Speaker
Description
Heavy ion storage rings offer a unique possibility to investigate atomic and nuclear properties of highly charged ions. GSI/FAIR accelerator facility is home to several such storage rings for highly charged ion research, such as the currently active experimental storage ring (ESR) and the CRYRING and the planned high energy storage ring (HESR) and the collector ring (CR). In such experiments, usually several as well as different types of detectors are used. As a result, huge amounts of data are generated that need to be analyzed using complex methods, both during the experiment (online) and afterwards (offline). As an example one can mention characteristic spectral lines in atomic physics experiments or nuclear mass and lifetime measurement scenarios.
A precise online analysis is of prominent significance due to the strict timing requirements imposed either by the physics (short lived states) or by the machine. Decisions based on a quick but exact identification of the beam components during the data taking can be used for manual or automated change of the parameters of the experiment and the machine in order to optimize the focus on the regions of interest. The above procedures can greatly profit from deep learning and artificial intelligence ANN/DNN methods. During the offline analysis, the scope of the application of machine learning algorithms can be extended not only to finalize the results but to examine farther reaching correlation inside larger amounts of data.
What is your expertise in computing and / or software development?
Expertise in Computing and Software Development: Advanced Python programming knowledge
Please describe your expertise/areas in which you would like to contribute / advise.
Expertise: data analysis and visualization (Python + ROOT)
In ErUM-Data, what kind of data are you dealing with?
Type of Data: Experimental data: continuous stream sampled in time domain. to be processed later in frequency domain.
What is your field and role?
Field and Role: Heavy Ion Storage Ring Physics, Staff scientist and adjunct assistant professor
Please describe areas in which you would like to improve your knowledge / skills.
Areas to improve: Deep neural networks, preparation of data to be fed to the DNNs
My current most burning research question, I like to find partners for, is:
Most burning question: how to implement join inference of machine parameters as well as the unknown physical signal.
Please describe areas in which you can contribute to “data handling” teaching.
Data handling: Experience in large-scale facilities together with IT experts. Experience with GPU and basic experience with parallel computing farms (SLURM).
| Your ErUM - Committee is | KHuK - Komitee für Hadronen- und Kernphysik |
|---|---|
| Do you consent to the data usage and public abstract data posting in the ErUM-Data Community Information Exchange? | Yes |
