kreczko / swordfish

A Python tool to study the information yield of counting experiments.

swordfish is a Python tool to study the information yield of counting experiments.

NOTE: The package is stable, but still in beta phase. Use for production only if you know what you are doing.

Motivation

With swordfish you can quickly and accurately forecast experimental sensitivities without all the fuss with time-intensive Monte Carlos, mock data generation and likelihood maximization.

With swordfish you 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.

A large range of experiments in particle physics and astronomy are statistically described by a Poisson point process. The swordfish module implements at its core a rather general version of a Poisson point process, and provides easy access to its information geometrical properties. Based on this information, a number of common and less common tasks can be performed.

Get started

Most of the functionality of swordfish is demonstrated in two jupyter notebooks.

• Linear models and effective counts method
• Non-linear models and confidence contours

Documentation

A full documentation of swordfish can be found on github.io. For extensive details about Fisher forecasting with Poisson likelihoods, the effective counts method, the definition of information flux and the treatment of background systematics see http://arxiv.org/abs/1704.05458.

Installation

swordfish has been tested with Python 2.7.13 and the packages

• numpy 1.13.1
• scipy 0.19.0
• matplotlib 2.0.0

Let us know if you run into problems.

swordfish can be installed by invoking

git clone https://github.com/cweniger/swordfish
cd swordfish
python setup.py install

Citation

Please cite http://arxiv.org/abs/1704.05458.