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[ascl:2110.014] swordfish: Information yield of counting experiments

Swordfish studies the information yield of counting experiments. It implements at its core a rather general version of a Poisson point process with background uncertainties described by a Gaussian random field, and provides easy access to its information geometrical properties. Based on this information, a number of common and less common tasks can be performed. Swordfish allows quick and accurate forecasts of experimental sensitivities without time-intensive Monte Carlos, mock data generation and likelihood maximization. It can:

- calculate the expected upper limit or discovery reach of an instrument;
- derive expected confidence contours for parameter reconstruction;
- visualize confidence contours as well as the underlying information metric field;
- calculate the information flux, an effective signal-to-noise ratio that accounts for background systematics and component degeneracies; and
- calculate the Euclideanized signal which approximately maps the signal to a new vector which can be used to calculate the Euclidean distance between points.

Code site:
https://github.com/cweniger/swordfish https://cweniger.github.io/swordfish/
Used in:
https://ui.adsabs.harvard.edu/abs/2019PhRvD..99d3541E
Described in:
https://ui.adsabs.harvard.edu/abs/2017arXiv171205401E
Bibcode:
2021ascl.soft10014W

Views: 280

ascl:2110.014
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