Transformers trained on cosmic ray simulations learn physically plausible features in positional encodings for symmetric air showers and in attention mechanisms for galaxy-origin particles.
Uncertainties in the Magnetic Field of the Milky Way
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
We improve on the model of the Galactic Magnetic Field (GMF) from Jansson \& Farrar (2012), which was constrained using all-sky rotation measures of extragalactic sources and polarized and unpolarized synchrotron emission data from WMAP. We have developed several alternative functional forms for the coherent and random components, used newer synchrotron products from Planck and WMAP and testes new models of the densities of thermal electrons and cosmic-ray electrons. The differences in the resultant GMF models, depending on which parameterization of the field, synchrotron product and electron densities are used, provides a measure of the uncertainty in our inference of the GMF. We discuss the impact of these uncertainties on charged-particle astronomy at ultra-high energies.
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astro-ph.IM 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
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What exactly did the Transformer learn from our physics data?
Transformers trained on cosmic ray simulations learn physically plausible features in positional encodings for symmetric air showers and in attention mechanisms for galaxy-origin particles.