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

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.
• Calculate the Euclideanized signal which approximately maps the signal to a new vector which can be used to calculate the Euclidean distance between points

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 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.

Get started

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

• Equivalent counts method and Fisher Information Flux
• Confidence contours, streamline visualisation, and Euclideanized signal

In addition we provide two physics examples from direct and indirect detection

• CTA
• Xenon-1T

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 and http://arxiv.org/abs/1712.05401.

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

or

pip install git+https://github.com/cweniger/swordfish

Citation

If you use the package, please cite one or both of the papers http://arxiv.org/abs/1712.05401 and http://arxiv.org/abs/1704.05458.