neos

neural nice end-to-end optimized statistics

About

Run the binder demo :) ->

Leverages the shoulders of giants (jax and pyhf) to differentiate through a high-energy physics analysis workflow, including the construction of the frequentist profile likelihood.

If you're more of a video person, see this talk given by Nathan on the broader topic of differentiable programming in high-energy physics, which also covers neos.

Example usage -- train a neural network to optimize an expected p-value

setup

In a python 3 environment, run the following:

pip install --upgrade pip setuptools wheel
pip install neos
pip install git+http://github.com/scikit-hep/pyhf.git@make_difffable_model_ctor

With this, you should be able to run the demo notebook demo.ipynb on your pc :)

This workflow is as follows:

• From a set of normal distributions with different means, we'll generate four blobs of (x,y) points, corresponding to a signal process, a nominal background process, and two variations of the background from varying the background distribution's mean up and down.
• We'll then feed these points into the previously defined neural network for each blob, and construct a histogram of the output using kernel density estimation. The difference between the two background variations is used as a systematic uncertainty on the nominal background.
• We can then leverage the magic of pyhf to construct an event-counting statistical model from the histogram yields.
• Finally, we calculate the p-value of a test between the nominal signal and background-only hypotheses. This uses the familiar profile likelihood-based test statistic.

This counts as one forward pass of the workflow -- we then optimize the neural network by gradient descent, backpropagating through the whole analysis!

Thanks

A big thanks to the teams behind jax, fax, jaxopt and pyhf for their software and support.