Ruprecht Karls Universität Heidelberg

Particle Phenomenology and Machine Learning

Greetings from Heidelberg, the most beautiful theoretical physics institute in Germany. Come and visit us at the villas to judge for yourself.

If I am not playing bass trombone in the Mannheim University Bigband, or Heidelberg's Galapagos bigband, I am interested in Higgs physics, QCD, and new physics at the LHC and elsewhere. Because I am not smart enough to just guess the true Theory of Everything, I approach all of those topics from a fairly technical point of view, including first-principle simulations and most recently all kinds of machine learning. You can find my publications here and here, including all my great ideas which turned out not to be realized in nature. You might notice that I am publishing almost all my papers in Scipost - that is because this journal is made by scientists for scientists, open access, cheap, and not Elsevier.

When I was young, I calculated higher-order cross sections in supersymmetry, working on a package called Prospino2. In those good old days I also worked on proper simulations of physics beyond the Standard Model with a Monte Carlo generator called Madgraph. Even though my enthusiasm for supersymmetry searches has taken hit after hit of LHC running, my group still takes pride in simulating things properly, even if it takes us and our computers some extra time. QCD is just too much fun to use short cuts.

During the early LHC years the Higgs boson has transitioned from the most exciting discovery to a handle to search for physics beyond the Standard Model. The way to search for such effects is through the extension of the Standard Model to an effective field theory, the Standard Model effective field theory. Coordinated with some field theory work we established a global Higgs and gauge boson fit through our SFitter tool - run by a French-German coalition of the difficult. There we have great fun with frequentist and Bayesian statistics applied to LHC data. Because we like crazy technical problems, our main goal is to get the uncertainties right, even those theory uncertainties which are not even defined properly. And since we are at that, we recycle all this technical know-how for global top-sector analyses, including higher-order predictions, or global dark matter analyses.

Talking about statistics, together with friends at NYU we developed a way to compute and understand where information about signal processes at the LHC actually come from; which regions of phase space we should study, and what we can expect from different production and decay channels. This application of information geometry is a great example how to apply proper maths to LHC physics without following of the usual path of formal physics. So if you are interested in learning what the Neyman-Pearson lemma or the Cramer-Rao bound have to do with the LHC, or if you want to know why physicists like to call a score an optimal observable, just drop me a mail.

A few years ago, I have started using machine learning on many aspects of LHC physics. This is not the usual multivariate analyses, but proper modern neural networks. It all started with the question how we can identify boosted top quarks using QCD jet algorithms, where our HEPTopTagger really made a difference. After a while we upgraded it to DeepTop, based on convolutional networks, and a completely new DeepTopLoLa setup. That network uses relativistic kinematic, and I am proud to say that we were able to measure the Minkowski metric with error bars - and to get that published. By now, these applications of machine learning in jet physics are standard at the LHC, just ask our local ATLAS groups.

As for most people, classification networks just marked my entrance to a huge technical playground. Looking at modern machine learning through the eyes of a technically minded LHC person feels like being a kid in Heidelberg's Zuckerladen. Anything you always wanted to do seems possible now, at least until you actually try it. Going back to my interest in error bars, I am a big fan of Bayesian networks and all the errors they can quantify for you. Or imagine what you can do with generative networks if you spent enough time in the Monte Carlo community and always wanted to find a better way to unweight events. And then your local machine learning friends tell you how to invert Monte Carlo simulations with neural networks, allowing you to unfold nastiness like detectors or QCD jet radiation. Sometimes such practical projects even allow us to learn something about the way certain neural networks work, and for a theorist such a projekt is of course especially exciting. And that is just the beginning of all the fun we will have with these new methods.

So, if you are interested in any of these things: call me, come by, catch me if you can. Of course I do not do all of this by myself. We have a great group of brilliant postdocs, outstanding graduate students, and amazing research students. We offer bachelor, master, and PhD thesis projects in all research directions. Just come and join us for a coffee or a thesis. The only thing we require is a nice set of lectures heard, indicating that your studies are driven more by the excitement for physics than a career perspective as a management consultant. This definitely includes having heard Quantum Field Theory I and II, Standard Model physics, and some of the advanced lectures and seminars we offer in particle phenomenology. If you started off as an experimentalist and you would like to switch to theory we will try hard to make this possible. Also, if you are a computer scientist and you suspect that theoretical particle physics might be much more fun, we will happily help you confirm this suspicion.

Finally, if you are interested how I ended up here, have a look at my short CV. Even though in our group we have seen some really nice careers unfold, please do yourself the favor and have a good plan B before trying your luck the same way...


Phone: +49 6221 54 9104
Fax: +49 6221 54 9333
Private: +49 176 62915975 (cell)

Institut für Theoretische Physik
Universität Heidelberg
Philosophenweg 16
D-69120 Heidelberg, Germany

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