What is the machine learning?
Applications of machine learning tools to problems of physical interest are often criticized for producing sensitivity at the expense of transparency. To address this concern, we explore a data planing procedure for identifying combinations of variables—aided by physical intuition—that can discriminate signal from background. Weights are introduced to smooth away the features in a given variable(s). New networks are then trained on this modified data. Observed decreases in sensitivity diagnose the variable’s discriminating power. Planing also allows the investigation of the linear versus nonlinear nature of the boundaries between signal and background. We demonstrate the efficacy of this approach using a toy example, followed by an application to an idealized heavy resonance scenario at the Large Hadron Collider. By unpacking the information being utilized by these algorithms, this method puts in context what it means for a machine to learn.
- Research Organization:
- Univ. of Oregon, Eugene, OR (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), High Energy Physics (HEP)
- Grant/Contract Number:
- SC0011640; SC0018191
- OSTI ID:
- 1425995
- Alternate ID(s):
- OSTI ID: 1498658; OSTI ID: 1600035
- Journal Information:
- Physical Review D, Journal Name: Physical Review D Vol. 97 Journal Issue: 5; ISSN 2470-0010
- Publisher:
- American Physical SocietyCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
Similar Records
(Machine) learning to do more with less
Boosting $ H\to b\overline{b} $ with machine learning