Home

Analysis with LDMX can have many moving parts, and you'll likely develop your own workflow to get things done while working on your own projects. However, there are many basic pieces of information that you may find useful across the board! The pages of this wiki should help ease you into starting up analysis with LDMX.

Getting Started with LDMX Software:

Follow the introductory guide given in the README for IncandelaLab/LDMX-scripts/TutorialFiles to begin using LDMX software and conducting basis analysis. For more guides and information, refer to the links below as well as the sidebar located on the right hand side of this page.

Quick LDMX Reference

Find HERE a brief overview of some of the LDMX variables, abbreviations, detector concept, and signal/background processes.

Publications

Read through the papers below to get a better idea of the experiment itself. The first paper is very comprehensive, so you shouldn't expect to understand everything right away. A good rule of thumb is to focus on sections I-IV, as well as sections V.E-V.H.

• LDMX Design Study (2018)
• Photon Veto Paper (2019)
• LDMX Phenomenology Paper (2020)
• LDMX 8 GeV Paper (2023)

GitHub Links

The GitHub links below contain most of the software that you will be using for your studies, as well as basic documentation.

• https://github.com/IncandelaLab/LDMX-scripts
• https://github.com/LDMX-Software/ldmx-sw
• ldmx-sw docs
• ParticleNet Code (for v13 detector)

Miscellaneous Links

• LDMX Basics Seminar by Tom
• https://confluence.slac.stanford.edu/display/MME/Light+Dark+Matter+Experiment
• UCSB HEX Seminar Slides by Valentina
• MIP Tracking Note by Phil and Juan
• seg-mip BDT presentation
• Linux Command Line Textbook
• Slurm Documentation (for submitting jobs on POD, also can run man sbatch on POD terminal)
• More Useful Slurm Commands

ParticleNet / Deep Learning

• ParticleNet Paper(2019)
• Dynamic Graph CNN Paper (2018)
• CS231n Lecture Notes (convolutional neural networks)