Plots as a function of <Npart>
Hi everyone,
I have currently an intern I put to write the analysis STAR_2019_I1724809
(https://inspirehep.net/literature/1724809) about cumulants of net-particle multiplicities. As some plots we're interested in depends on the mean participant numbers (<Npart>), we face the problem already known about data provided without bin width (cited for instance in #142 (closed)).
I have discussed how to proceed about this issue with Christine Nattrass, who supervised a lot of analyses writing already on HICs and proposed me to redefine data as a function of centrality.
This is of course a nice solution which works technically well, however I'm still not really satisfied with this. Hence, I'd like to know if we can go further, with 2 different approaches :
- can we plot both experimental data (with no bin-width) and the results of our analyses as a function of Npart on the same plot (without booking our histo on the data) ? And if yes, how ? (this point is only for personal purpose, I know it's not a good idea to propose an analysis as a function of Npart directly as it can be too much model dependent)
- is there any way to change the scale of an axis by "convoluting" plots ? By this, I mean for instance in my case storing the cumulants as a function of centrality first, then estimating the <Npart> value for each centrality bin to finally plot the cumulants as a function of those <Npart> values (which could be then compared to the data available on HepData without modification). (this alternative, if doable, could be used in the publicly available analysis in RIVET, contrary to the 1st one)
The main argument behind this desire of plotting as a function of Npart is that, even though doing the analyses and plotting as a function of centrality works, we "lose" in some way a little bit of the physical interpretation of the results. Still in the case of this paper for instance, one expects a linear increase of the cumulants as a function of Npart, what is clearly not visible at first sight when plotting as a function of centrality.
I know it's not an easy thing to handle, and maybe there has been discussions already about this, but if someone could help us at least with the 1st point, it would be nice