Automatic differentiation for data analysis

[ramos]

Hi all,

I have been pointed out to this working group by a college. First some background: I am a researcher in Lattice QCD. I have found that the typical data analysis that we usually done is greatly simplified using automatic differentiation. The most relevant reference is:

Automatic differentiation for error analysis of Monte Carlo data
https://inspirehep.net/literature/1692759

The basic idea is to use AD to keep track of the derivatives of your data analysis results with respect to the input. This is al that is needed for error propagation, and turns out to be useful for sensitivity analysis (i.e. how much does my result depends of this parameter that I have fixed). A complete implementation of these ideas can be found in:

https://gitlab.ift.uam-csic.es/alberto/aderrors.jl

This package (in Julia) keeps track of the derivatives w.r.t the input in your codes. It is quite general and athough the main propose is error analysis in Lattice QCD, I am convinced that the ideas that are explained in the reference and implemented there might have many other applications. In my case it has changed how I do data analysis. An overview of the capabilities of the software (it only touches the basics) is available in a recent proceedings contribution:

Automatic differentiation for error analysis
https://inspirehep.net/literature/1837727

I would be happy to join this working group. I am also happy to give an overview of how we have been using these techniques for the past years, and of course, very interested to learn what you have been doing.

Thanks!

A.

[GilesStrong]

Hi @ramos ! Welcome, thanks for contacting us, and for the links.
Certainly AD for error propagation sounds very interesting, and relevant for HEP.
We have a channel on mattermost, where we generally discuss ideas, and occasionally organise meetings or presentations.
If you're interested, send me an email (giles.chatham.strong (@) cern.ch) and I'll reply with an invite link.