Machine Learning for the LHC (under construction)
LHC physics, like many other field in and around particle physics is living through an exciting, data-driven era. As theorists we can come up with ideas for physics beyond the Standard Model, motivated by dark matter or the matter-antimatter asymmetry or whatever else keeps us up at night, and immediately try it on LHC data. Even if we do not have access to the actual data we can devise search strategies using a simulation chain based on first principles and then hand our ideas and tools over to our experimental friends. The scientific challenge is that we have to understand the basic structure of LHC data, its (precision) simulation, and the best ways of using huge amounts of data. This immediately brings us to machine learning as a great tool for particle physicists. In our group we develop and apply modern data analysis tools in four ways, where this number should be increasing. Here is list of papers we published on machine learning applications:
Jet and Event Classification
Multi-variate analysis methods have a long tradition in particle physics. If we use sub-jet physics as an example, we want to ask the question what kind of quark or gluon initiated an observed jet. Traditionally, this question is asked in relation to bottom quarks, which can be identified by a displaced decay vertex and one or more leptons inside the hadronic jets. From a theory and experimental perspective the cleanest signature are boosted top quarks. The original HEPTopTagger approach asks for two mass drops, one from the top decay and one from the W-decay. We know that this tagger can be hugely improved when we include a wealth of kinematic observables, some directly and some algorithmically derived from the energies and the momenta of the subjets. The obvious question is why we need to construct these high-level observables and if we cannot just feed the 4-momenta of the subjets into a classification algorithm. As a matter of fact, we can do exactly that. This work has been done with Sven Bollweg, Anja Butter, Sascha Diefenbacher, Hermann Frost, Manuel Haussmann, Theo Heimel, Gregor Kasieczka, Nicholas Kiefer, Michel Luchmann, Michael Russell, Torben Schell, and Jennie Thompson, number still growing.
Measurement of jet momenta with uncertainties and calibration of regression tools with Bayesian networks (2020). The measurement by the regression network tracks statistical and systematic uncertainties from the training data. We propose to calibrate the network in a straightforward way through the smearing introduced by the measurement of labels.
Jet and event classification with capsule networks (2019). Such capsule networks are a natural extension of (scalar) convolutional networks and can be used to analyse sparse sets of detector objects, each represented by a sparse calorimeter image.
Top tagging with uncertainties using Bayesian classification networks (2019). Such a tagger provides a classification output and a jet-wise error estimate on the classification outcome. While statistical uncertainties from a limited training sample are easily traced, systematic uncertainties lead to a correlation of the central value and the uncertainties, all the way to adversarial examples.
Top-tagging community paper (2019) comparing a wide range of top taggers, from image-based to 4-vector-based and theory-motivated tagging approaches. We show their respective performances for a standard data set and find that there are many ways of contructing highly performing taggers ready to hit LHC data.
LoLa-based quark-gluon tagger (2018) showing that the same 4-vector-based architecture can be used to distinguish hard processes with preferably quark or gluon jets. The issue is how to design a high-performance tagger in the presence of detector effects.
Jet autoencoder (2018) based on the DeepTop and LoLa taggers. We show howtop jets are less compressible than QCD jets, how they can be tagged, and how we can de-correlate the jet mass using an adversary. We also test the autoencoder on new physics in jets.
4-vector-based LoLa tagger (2017) which allows us to combine information from the calorimeter and the tracker, accounting for the different resolutions. It can be thought of a graph network over Minkowski space, where we use the fact that we know the Minkowski metric. For a cross check this is one of the few papers which quotes the Minkowski metric with an error bar.
Image-based DeepTop tagger (2017) showing that we can use convolutional networks on calorimeter images to identify boosted tops. We showed how this deep-learning tagger compares to classic multi-variate methods and how it is possible to interpret the intermediate layers and the output of the tagging network.
