Add PDF4LHC_DY_13_TEV_21_PHENO grids

[Radonirinaunimi]

Outputs from results.log:

-------------------------------------------------------------------------
bin   PineAPPL         MC        sigma      central         min      max
                                 1/100   sigma   1/1000   1/1000   1/1000
-------------------------------------------------------------------------
0  4.740330e-03  4.740069e-03   0.010   0.548   0.0549   0.0567   0.0537
1  6.592477e-04  6.592024e-04   0.027   0.250   0.0688   0.0729   0.0656
2  1.581839e-04  1.581659e-04   0.056   0.203   0.1141   0.1353   0.0930
3  9.776636e-06  9.775622e-06   0.155   0.067   0.1037   0.1163   0.0898
4  7.040504e-07  7.043237e-07   0.424   0.092   0.3880   0.3794   0.4058
5  1.083594e-08  1.090689e-08   8.966   0.073   6.5051   7.7141   5.5307

Results with NNPDF40_nnlo_as_01180:

bin    Mll      dsig/dMll   neg unc pos unc
---+----+----+-------------+-------+-------
  0 1000 1500  9.0281902e-6  -1.77%   1.78%
  1 1500 2000  1.2412009e-6  -2.23%   2.01%
  2 2000 3000  1.4796270e-7  -2.67%   2.29%
  3 3000 4000  9.2653979e-9  -3.29%   2.65%
  4 4000 5000 8.0103363e-10  -3.67%   2.78%
  5 5000 7000 5.2674888e-11  -3.99%   2.95%

Results with CT18NNLO:

bin    Mll      dsig/dMll   neg unc pos unc
---+----+----+-------------+-------+-------
  0 1000 1500  8.8532049e-6  -1.70%   1.74%
  1 1500 2000  1.2175299e-6  -2.15%   1.97%
  2 2000 3000  1.4548129e-7  -2.59%   2.20%
  3 3000 4000  9.1704620e-9  -3.23%   2.53%
  4 4000 5000 7.4450818e-10  -3.86%   2.89%
  5 5000 7000 3.4232552e-11  -4.76%   3.66%

Results with MSHT20nnlo_as118:

bin    Mll      dsig/dMll   neg unc pos unc
---+----+----+-------------+-------+-------
  0 1000 1500  9.0237314e-6  -1.70%   1.74%
  1 1500 2000  1.2611906e-6  -2.16%   1.98%
  2 2000 3000  1.5315468e-7  -2.62%   2.23%
  3 3000 4000  9.8787330e-9  -3.26%   2.56%
  4 4000 5000 8.2269736e-10  -3.87%   2.90%
  5 5000 7000 3.9245615e-11  -4.73%   3.61%

Pheno Pots:

[cschwan]

@Radonirinaunimi would you please also upload the result tables that contain the MC uncertainties?

[Radonirinaunimi]

@Radonirinaunimi would you please also upload the result tables that contain the MC uncertainties?

You are referring to the table I added above, right? I will also add the pheno plots later on.

[cschwan]

@Radonirinaunimi No, the tables above give the scale uncertainties, but we also need the MC uncertainties. They are given in the results.log file.

[Radonirinaunimi]

@cschwan Sorry, I should have pointed out that I edited the above, which included the outpus from results.log.

[cschwan]

@Radonirinaunimi Oh, in that case I'm sorry - thanks!

[Radonirinaunimi]

@Radonirinaunimi Oh, in that case I'm sorry - thanks!

No, it was my fault! I could have been more succinct.

[cschwan]

[..] I will also add the pheno plots later on.

To upload them to github, you should generate PNGs. Simply change the .pdf to .png in the matplotlib script containing the following line:

figure.savefig(name + '.pdf')

[Radonirinaunimi]

Plots added (in the edited version)!

[cschwan]

@Radonirinaunimi note that we're using Git LFS in this repository, the last commit converted the binary to it. You'll need to install LFS, otherwise you're going to see just a text file instead of the PineAPPL grid. I'll update the README soon.

[Radonirinaunimi]

@cschwan, I will do so. I thought this was already installed when I was installing the runcard rr script, but apparently note. Thanks for pointing this out.