Speaker
Description
An important prerequisite for performing a joint analysis of data from different experiments is a search for ways to integrate these data. This process includes mapping, i.e. finding a correspondence of observables between each other, establishing similarities and differences, and normalization, i.e. shifting measurement scales to allow comparison of corresponding normalized values for observables, after which we can perform data analysis on a common dataset.
At the system level, we are interested in developing a distributed storage system for the data, as well as using parallel computations to speed up the analysis and setting up a workload management system for distributed launching of analysis and simulation jobs.
The talk will consider approaches to solve these problems that would be used in the German-Russian Astroparticle Data Life Cycle project for the joint analysis of data from the TUNKA-133 and KASCADE-Grande experiments. The increased statistics obtained with the help of these methods will be used to investigate rare processes, for example, study the properties of high-energy gamma rays from galactic sources.
