mlsanityData to 7 Oct 2026

nu-Flows: Conditional Neutrino Regression

arXiv 2207.00664 ↗First posted 1 Jul 2022hep-ph cs.LG hep-excited by 11

Not on the map. Why?

Abstract (arXiv)

We present nu-Flows, a novel method for restricting the likelihood space of neutrino kinematics in high energy collider experiments using conditional normalizing flows and deep invertible neural networks. This method allows the recovery of the full neutrino momentum which is usually left as a free parameter and permits one to sample neutrino values under a learned conditional likelihood given event observations. We demonstrate the success of nu-Flows in a case study by applying it to simulated semileptonic tbart events and show that it can lead to more accurate momentum reconstruction, particularly of the longitudinal coordinate. We also show that this has direct benefits in a downstream task of jet association, leading to an improvement of up to a factor of 1.41 compared to conventional methods.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 16 of its 71 references that are in the map. First posted on arXiv 1 Jul 2022.

Builds on

16 of 71 references.

References · 16

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2016Layer Normalizationcited by 5,579
  4. 2015Fast R-CNNcited by 2,930
  5. 2015Variational Inference with Normalizing Flowscited by 2,161
  6. 2018Glow: Generative Flow with Invertible 1x1 Convolutionscited by 1,539
  7. 2017Deep Setscited by 1,394
  8. 2019Neural Spline Flowscited by 590
  9. 2019Guided Image Generation with Conditional Invertible Neural Networkscited by 147
  10. 2020Flows for simultaneous manifold learning and density estimationcited by 117
  11. 2020Anomaly Detection with Density Estimationcited by 71
  12. 2021CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flowscited by 46
4 more
  1. 2021CaloFlow II: Even Faster and Still Accurate Generation of Calorimeter Showers with Normalizing Flowscited by 34
  2. 2021Tractable Density Estimation on Learned Manifolds with Conformal Embedding Flowscited by 30
  3. 2020Permutationless Many-Jet Event Reconstruction with Symmetry Preserving Attention Networkscited by 22
  4. 2021SPANet: Generalized Permutationless Set Assignment for Particle Physics using Symmetry Preserving Attentioncited by 18

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 3; 2024: 4; 2025: 1; 2026: 2.

11 papers.

  1. 2024The Landscape of Unfolding with Machine Learningcited by 20
  2. 2022CaloFlow for CaloChallenge Dataset 1cited by 14
  3. 2023nu2-Flows: Fast and improved neutrino reconstruction in multi-neutrino final states with conditional normalizing flowscited by 10
  4. 2023Calorimeter shower superresolutioncited by 7
  5. 2023Reconstruction of Unstable Heavy Particles Using Deep Symmetry-Preserving Attention Networkscited by 7
  6. 2024Unifying Simulation and Inference with Normalizing Flowscited by 6
  7. 2025Simulation-Prior Independent Neural Unfolding Procedurecited by 4
  8. 2024Extrapolating Jet Radiation with Autoregressive Transformerscited by 2
  9. 2024Variational inference for pile-up removal at hadron colliders with diffusion modelscited by 1
  10. 2026CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeterscited by 1
  11. 2026Comprehensive reconstruction of collider events with hypergraph representation learning and graph-conditioned diffusioncited by 0