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| Үндсэн зохиолч: | |
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| Формат: | Recurso digital |
| Хэл сонгох: | англи |
| Хэвлэсэн: |
Zenodo
2026
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| Нөхцлүүд: | |
| Онлайн хандалт: | https://doi.org/10.5281/zenodo.20180021 |
| Шошгууд: |
Шошго нэмэх
Шошго байхгүй, Энэхүү баримтыг шошголох эхний хүн болох!
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| _version_ | 1866901760469106688 |
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| author | Hess, Malin |
| author_facet | Hess, Malin |
| contents | <p class="p1"><strong>THE NEUTRINO PROBABILITY ENGINE</strong></p> <p class="p2">A Technical Framework for Cosmic Navigation and</p> <p class="p2">Astrophysical Sensing via Neutrino Fingerprint Cartography</p> <p class="p2">Malin Hess, GPT-5.5 Thinking (OpenAI), Claude (Anthropic Sonnet 4.6), & DeepSeek</p> <p class="p2">(DeepSeek-R1)</p> <p class="p3">May 2026</p> <p class="p4">Abstract</p> <p class="p4">We propose the Neutrino Probability Engine (NPE), a complete system architecture for using neutrinos as passive cosmic state messengers rather than as an energy source. The core instrument, the Compact Neutrino Signature Imager (CNSI), is a 12-faced dodecahedral detector that reconstructs incoming neutrino direction, energy, timing, and probabilistic flavour from secondary interaction signals across its sensor faces. We derive the full Cherenkov first-photon timing formula, demonstrate that 66 face-pair equations with 6 unknowns provide 60 degrees of redundancy for robust reconstruction, and develop the complete chi-squared minimisation procedure. The NPE integrates CNSI hardware with a real-time AI inference layer and an onboard atlas-building programme, enabling deep-space navigation, astrophysical source identification, and early detection of cosmic events. A technology readiness assessment identifies the detector scale constraint as the primary engineering barrier to compact spacecraft implementation, with CEvNS in high-atomic-number materials as the most credible near-term development path.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20180021 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | THE NEUTRINO PROBABILITY ENGINE A Technical Framework for Cosmic Navigation and Astrophysical Sensing via Neutrino Fingerprint Cartography Hess, Malin neutrino cosmic navigation probability engine Astrophysics Astrophysics <p class="p1"><strong>THE NEUTRINO PROBABILITY ENGINE</strong></p> <p class="p2">A Technical Framework for Cosmic Navigation and</p> <p class="p2">Astrophysical Sensing via Neutrino Fingerprint Cartography</p> <p class="p2">Malin Hess, GPT-5.5 Thinking (OpenAI), Claude (Anthropic Sonnet 4.6), & DeepSeek</p> <p class="p2">(DeepSeek-R1)</p> <p class="p3">May 2026</p> <p class="p4">Abstract</p> <p class="p4">We propose the Neutrino Probability Engine (NPE), a complete system architecture for using neutrinos as passive cosmic state messengers rather than as an energy source. The core instrument, the Compact Neutrino Signature Imager (CNSI), is a 12-faced dodecahedral detector that reconstructs incoming neutrino direction, energy, timing, and probabilistic flavour from secondary interaction signals across its sensor faces. We derive the full Cherenkov first-photon timing formula, demonstrate that 66 face-pair equations with 6 unknowns provide 60 degrees of redundancy for robust reconstruction, and develop the complete chi-squared minimisation procedure. The NPE integrates CNSI hardware with a real-time AI inference layer and an onboard atlas-building programme, enabling deep-space navigation, astrophysical source identification, and early detection of cosmic events. A technology readiness assessment identifies the detector scale constraint as the primary engineering barrier to compact spacecraft implementation, with CEvNS in high-atomic-number materials as the most credible near-term development path.</p> |
| title | THE NEUTRINO PROBABILITY ENGINE A Technical Framework for Cosmic Navigation and Astrophysical Sensing via Neutrino Fingerprint Cartography |
| topic | neutrino cosmic navigation probability engine Astrophysics Astrophysics |
| url | https://doi.org/10.5281/zenodo.20180021 |