Improvement of the Rucio implementation for the LDCS platform and search for dark data

Abstract: In this work we aim to implement a software package to detect and categorize dark data, data not accessible or not known by the user, generated in the simulations of the Light Dark Matter eXperiment (LDMX). This will involve studying current existing solutions for such problems, attempting to implement them for the Lightweight Distributed Computing System (LDCS), and developing our own Dark Data Search (DDS) toolkit to perform the detection and categorization of the dark data. The result provided by these tools will be examined further for clues as to why and how dark data was created. Physics simulations of the LDMX detector were executed to create dark data, allowing us to study the conditions for their creation, and to get a deeper understanding of the physics for a missing momentum dark matter experiment. Based on the research done in this paper a multitude of systematic problems was found that would require addressing for the LDCS.