Enhancing low energy reconstruction and classification in KM3NeT/ORCA with transformers
This work addresses neutrino detection challenges for physics researchers, but it is incremental as it builds on existing transformer methods with domain-specific adaptations.
The study tackled the problem of low energy neutrino reconstruction and classification in the KM3NeT/ORCA telescope by incorporating physics and detector-inspired attention masks into transformers, resulting in improved model understanding and efficacy in fine-tuning across detector configurations.
The current KM3NeT/ORCA neutrino telescope, still under construction, has not yet reached its full potential in neutrino reconstruction capability. When training any deep learning model, no explicit information about the physics or the detector is provided, thus they remain unknown to the model. This study leverages the strengths of transformers by incorporating attention masks inspired by the physics and detector design, making the model understand both the telescope design and the neutrino physics measured on it. The study also shows the efficacy of transformers on retaining valuable information between detectors when doing fine-tuning from one configurations to another.