IDEAS home Printed from https://ideas.repec.org/p/osf/osfxxx/5jwdg_v1.html
   My bibliography  Save this paper

Galactic Gravitational Field Measurements Part 2: Black Hole Energy Transfer

Author

Listed:
  • Young, Christopher M

Abstract

This paper, Part 2 of the Galactic Gravitational Field Measurements series, presents a mathematical analysis of black hole energy transfer mechanisms into the auxiliary modulation coordinate, with particular emphasis on the extended gravitational metric incorporating field modulation and generalized modulation-based field equation as complementary frameworks for understanding reality at its most fundamental level. We establish that black holes function not as terminal gravitational singularities but as sophisticated dimensional interfaces that convert matter into structured energy patterns within the auxiliary modulation coordinate. Through rigorous analysis using entropy-resolved resonance profiles, we demonstrate that information is never lost but rather transformed through specific resonance channels into modulation patterns that preserve all quantum information. Our framework transcends conventional approaches by revealing black holes as computational nodes in a universal information processing network, with event horizons functioning as phase transition boundaries between spacetime and the deeper dimensional structure of the auxiliary modulation coordinate. The mathematical formalism resolves longstanding paradoxes in physics while establishing a rigorous foundation for understanding reality as a unified informational substrate from which matter, energy, space, time, and consciousness emerge as interconnected phenomena.

Suggested Citation

  • Young, Christopher M, 2025. "Galactic Gravitational Field Measurements Part 2: Black Hole Energy Transfer," OSF Preprints 5jwdg_v1, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:5jwdg_v1
    DOI: 10.31219/osf.io/5jwdg_v1
    as

    Download full text from publisher

    File URL: https://osf.io/download/681579ac382990233a9fdcca/
    Download Restriction: no
    File URL: https://libkey.io/10.31219/osf.io/5jwdg_v1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Jacob Biamonte & Peter Wittek & Nicola Pancotti & Patrick Rebentrost & Nathan Wiebe & Seth Lloyd, 2017. "Quantum machine learning," Nature, Nature, vol. 549(7671), pages 195-202, September.
    2. Seth Lloyd, 2000. "Ultimate physical limits to computation," Nature, Nature, vol. 406(6799), pages 1047-1054, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wu, Jiang & Ou, Guiyan & Liu, Xiaohui & Dong, Ke, 2022. "How does academic education background affect top researchers’ performance? Evidence from the field of artificial intelligence," Journal of Informetrics, Elsevier, vol. 16(2).
    2. Xinbiao Wang & Yuxuan Du & Zhuozhuo Tu & Yong Luo & Xiao Yuan & Dacheng Tao, 2024. "Transition role of entangled data in quantum machine learning," Nature Communications, Nature, vol. 15(1), pages 1-8, December.
    3. Ajagekar, Akshay & You, Fengqi, 2021. "Quantum computing based hybrid deep learning for fault diagnosis in electrical power systems," Applied Energy, Elsevier, vol. 303(C).
    4. Richters, Oliver, 2013. "Perspektiven für ein glückliches Leben jenseits des Wachstums," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, pages 99-113.
    5. Jurgita Bruneckiene & Robertas Jucevicius & Ineta Zykiene & Jonas Rapsikevicius & Mantas Lukauskas, 2019. "Assessment of Investment Attractiveness in European Countries by Artificial Neural Networks: What Competences are Needed to Make a Decision on Collective Well-Being?," Sustainability, MDPI, vol. 11(24), pages 1-23, December.
    6. Nikolaos Schetakis & Davit Aghamalyan & Michael Boguslavsky & Agnieszka Rees & Marc Rakotomalala & Paul Robert Griffin, 2024. "Quantum Machine Learning for Credit Scoring," Mathematics, MDPI, vol. 12(9), pages 1-12, May.
    7. Eric Vaz, 2024. "Quantum machine learning in spatial analysis: a paradigm shift in resource allocation and environmental modeling," Letters in Spatial and Resource Sciences, Springer, vol. 17(1), pages 1-13, December.
    8. Li, Nianqiao & Yan, Fei & Hirota, Kaoru, 2022. "Quantum data visualization: A quantum computing framework for enhancing visual analysis of data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 599(C).
    9. Xu, Zhengmeng & Wang, Yujie & Feng, Xiaotong & Wang, Yilin & Li, Yanli & Lin, Hai, 2024. "Quantum-enhanced forecasting: Leveraging quantum gramian angular field and CNNs for stock return predictions," Finance Research Letters, Elsevier, vol. 67(PA).
    10. Cambyse Rouzé & Daniel Stilck França & Emilio Onorati & James D. Watson, 2024. "Efficient learning of ground and thermal states within phases of matter," Nature Communications, Nature, vol. 15(1), pages 1-8, December.
    11. Elies Gil-Fuster & Jens Eisert & Carlos Bravo-Prieto, 2024. "Understanding quantum machine learning also requires rethinking generalization," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    12. Guo, Mingchao & Liu, Hailing & Li, Yongmei & Li, Wenmin & Gao, Fei & Qin, Sujuan & Wen, Qiaoyan, 2022. "Quantum algorithms for anomaly detection using amplitude estimation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    13. Vicente Moret-Bonillo & Samuel Magaz-Romero & Eduardo Mosqueira-Rey, 2022. "Quantum Computing for Dealing with Inaccurate Knowledge Related to the Certainty Factors Model," Mathematics, MDPI, vol. 10(2), pages 1-21, January.
    14. D. Bernal-Casas & J. M. Oller, 2024. "Analyzing Sample Size in Information-Theoretic Models," Mathematics, MDPI, vol. 12(24), pages 1-15, December.
    15. Gong, Li-Hua & Xiang, Ling-Zhi & Liu, Si-Hang & Zhou, Nan-Run, 2022. "Born machine model based on matrix product state quantum circuit," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    16. Laura Böhm & Sebastian Kolb & Thomas Plankenbühler & Jonas Miederer & Simon Markthaler & Jürgen Karl, 2023. "Short-Term Natural Gas and Carbon Price Forecasting Using Artificial Neural Networks," Energies, MDPI, vol. 16(18), pages 1-25, September.
    17. Isaiah Hull & Or Sattath & Eleni Diamanti & Göran Wendin, 2024. "Quantum Algorithms," Contributions to Economics, in: Quantum Technology for Economists, chapter 0, pages 37-103, Springer.
    18. Serena Di Giorgio & Paulo Mateus, 2021. "On the Complexity of Finding the Maximum Entropy Compatible Quantum State," Mathematics, MDPI, vol. 9(2), pages 1-24, January.
    19. Sheshadri Chatterjee & Ranjan Chaudhuri & Sachin Kamble & Shivam Gupta & Uthayasankar Sivarajah, 2023. "Adoption of Artificial Intelligence and Cutting-Edge Technologies for Production System Sustainability: A Moderator-Mediation Analysis," Information Systems Frontiers, Springer, vol. 25(5), pages 1779-1794, October.
    20. Junyu Liu & Minzhao Liu & Jin-Peng Liu & Ziyu Ye & Yunfei Wang & Yuri Alexeev & Jens Eisert & Liang Jiang, 2024. "Towards provably efficient quantum algorithms for large-scale machine-learning models," Nature Communications, Nature, vol. 15(1), pages 1-6, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:osf:osfxxx:5jwdg_v1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: OSF (email available below). General contact details of provider: https://osf.io/preprints/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.