Author(s)

How to Obtain Support

General Information

Software and Tools

Needed datasets

Short Description of the Use Case

The problem of signal-background classification is an important part of physics analyses, since an improved classifier helps to achieve more statistically relevant results. The task is usually framed as binary classification, in which the positive class is represented by the signal, and the negative one by the background since we want to reject it as much as possible while preserving (i.e. correctly classifying) the most signal we can.

Such problem can be solved either manually (with a cut-based approach by determining selection thresholds on multiple variables) or by means of machine learning. The machine learning approach we are going to discuss is about parametric neural networks (pNNs), which are a specialized kind of neural network classifier able to leverage a physics parameter, like the particle's mass. Such design enables the pNN to replace a set of classifiers each trained at a particular value of the physics parameters: e.g. each model trained on a specific signal mass hypothesis.

Another benefit of pNNs is their ability to smoothly interpolate intermediate values of the physics parameter, in a natural and consistent way: neural networks are notably smooth functions, but overfitting may prevent to achieve interpolation on some or all intermediate values of the parameter at all. In this regard is usually useful to ensure enough regularization of the network: for example, we use a combination of dropout and weight decay.

Finally, the main design decisions to consider when defining a pNN, are:

• Which conditioning mechanism to use, responsible of combining the input features (or intermediate output) with the given physics parameter.
• How to assign the physics parameter to the background data samples.
• How to leverage the domain knowledge about the data to improve the training of the model.

How to execute it

The full code that supports this tutorial is available at:

The provided tutorial notebook can be run, either: manually (requires installing the dependencies, and downloading the datasets), or through our docker image (configured with libraries and data).

Manual Setup

Assuming a working Python setup, also having Jupyter notebook or lab (see here) already installed:

1. Clone the repository tutorial branch, and move within the folder: 

   git clone https://github.com/Luca96/affine-parametric-networks.git --branch tutorial
   # if on Google Colab, use %cd instead
   cd affine-parametric-networks

2. Run the notebook tutorial.ipynb either on your local machine, or via Google Colab: 

   # on a terminal: 
   jupyter notebook tutorial.ipynb

3. Copy the URL given in the terminal output on your browser.

Docker Setup

Assuming a working Docker installation, on a terminal:

1. Clone the tutorial branch and change directory, as described above at step 1.

2. Run the container (this also downloads the image which is about 4.3 Gb):

   docker run -it -d -v ${PWD}:/affine-parametric-networks -w /affine-parametric-networks -p 8888:8888 --name tutorial tommaso93/affine-parametric-networks

3. Execute the image named tutorial: 

   docker exec -it tutorial jupyter notebook --ip 0.0.0.0 --no-browser

4. Copy the URL starting with "http://127.0.0.1" given in the terminal output, and paste it on your browser. In alternative, type "http://localhost:8888/tree" on your browser, and then insert the token provided on the terminal output (the one starting with "?token="). Then run the tutorial.ipynb notebook (skipping the installation of the dependencies - "Set-up" section).

References

Improving parametric neural networks for high-energy physics (and beyond) - MLST

Parameterized neural networks for high-energy physics - EPJ