First-principle simulations LHC events based on quantum field theory (mostly QCD) is one of the unique features of LHC physics. Many groups are investing a huge effort into computing the underlying predictions in perturbative quantum field theory. These prediction can be included in multi-purpose event generators like Pythia, Sherpa, Madgraph, or Herwig. These simulations are based on Monte Carlo simulations and are extremely efficient. The question is if we can use machine learning tools to improve them further or to get access to information that is usually lost in the simulation framework. One example is the use of the hard matrix element in a hypothesis test, usually called the matrix element method. Our group includes Marco Bellagente, Anja Butter, Sascha Diefenbacher, Gregor Kasieczka, Armand Rousselot, and Ramon Winterhalder, and it is still expanding.
Generative networks review article (2020). We given an overview of the many ways generative networks are developed for LHC event generation. This includes many different architectures and applications.
GAN study on the statistical gain (2020). An open question, crucial to LHC applications, is how much we can gain beyond the statistical power of the training sample by training a generatve network for more events. We define an amplification factor for simple multi-dimensional toy distributions and first find that, just like a fit, the structure of the GAN adds information to the discrete set of training events. Second, the GANned events do not have the same individual statistical power as a sampled event. The GAN applification factor becomes larger for sparcely distributed training data in high-dimensional phase spaces.
Conditional INN application to detector and QCD unfolding (2020). Invertible networks based on normalizing flows and built out of coupling layers can also serve as generative networks for LHC events. Their greatest advantage is that in a conditional setup they are built to generate spread-out probability distributions in the target space. For LHC unfolding this means we can construct a probability distribution over parton-level phase space for a single detector-level event. We expand the detector unfolding to also unfold jet radiation to a pre-defined hard process.
Fully conditional GAN application to detector unfolding (2019). If we train a conditional GAN on matched event samples we can use it to invert a Monte-Carlo-based simulation of, for instance, detector effects. This unfolding is not limited to one- or low-dimensional distribution, but covers the entire phase space. The matching of local structures in the two data sets reduces the model dependence of the unfolding procedure.
GAN application to event subtraction (2019). A general problem in dealing with event samples is that there is no efficient way to subtract event sample from each other, or combine events with positive and negative weights. We show how GANs can do that avoid statistical limitations from the usual binning procedure. Applications could be subtraction terms in Monte Carlo simulations or background subtraction in analyses.
GAN application to event generation (2019). We show that a GAN, supplemented with a dedicated MMD loss, can generate top pair production events at the LHC. We model all kinematic distributions all the way to the three top decay jets. We find that regions with large systematic uncertainties on the GAN are directly linked to sparse training data.
Information geometry is not exactly machine learning, but it is a concept which benefits from machine learning when we apply it to LHC physics. The question we are trying to answer is what kind of information is available to an LHC analysis, what observables capture it best, and what the limiting factors in an analysis might be. The machine learning aspect only comes in once we ask these questions beyond the parton level, and two former Heidelberg students (Johann Brehmer and Felix Kling) have worked with Kyle Cranmer's NYU group on developing the corresponding MadMiner program. Other collaborators on this topic include Sally Dawson and Sam Homiller.
Validation of simplfied template cross sections for VH production (2019). We analyse how these observables compare to an analysis of the full phase space, including detector effects and mixxing transverse momentum. This is where machine learning enters.
Information geometry of Higgs CP in the SMEFT framework (2017). We compare different Higgs production and decay signatures, all based on the amplitude with four additional fermions, in their potential to test the CP properties of the Higgs coupling to intermediate gauge bosons. We link this approach to the established optimal observables.
Information geometry of Higgs signatures in the SMEFT framework (2016). We compute the information available over the entire partonic phase space of Higgs signatures and compare for instance the impact of the QBF tagging jet kinematics with the Higgs decay kinematics.
Global SFitter analyses
Obviously, machine learning should be able to improve global analyses of LHC data, where we interpret a large number of measurements in a high-dimensional parameter space. Our SFitter tool has serves us well in the last 10 years, so we are working on improving it with machine learning applications...