Master Thesis: Lossy Compression Algorithms for Space-based Particle Detector Data

01.09.2024, Abschlussarbeiten, Bachelor- und Masterarbeiten

We are looking for a master student at the interface between space-based particle physics and data processing. Our detector will measure the spectra of particles that are trapped in the Earth's magnetic field. Due to the limitations of our satellite platform, we want to investigate and evaluate lossy and lossless compression algorithms for our detector data. These can be based on simple, classic approaches or employ more complex, neural-network-based techniques.

The Laboratory for Rapid Space Missions at the Origins Excellence Cluster focusses on developing scientific instruments on compact satellite platforms, called CubeSats. These enable fast and modular deployment of complete, autonomous satellite systems at low cost. One of these instruments will be the AFIS particle detector (Antiproton Flux in Space), which measures spectra of trapped particles in low orbits around the Earth. High event rates are expected and resources are limited, so we are developing data acquisition, particle trigger and compression algorithms based on an FPGA (Field-Programmable Gate Array). One approach is lossy compression of event data, e.g. by transformation to other bases or dimensionality reduction. There are numerous possibilities, including classical approaches, custom solutions and neural networks (e.g. autoencoders). Research, comparison, design and if possible Implementation of such algorithms will be your task in our team.

Your objectives include:

• Design space exploration of possible lossy compression algorithms • Development of compression algorithms based on simulated and real data from our detector on the ISS • Test and Evaluation of different promising approaches • Optional: Implementation of algorithms on an FPGA via HLS (C++), Verilog or VHDL • Optional: Support in the development of the processing framework, including particle trigger

You will gain skills in the following areas:

• Data and image processing • Deep learning, compression techniques • FPGA, VHDL, Python

You will work at the interface between science and engineering. If successful, your own software could be part of missions, e.g. on the ISS or satellites. We expect a high degree of self-responsibility, motivation, a structured way of working, creativity, and a good share of curiosity. We offer work in a small, interdisciplinary team, diverse topics, and enough space for self-development and own ideas. Knowledge of one or more of the above-mentioned fields is strongly appreciated.

Available for: MSc. Physics (AEP, KTA) MSc. Engineering (Electrical, Aerospace or similar) MSc. Software Engineering

Chair: E18, Prof. Paul

Further information: peter.hinderberger@tum.de or personally in PH3276

Kontakt: peter.hinderberger@tum.de