Unification of Deconvolution Algorithms for Cherenkov Astronomy
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Obtaining the distribution of a physical quantity is a frequent objective in experimental physics. In cases where the distribution of the relevant quantity cannot be accessed experimentally, it has to be reconstructed from distributions of correlated quantities that are measured, instead. This reconstruction is called deconvolution. Cherenkov astronomy is a deconvolution use case which studies the energy distribution of cosmic gamma radiation to reason about the characteristics of celestial objects emitting such radiation. We present a novel unified view on deconvolution methods, rephrasing them in the language of data science. Based on our unified formulation, we propose a novel stopping condition that guarantees fast convergence. We compare existing and new methods on synthetic and real-world data, showing that our method converges faster and more accurately than the existing machine learning based approach.
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