ML4Jets2021

Session

ML-Assisted Measurements and Searches

6 Jul 2021, 14:00

Conveners

ML-Assisted Measurements and Searches

• Vinicius Massami Mikuni (Universitaet Zuerich (CH))
• Matthew Schwartz

95. Invertible Neural Networks beyond Particle Physics

Lynton Ardizzone (Heidelberg)

06/07/2021, 14:00

Invertible Neural Networks (INNs) are an extremely versatile class of generative models. Their invertibility allows for exact modelling of proability densities, computation of information-theoretic quanities, interpretable and disentangled features, among other things. Due to these properties, INNs have seen growing adoption in recent years, especially in natural sciences and engineering...

71. Combining Neural Network predictions with Hypothesis Testing for discovery in the LHC

Michael Soughton (University of Sussex)

06/07/2021, 14:40

As the use of Machine Learning techniques become more widespread within High Energy Physics it is important to consider how the results from Neural Networks can be applied within hypothesis testing. We show how a Log-Likelihood Ratio test can be performed using the the output of Neural Network classifiers trained on different physical datasets to yield a detection significance between two...

30. Invertible Networks or Partons to Detector and Back Again

Anja Butter

06/07/2021, 15:00

For simulations where the forward and the inverse directions have a physics meaning, invertible neural networks are especially useful. A conditional INN can invert a detector simulation in terms of high-level observables, specifically for ZW production at the LHC. It allows for a per-event statistical interpretation. Next, we allow for a variable number of QCD jets. We unfold detector effects...

64. Towards a new generation of PDFs using ML

Roy Stegeman (University of Milan)

06/07/2021, 15:20

We present the machine learning methodology that is the backbone of the new release of the NNPDF family of parton distribution functions. The new methodology introduces state of the art machine learning techniques such as stochastic gradient descent for neural network training which results in a major reduction in computational costs, and an automated optimization of the hyperparameters which...

45. Emerging techniques for sampling, searching, and summing over the combinatorially large space of shower histories

Sebastian Macaluso (New York University)

06/07/2021, 15:40

A central challenge in jet physics is that the evolution of the jet is an unobserved, latent process. In a semi-classical parton shower, this corresponds to a sequence of 1-to-2 splittings that form a tree-like showering history. Framing jet physics in probabilistic terms is attractive as it provides a principled framework to think about tasks as diverse as clustering, classification, parton...

44. Tuning the parton shower parameters with the marginal likelihood

Matthew Drnevich (New York University (US))

06/07/2021, 16:00

Tuning parton shower models to data is an important task for HEP experiments. We are performing exploratory research for what tuning the parton shower might look like if the parton shower were described by a generative model with a tractable likelihood, which might be implemented with a hybrid of theoretically-motivated components or generic neural network components. For this work we consider...

42. Computing the exact optimal classifier for Ginkgo jets

Lauren Greenspan (NYU)

06/07/2021, 16:20

In the last several years, the ML4Jets community has worked to improve performance for jet tagging and performed a number of comparisons of different architectures for jet tagging and other tasks. We have seen that combining multiple classifiers together into a meta-tagger or an ensemble improves performance. But is there still room for improvement? In other words, are we approaching the...

77. Parametrized classifiers for optimal EFT sensitivity

Alfredo Glioti (EPFL - Ecole Polytechnique Federale Lausanne (CH))

06/07/2021, 16:40

We study unbinned multivariate analysis techniques, based on Statistical Learning, for indirect new physics searches at the LHC in the Effective Field Theory framework. We focus in particular on high-energy ZW production with fully leptonic decays, modeled at different degrees of refinement up to NLO in QCD. We show that a considerable gain in sensitivity is possible compared with current...

12. Measuring QCD Splittings with Invertible Networks

Theo Heimel (Universität Heidelberg)

06/07/2021, 17:00

QCD splittings are among the most fundamental theory concepts at the LHC. In this talk, I will show how they can be studied systematically with the help of invertible neural networks. These networks work with sub-jet information to extract fundamental parameters from jet samples. Our approach expands the LEP measurements of QCD Casimirs to a systematic test of QCD properties based on low-level...

59. Jet-based TMD measurements with H1 data, unfolded using machine-learning techniques

Miguel Ignacio Arratia Munoz (Lawrence Berkeley National Lab. (US)), Miguel Ignacio Arratia Munoz

06/07/2021, 17:20

Recently, jet measurements in DIS events close to Born kinematics have been proposed as a new probe to study transverse-momentum-dependent (TMD) PDFs, TMD fragmentation functions, and TMD evolution. We report measurements of lepton-jet momentum imbalance and hadron-in-jet correlations in high-$Q^2$ DIS events collected with the H1 detector at HERA. The jets are reconstructed with the kT...

17. Parameter Inference from Event Ensembles and the Top-Quark Mass

Katherine Fraser (Harvard University)

06/07/2021, 17:40

Measurements at colliders are often done by fitting data to simulations, which depend on many physical and unphysical parameters. One example is the top-quark mass, where parameters in simulation must be profiled when fitting the top-quark mass parameter. In particular, the dependence of top-quark mass fits on simulation parameters contributes to the error in the best measurements of the...