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Project
Establishing the scientific capabilities for the Radar Echo Telescope for Neutrinos through the development of reconstruction algorithms (FWOTM1332)
High-energy neutrinos are an ideal cosmic messenger to probe the universe, making their detection
crucial. Due to their weakly interacting nature and their small flux at high energies, detecting
neutrinos is non-trivial and requires large-scale detectors. In this research project, a novel in-ice
radio detection method is adopted based on radar. By developing reconstruction algorithms for
angular and energy resolution, I will determine the point and transient source sensitivity of the
future Radar Echo Telescope for Neutrinos (RET-N). First, I will adopt classical radar reconstruction
techniques. Second, I will develop a machine-learning algorithm. This approach allows for validating
the two methods to each other and to build a rigorous reconstruction framework. The second subject
of this project is neutrino flavour discrimination. There are three different neutrino types observed in
nature but it is often hard to distinguish them in experiments. Because of the wide angular
acceptance of the radar signal, we will show that full flavour discrimination in the high energy regime
(>10¹⁶ eV) is possible. As such, RET-N has the potential to be the first experiment to perform full
flavour discrimination in the high-energy regime and link in-ice and in-air neutrino detection
methods. With this project, I would largely contribute to establishing the scientific capabilities of RETN.
crucial. Due to their weakly interacting nature and their small flux at high energies, detecting
neutrinos is non-trivial and requires large-scale detectors. In this research project, a novel in-ice
radio detection method is adopted based on radar. By developing reconstruction algorithms for
angular and energy resolution, I will determine the point and transient source sensitivity of the
future Radar Echo Telescope for Neutrinos (RET-N). First, I will adopt classical radar reconstruction
techniques. Second, I will develop a machine-learning algorithm. This approach allows for validating
the two methods to each other and to build a rigorous reconstruction framework. The second subject
of this project is neutrino flavour discrimination. There are three different neutrino types observed in
nature but it is often hard to distinguish them in experiments. Because of the wide angular
acceptance of the radar signal, we will show that full flavour discrimination in the high energy regime
(>10¹⁶ eV) is possible. As such, RET-N has the potential to be the first experiment to perform full
flavour discrimination in the high-energy regime and link in-ice and in-air neutrino detection
methods. With this project, I would largely contribute to establishing the scientific capabilities of RETN.
Date:1 Nov 2025 → Today
Keywords:Neutrino Astronomy, Radar and radio detection methods, Neutrino and Cosmic ray physics
Disciplines:High energy physics, Experimental particle physics, High energy astrophysics, astroparticle physics and cosmic rays