Simulation Assisted Likelihood-free Anomaly Detection
- URL: http://arxiv.org/abs/2001.05001v1
- Date: Tue, 14 Jan 2020 19:00:09 GMT
- Title: Simulation Assisted Likelihood-free Anomaly Detection
- Authors: Anders Andreassen, Benjamin Nachman, and David Shih
- Abstract summary: This paper introduces a hybrid method that makes the best of both approaches to model-independent searches.
For potential signals that are resonant in one known feature, this new method first learns a parameterized reweighting function to morph a given simulation to match the data in sidebands.
The background estimation from the reweighted simulation allows for non-trivial correlations between features used for classification and the resonant feature.
- Score: 3.479254848034425
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Given the lack of evidence for new particle discoveries at the Large Hadron
Collider (LHC), it is critical to broaden the search program. A variety of
model-independent searches have been proposed, adding sensitivity to unexpected
signals. There are generally two types of such searches: those that rely
heavily on simulations and those that are entirely based on (unlabeled) data.
This paper introduces a hybrid method that makes the best of both approaches.
For potential signals that are resonant in one known feature, this new method
first learns a parameterized reweighting function to morph a given simulation
to match the data in sidebands. This function is then interpolated into the
signal region and then the reweighted background-only simulation can be used
for supervised learning as well as for background estimation. The background
estimation from the reweighted simulation allows for non-trivial correlations
between features used for classification and the resonant feature. A dijet
search with jet substructure is used to illustrate the new method. Future
applications of Simulation Assisted Likelihood-free Anomaly Detection (SALAD)
include a variety of final states and potential combinations with other
model-independent approaches.
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