IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-024-47566-0.html
   My bibliography  Save this article

Long-baseline quantum sensor network as dark matter haloscope

Author

Listed:
  • Min Jiang

    (University of Science and Technology of China
    University of Science and Technology of China
    University of Science and Technology of China)

  • Taizhou Hong

    (University of Science and Technology of China
    University of Science and Technology of China
    University of Science and Technology of China)

  • Dongdong Hu

    (University of Science and Technology of China
    University of Science and Technology of China)

  • Yifan Chen

    (Niels Bohr Institute)

  • Fengwei Yang

    (University of Utah)

  • Tao Hu

    (Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of Sciences)

  • Xiaodong Yang

    (Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of Sciences)

  • Jing Shu

    (Peking University
    Peking University
    Huairou)

  • Yue Zhao

    (University of Utah)

  • Xinhua Peng

    (University of Science and Technology of China
    University of Science and Technology of China
    University of Science and Technology of China)

  • Jiangfeng Du

    (University of Science and Technology of China
    University of Science and Technology of China
    University of Science and Technology of China
    Zhejiang University)

Abstract

Ultralight dark photons constitute a well-motivated candidate for dark matter. A coherent electromagnetic wave is expected to be induced by dark photons when coupled with Standard-Model photons through kinetic mixing mechanism, and should be spatially correlated within the de Broglie wavelength of dark photons. Here we report the first search for correlated dark-photon signals using a long-baseline network of 15 atomic magnetometers, which are situated in two separated meter-scale shield rooms with a distance of about 1700 km. Both the network’s multiple sensors and the shields large size significantly enhance the expected dark-photon electromagnetic signals, and long-baseline measurements confidently reduce many local noise sources. Using this network, we constrain the kinetic mixing coefficient of dark photon dark matter over the mass range 4.1 feV-2.1 peV, which represents the most stringent constraints derived from any terrestrial experiments operating over the aforementioned mass range. Our prospect indicates that future data releases may go beyond the astrophysical constraints from the cosmic microwave background and the plasma heating.

Suggested Citation

  • Min Jiang & Taizhou Hong & Dongdong Hu & Yifan Chen & Fengwei Yang & Tao Hu & Xiaodong Yang & Jing Shu & Yue Zhao & Xinhua Peng & Jiangfeng Du, 2024. "Long-baseline quantum sensor network as dark matter haloscope," Nature Communications, Nature, vol. 15(1), pages 1-7, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-47566-0
    DOI: 10.1038/s41467-024-47566-0
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-024-47566-0
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-024-47566-0?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. Elena Boto & Niall Holmes & James Leggett & Gillian Roberts & Vishal Shah & Sofie S. Meyer & Leonardo Duque Muñoz & Karen J. Mullinger & Tim M. Tierney & Sven Bestmann & Gareth R. Barnes & Richard Bow, 2018. "Moving magnetoencephalography towards real-world applications with a wearable system," Nature, Nature, vol. 555(7698), pages 657-661, March.
    2. I. K. Kominis & T. W. Kornack & J. C. Allred & M. V. Romalis, 2003. "A subfemtotesla multichannel atomic magnetometer," Nature, Nature, vol. 422(6932), pages 596-599, April.
    3. K. M. Backes & D. A. Palken & S. Al Kenany & B. M. Brubaker & S. B. Cahn & A. Droster & Gene C. Hilton & Sumita Ghosh & H. Jackson & S. K. Lamoreaux & A. F. Leder & K. W. Lehnert & S. M. Lewis & M. Ma, 2021. "A quantum enhanced search for dark matter axions," Nature, Nature, vol. 590(7845), pages 238-242, February.
    4. Xiang-Dong Chen & En-Hui Wang & Long-Kun Shan & Shao-Chun Zhang & Ce Feng & Yu Zheng & Yang Dong & Guang-Can Guo & Fang-Wen Sun, 2023. "Quantum enhanced radio detection and ranging with solid spins," Nature Communications, Nature, vol. 14(1), pages 1-8, December.
    5. Benjamin M. Roberts & Geoffrey Blewitt & Conner Dailey & Mac Murphy & Maxim Pospelov & Alex Rollings & Jeff Sherman & Wyatt Williams & Andrei Derevianko, 2017. "Search for domain wall dark matter with atomic clocks on board global positioning system satellites," Nature Communications, Nature, vol. 8(1), pages 1-9, December.
    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. Itay M. Bloch & Roy Shaham & Yonit Hochberg & Eric Kuflik & Tomer Volansky & Or Katz, 2023. "Constraints on axion-like dark matter from a SERF comagnetometer," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    2. Chen Zhang & Durga Dasari & Matthias Widmann & Jonas Meinel & Vadim Vorobyov & Polina Kapitanova & Elizaveta Nenasheva & Kazuo Nakamura & Hitoshi Sumiya & Shinobu Onoda & Junichi Isoya & Jörg Wrachtru, 2022. "Quantum-assisted distortion-free audio signal sensing," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    3. Arjen Vaartjes & Anders Kringhøj & Wyatt Vine & Tom Day & Andrea Morello & Jarryd J. Pla, 2024. "Strong microwave squeezing above 1 Tesla and 1 Kelvin," Nature Communications, Nature, vol. 15(1), pages 1-8, December.
    4. Ryan Snodgrass & Vincent Kotsubo & Scott Backhaus & Joel Ullom, 2024. "Dynamic acoustic optimization of pulse tube refrigerators for rapid cooldown," Nature Communications, Nature, vol. 15(1), pages 1-8, December.
    5. Alen Senanian & Sridhar Prabhu & Vladimir Kremenetski & Saswata Roy & Yingkang Cao & Jeremy Kline & Tatsuhiro Onodera & Logan G. Wright & Xiaodi Wu & Valla Fatemi & Peter L. McMahon, 2024. "Microwave signal processing using an analog quantum reservoir computer," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    6. Xin Meng & Youwei Zhang & Xichang Zhang & Shenchao Jin & Tingran Wang & Liang Jiang & Liantuan Xiao & Suotang Jia & Yanhong Xiao, 2023. "Machine learning assisted vector atomic magnetometry," Nature Communications, Nature, vol. 14(1), pages 1-9, 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:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-47566-0. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    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.