ML4Jets2022

Session

Measurement

3 Nov 2022, 09:00

202ABC (Rutgers University)

Conveners

Measurement

• Manuel Szewc
• Eva Halkiadakis (Rutgers State Univ. of New Jersey (US))

Measurement

• Oz Amram (Johns Hopkins University (US))
• Jesse Thaler (MIT)

3. Multi-differential Jet Substructure Measurement in High $Q^{2}$ Deep-Inelastic Scattering with the H1 Detector

Vinicius Massami Mikuni (Lawrence Berkeley National Lab. (US))

03/11/2022, 09:00

A study of different jet observables in high $Q^{2}$ Deep-Inelastic Scattering events close to the Born kinematics is presented. Differential and multi-differential cross-sections are presented as a function of the jet’s charged constituent multiplicity, momentum dispersion, jet charge, as well as three values of jet angularities. Results are split into multiple $Q^{2}$ intervals, probing the...

54. Machine learning for top physics in CMS

Philip Daniel Keicher (Hamburg University (DE))

03/11/2022, 09:20

Machine learning (ML) plays a significant role in the physics analyses at the CMS experiment. Many different techniques and strategies have been deployed to a wide range of applications. In this presentation we will illustrate the most advanced techniques used in top quark physics measurements, such as using ML algorithms to improve the extraction of effective field theory contributions, and...

73. ML Unfolding based on conditional Invertible Neural Networks using iterative training

Mathias Josef Backes (Universität Heidelberg)

03/11/2022, 09:40

The unfolding of detector effects impacting experimental measurements is crucial for the comparison of data to theory predictions. While traditional methods were limited to low dimensional data, machine learning has enabled new tech- niques to unfold high-dimensional data. Generative networks like conditional Invertible Neural Networks (cINN) enable a probabilistic unfolding, which map...

9. Moment Unfolding using Deep Learning

Krish Desai

03/11/2022, 10:00

Deconvolving ('unfolding') detector distortions is a critical step in the comparison of cross section measurements with theoretical predictions. However, most of these approaches require binning while many predictions are at the level of moments. We develop a new approach to directly unfold distribution moments as a function of any other observables without having to first discretize. Our...

16. Invertible Networks for the Matrix Element Method

Theo Heimel (Heidelberg University)

03/11/2022, 10:20

The matrix element method is widely considered the perfect approach to LHC inference, but computationally expensive. We show how a combination of two conditional Invertible Neural Networks can be used to learn the transfer function between parton level and reconstructed objects, and to make integrating out the partonic phase space numerically tractable. We illustrate our approach for the...

46. Constraining quark and gluon jet energy loss distributions in quark-gluon plasma using Bayesian inference

Alexandre Falcão (University of Bergen)

03/11/2022, 11:10

Zoom

QCD factorization allows us to model the jet energy-loss in A-A collisions as a convolution between the jet cross section in p-p collisions and an energy loss distribution. Meanwhile, Bayesian inference provides a data-driven way of constraining the energy loss distribution parameterization. Only a few efforts have been made in this direction, and solely using untagged jets. However, gluon and...

13. Estimating Uncertainties for Trained Neural Networks

Sebastian Guido Bieringer (Hamburg University)

03/11/2022, 11:30

Zoom

Uncertainty estimation is a crucial issue when considering the application of deep neural network to problems in high energy physics such as jet energy calibrations.

We introduce and benchmark a novel algorithm that quantifies uncertainties by Monte Carlo sampling from the models Gibbs posterior distribution. Unlike the established 'Bayes By Backpropagation' training regime, it does not...

95. How can Bayesian networks be used for uncertainty quantification in particle physics?

Christina Peters (University of Delaware)

03/11/2022, 11:50

Uncertainty quantification is crucial for data analysis and hypothesis testing. Many machine learning algorithms were not designed to provide information about the reliability of their predictions, and the methods for estimating uncertainties from these algorithms can lack transparency. In this talk we demonstrate the Bayesian network framework, which was developed using a rigorous formalism...

69. Using Machine Learning to Improve our Understanding of the Jet Background in Nucleus-Nucleus Collisions.

Tanner Mengel (University of Tennessee)

03/11/2022, 12:10

Jets in heavy ion collisions contain contributions from a background of soft-particles. The kinematic reach into low jet momentum is largely driven by the precision of the method used to subtract this background. This precision is also a significant contribution to uncertainties of jet measurements. Previous studies have suggested that deep neural networks can improve momentum resolution at...

82. Loop Amplitudes from Precision Networks

Tilman Plehn

03/11/2022, 12:30

Evaluating loop amplitudes is a time-consuming part of LHC event generation. I will shown for di-photon production with jets how simple, Bayesian networks can learn such amplitudes and model their uncertainties reliably. A boosted training of the Bayesian network further improves the uncertainty estimate and the network precision in critical phase space regions. In general, boosted network...