Machine learning in the search for new fundamental physics
Compelling experimental evidence suggests the existence of new physics beyond the well-
established and tested standard model of particle physics. Various current and upcoming …
established and tested standard model of particle physics. Various current and upcoming …
Toward the end-to-end optimization of particle physics instruments with differentiable programming
T Dorigo, A Giammanco, P Vischia, M Aehle, M Bawaj… - Reviews in Physics, 2023 - Elsevier
The full optimization of the design and operation of instruments whose functioning relies on
the interaction of radiation with matter is a super-human task, due to the large dimensionality …
the interaction of radiation with matter is a super-human task, due to the large dimensionality …
Anomaly detection with density estimation
B Nachman, D Shih - Physical Review D, 2020 - APS
We leverage recent breakthroughs in neural density estimation to propose a new
unsupervised ANOmaly detection with Density Estimation (ANODE) technique. By …
unsupervised ANOmaly detection with Density Estimation (ANODE) technique. By …
End-to-end latent variational diffusion models for inverse problems in high energy physics
A Shmakov, K Greif, M Fenton… - Advances in …, 2024 - proceedings.neurips.cc
High-energy collisions at the Large Hadron Collider (LHC) provide valuable insights into
open questions in particle physics. However, detector effects must be corrected before …
open questions in particle physics. However, detector effects must be corrected before …
MLPF: efficient machine-learned particle-flow reconstruction using graph neural networks
In general-purpose particle detectors, the particle-flow algorithm may be used to reconstruct
a comprehensive particle-level view of the event by combining information from the …
a comprehensive particle-level view of the event by combining information from the …
Measurement of lepton-jet correlation in deep-inelastic scattering with the H1 detector using machine learning for unfolding
V Andreev, M Arratia, A Baghdasaryan, A Baty… - Physical review …, 2022 - APS
The first measurement of lepton-jet momentum imbalance and azimuthal correlation in
lepton-proton scattering at high momentum transfer is presented. These data, taken with the …
lepton-proton scattering at high momentum transfer is presented. These data, taken with the …
Getting high: High fidelity simulation of high granularity calorimeters with high speed
E Buhmann, S Diefenbacher, E Eren, F Gaede… - Computing and Software …, 2021 - Springer
Accurate simulation of physical processes is crucial for the success of modern particle
physics. However, simulating the development and interaction of particle showers with …
physics. However, simulating the development and interaction of particle showers with …
Precision-machine learning for the matrix element method
The matrix element method is the LHC inference method of choice for limited statistics. We
present a dedicated machine learning framework, based on efficient phase-space …
present a dedicated machine learning framework, based on efficient phase-space …
Improving generative model-based unfolding with Schrödinger bridges
Machine learning-based unfolding has enabled unbinned and high-dimensional differential
cross section measurements. Two main approaches have emerged in this research area; …
cross section measurements. Two main approaches have emerged in this research area; …
Invertible networks or partons to detector and back again
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 …
invertible neural networks are especially useful. A conditional INN can invert a detector …