NNPDF
ATLAS_WCHARM_13TEV
This pull request is for the implementation of the ATLAS_WCHARM_13TEV dataset
Dear @ecole41 , in view of the upcoming Morimondo meeting, I have revised the implementation of this data set. After a more careful look at the paper, I came to the conclusion that the best way of proceeding is to implement only two observables: one for the decay of a W in a D meson, and one for the decay of a W in a Dstar meson. In both cases, the observable incorporates a W- and a W+. The observable is the absolute distribution differential in the pseudorapidity of the lepton. This choice greatly simplifies the implementation, because we only need to take into account the full covariance matrix (Table 12 of the paper), and decompose it into the artificial systematic uncertainties. Because the full covariance matrix is not available for normalised distributions, we simply dismiss them. We dismiss the distributions differential in pT because they are theoretically trickier than the distributions in eta. Because W- and W+ are correlated, we have to incorporate the two in the same observable, for each decay mode. The two decay modes, as per the paper, are instead largely uncorrelated, except for the luminosity uncertainty. For this reason, I first compute the luminosity covariance matrix, and I subtract it from the full covariance matrix provided by the experimentalists before generating the artificial systematic uncertainties. I then attach the luminosity uncertainty to the list of uncertainties as usual. This way the luminosity uncertainty can be correlated across data sets and experiments as it should. This way the implementation is significantly more compact. The tests now pass. We can discuss further in Morimondo. Thanks.
@ecole41 could you generate also the (NLO) grids for this dataset?
I am happy to give it a go. I am new to making grids without the pine cards, do we have a similar dataset already to use a base?
Dear @ecole41 unfortunately we don't have an example for W+charm. We used MCFM to compute applgrids back in the days, but I don't think that this is of any help now. As @scarlehoff suggests, we may want to use madgraph to generate the NLO PineAPPL grids. These will then be supplemented with K-factors to incorporate the NNLO correction. In this respect, I point you out to this paper https://arxiv.org/pdf/2510.24525, in which they perform a comparison between NNLO predictions and the very same data set in this PR (see in particular Sect. 3). Maybe you've got a chance to talk to René Poncelet who sits at Cavendish and ask him if they can provide you with any useful input to compute theoretical predictions?