with NNPDF4.0 we get quite reasonable results for all datapoints (I'm not copying them here but the central values are all positive and the scale variations move in the 10-20% range). One example of this:

pineappl convolute DYE866P.pineappl.lz4 NNPDF40_nnlo_as_01180

124 12.35 12.35 0.748651 0.748651       1.2137391e-1   -17.59    22.61
125 13.85 13.85 0.679481 0.679481       6.7099958e-2   -18.69    24.38
126 15.85 15.85 0.604361 0.604361       2.8980554e-2   -20.07    26.64

However, if I now use 190310-tg-nlo-global with pineappl convolute, some points are very similar, while others become negative or receive very large scale corrections (the last three columns are cv, and -,+ scales in the 7-points prescription)

pineappl convolute DYE866P.pineappl.lz4 190310-tg-nlo-global

124 12.35 12.35 0.748651 0.748651       2.0278953e-2   -72.45   112.19
125 13.85 13.85 0.679481 0.679481      -4.8924728e-2     4.59    -4.58
126 15.85 15.85 0.604361 0.604361      -6.4598359e-2    17.67   -15.38

Note that this is with the NLO grid. With the NNLO grids the effect are less pronounced (but still there).

This is coming from negative points in the PDF. When I do convolute with 190310-tg-nlo-global with --force positive I find results compatible with NNPDF4.0. So @andreab1997 one possible solution (regardless of other cuts that might be implemented) would be to apply a "positive cutoff" in the computation of the MHOU. On one hand this is reasonable (a negative cross section is unphysical) but it might be a bit challenging to do while keeping the uncertainties perfectly gaussian.

pineappl convolute DYE866P.pineappl.lz4 190310-tg-nlo-global --force-positive

124 12.35 12.35 0.748651 0.748651       1.3515310e-1   -17.79    23.27
125 13.85 13.85 0.679481 0.679481       6.9460581e-2   -19.38    25.07
126 15.85 15.85 0.604361 0.604361       2.6455519e-2   -21.89    28.98

Put the actual grid in a more reasonable location

At the moment the heavy part of the theory, the grids, are stored in this repository.
This is a temporary solution. The grids should live somewhere else (in some external server) and instead the grids folder should be substituted by some kind of file that keeps track of grids and changes.

Something like:

- ATLASBLABLA: <name_in_remote_folder>.pineappl.lz4
- CMSBLUBLU: <name_in_remote_folder>.pineappl.lz4

The name_in_remote_folder should be an unique identifier (to first approximation it can be theory_dataset_date) so that if a fix is necessary for a given grid, both the fixed and broken grids can be kept in the server and the changes to the grid_meta.yaml file can be tracked in this repository.

@scarrazza can we use the INFN for this? We would need a lot of storage potentially.

Am I allowed to push to this repository?

I'd like to add the grid and the yamldb file for the EMC data set, for which FK tables are currently missing in theory 400. Am I allowed to clone (and push to) this repository?

Jet Pineappl grids

Hi @scarlehoff

I have a few jet theory grids that should be added to theories repo, however there are two new aspects to them:

1. they are at NNLO
2. they are obtained by conversion from fastnlo format.

Which folder should these be added to?

cc @enocera