Jupyter Notebooks used for Chapters 6 and 7 of my PhD Thesis

In these pages you will find the jupyter notebooks that are needed to fully reproduce the results of chapters 6 and 7, about various applications of the Standard Model Effective Field Theory to investigate the B-meson anomalies and their relation to other observables.

For the source code, go to https://github.com/Jorge-Alda/SMEFT19 and to download the notebooks, go to https://github.com/Jorge-Alda/SMEFT19-notebooks

This code was used for the following papers:

• Jorge Alda, Jaume Guasch, Siannah Penaranda, Anomalies in B mesons decays: A phenomenological approach.
• Jorge Alda, Jaume Guasch, Siannah Penaranda, Anomalies in B mesons decays: Present status and future collider prospects. Contribution to LCWS2021.
• Jorge Alda, Jaume Guasch, Siannah Penaranda, Using Machine Learning techniques in phenomenological studies in flavour physics.
• Jorge Alda, Jaume Guasch, Siannah Penaranda, Exploring B-physics anomalies at colliders. Contribution to EPS-HEP2021.

Contents

• Section 6.2
• Section 6.3
• Chapter 7

How to run the notebooks

Run on-line

The simplest way to run the notebooks is by visiting

Click on demo.ipynb to see a simple example, or navigate the folders to run the code [Be careful! Binder might impose CPU limits for long computations].

You can save any generated files with the menu File > Download. All files will be lost after shutting down Binder.

Run on your computer

The cleanest way to run the notebooks is using the Docker container. In this way you always have the right version of each packaging without interfering with the rest of your system. To get the container use the command (only the first time, may take a while)

docker pull jorgealda/smeft19-notebooks

and to start it

docker run -p 8888:8888 jorgealda/smeft19-notebooks

Copy the url that will appear at the bottom of the terminal and open it in your favourite browser.

Click on demo.ipynb to see a simple example, or navigate the folders to run the code. You can save any generated files with the menu File > Download.

Using File > Shut Down closes the JupyterLab and deletes the current container, so don't do this if you want to come back. If you just want to temporally stop the container, open another terminal and type

docker ps -a | grep smeft19-notebooks

and you will get something like this

068d5ecc684e   jorgealda/smeft19-notebooks           "/usr/local/bin/repo…"   43 seconds ago   Up 37 seconds            0.0.0.0:8888->8888/tcp, :::8888->8888/tcp   agitated_wiles

The first hexadecimal digits, 068d5ecc684e in this case, are the ID of the running container (yours will be different). You can stop the container using

docker stop 068d5ecc684e

To restart the container, use

docker start 068d5ecc684e && docker attach

To retrieve any file, even if the container is stopped, use Docker's cp. Files are stored by default in /home/smeft19/, and a copy the data produced for my thesis is in /home/smeft19/data/. If you have executed the Notebook demo.ipynb, you can get the generated plot with the command

docker cp 068d5ecc684e:/home/smeft19/demo.pdf demo.pdf