Classification without labels: learning from mixed samples in high energy physics
Modern machine learning techniques can be used to construct powerful models for difficult collider physics problems. In many applications, however, these models are trained on imperfect simulations due to a lack of truth-level information in the data, which risks the model learning artifacts of the...
Main Authors: | Nachman, Benjamin (Author), Metodiev, Eric Mario (Contributor), Thaler, Jesse (Contributor) |
---|---|
Other Authors: | Massachusetts Institute of Technology. Center for Theoretical Physics (Contributor), Massachusetts Institute of Technology. Department of Physics (Contributor) |
Format: | Article |
Language: | English |
Published: |
Springer Berlin Heidelberg,
2017-12-08T23:23:08Z.
|
Subjects: | |
Online Access: | Get fulltext |
Similar Items
-
Classification without labels: learning from mixed samples in high energy physics
by: Nachman, Benjamin, et al.
Published: (2018) -
Classification without labels: learning from mixed samples in high energy physics
by: Eric M. Metodiev, et al.
Published: (2017-10-01) -
Learning to classify from impure samples with high-dimensional data
by: Nachman, Benjamin, et al.
Published: (2018) -
Energy flow networks: deep sets for particle jets
by: Thaler, Jesse, et al.
Published: (2019) -
Energy flow polynomials: a complete linear basis for jet substructure
by: Komiske, Patrick T., et al.
Published: (2018)