MadMiner: Machine learning-based inference for particle physics - INSPIRE

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MadMiner: Machine learning-based inference for particle physics

Johann Brehmer
(
- New York U., CCPP
)
,
Felix Kling
(
- UC, Irvine and
- SLAC
)
,
Irina Espejo
(
- New York U., CCPP
)
,
Kyle Cranmer
(
- New York U., CCPP
)

Jul 24, 2019

35 pages

Published in:

Comput.Softw.Big Sci. 4 (2020) 1, 3

Published: Jan 18, 2020

e-Print:

1907.10621 [hep-ph]

DOI:

10.1007/s41781-020-0035-2

View in:

ADS Abstract Service

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Abstract: (Springer)

Precision measurements at the LHC often require analyzing high-dimensional event data for subtle kinematic signatures, which is challenging for established analysis methods. Recently, a powerful family of multivariate inference techniques that leverage both matrix element information and machine learning has been developed. This approach neither requires the reduction of high-dimensional data to summary statistics nor any simplifications to the underlying physics or detector response. In this paper, we introduce MadMiner , a Python module that streamlines the steps involved in this procedure. Wrapping around MadGraph5_aMC and Pythia 8, it supports almost any physics process and model. To aid phenomenological studies, the tool also wraps around Delphes 3, though it is extendable to a full Geant4-based detector simulation. We demonstrate the use of MadMiner in an example analysis of dimension-six operators in ttH production, finding that the new techniques substantially increase the sensitivity to new physics.

Note:

MadMiner is available at https://github.com/diana-hep/madminer . v2: improved text, fixed typos, better colors, added references

new physics: sensitivity
statistics
kinematics
signature
higher-dimensional
operator: dimension: 6
PYTHIA
GEANT
CERN LHC Coll

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