How to Unfold Top Decays
arXiv:2501.12363 · doi:10.21468/SciPostPhysCore.8.3.053
Abstract
Using unfolded top-quark decay data we can measure the top quark mass, as well as search for unexpected kinematic effects. We present a new generative unfolding method for the two tasks and show how they both benefit from unbinned, high-dimensional unfolding. Unlike weight-based or iterative generative methods we include a targeted unbiasing with respect to the training data. This shows significant advantages over standard, iterative methods, in terms of applicability, flexibility and accuracy.
References in corpus (54)
- The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
- An Introduction to PYTHIA 8.2
- DELPHES 3, A modular framework for fast simulation of a generic collider experiment
- Identifying Boosted Objects with N-subjettiness
- Extraction and validation of a new set of CMS PYTHIA8 tunes from underlying-event measurements
- Stop Reconstruction with Tagged Tops
- How to GAN LHC Events
- OmniFold: A Method to Simultaneously Unfold All Observables
- TUnfold: an algorithm for correcting migration effects in high energy physics
- Machine Learning and LHC Event Generation
- Invertible Networks or Partons to Detector and Back Again
- How to GAN away Detector Effects
- Unfolding with Generative Adversarial Networks
- Accelerating Monte Carlo event generation -- rejection sampling using neural network event-weight estimates
- Event Generators for High-Energy Physics Experiments
- GANplifying Event Samples
- Generative Networks for Precision Enthusiasts
- A factorisation-aware Matrix element emulator
- XCone: N-jettiness as an Exclusive Cone Jet Algorithm
- Measurement of jet-substructure observables in top quark, boson and light jet production in proton-proton collisions at TeV with the ATLAS detector
- MadNIS -- Neural Multi-Channel Importance Sampling
- Presenting Unbinned Differential Cross Section Results
- Learning to Simulate High Energy Particle Collisions from Unlabeled Data
- How to Understand Limitations of Generative Networks
- Modern Machine Learning for LHC Physicists
- Measurement of the jet mass distribution and top quark mass in hadronic decays of boosted top quarks in pp collisions at 13 TeV
- ν-Flows: Conditional Neutrino Regression
- The MadNIS Reloaded
- W+Jets at CDF: Evidence for Top Quarks
- Improving Generative Model-based Unfolding with Schrödinger Bridges
- Jet Diffusion versus JetGPT -- Modern Networks for the LHC
- Returning CP-Observables to The Frames They Belong
- End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physics
- Multidifferential study of identified charged hadron distributions in -tagged jets in proton-proton collisions at 13 TeV
- The Landscape of Unfolding with Machine Learning
- Measurement of the differential production cross section as a function of the jet mass and extraction of the top quark mass in hadronic decays of boosted top quarks
- Unbinned Deep Learning Jet Substructure Measurement in High ep collisions at HERA
- Scaffolding Simulations with Deep Learning for High-dimensional Deconvolution
- Precision-Machine Learning for the Matrix Element Method
- A simultaneous unbinned differential cross section measurement of twenty-four +jets kinematic observables with the ATLAS detector
- Differentiable MadNIS-Lite
- Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physics
- Loop Amplitudes from Precision Networks
- Full Event Particle-Level Unfolding with Variable-Length Latent Variational Diffusion
- Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inference
- Measurement of the primary Lund jet plane density in proton-proton collisions at = 13 TeV
- An unfolding method based on conditional Invertible Neural Networks (cINN) using iterative training
- Generative Unfolding with Distribution Mapping
- Kicking it Off(-shell) with Direct Diffusion
- Measurement of lepton-jet correlation in deep-inelastic scattering with the H1 detector using machine learning for unfolding
- Machine Learning-Assisted Measurement of Lepton-Jet Azimuthal Angular Asymmetries in Deep-Inelastic Scattering at HERA
- Unfolding algorithms and tests using RooUnfold
- Event-by-event Comparison between Machine-Learning- and Transfer-Matrix-based Unfolding Methods
- Measurement of the Lund jet plane in hadronic decays of top quarks and W bosons with the ATLAS detector