Chrome Extension
WeChat Mini Program
Use on ChatGLM

Search for Ultralight Axion Dark Matter in a Side-Band Analysis of a 199hg Free-Spin Precession Signal

SCIPOST PHYSICS(2023)

Univ Sussex | Univ Caen | Paul Scherrer Inst | Jagiellonian Univ | Swiss Fed Inst Technol | Univ Bern | Univ Leuven | Univ Grenoble Alpes | Scherrer Inst | Johannes Gutenberg Univ Mainz

Cited 2|Views46
Abstract
Ultra-low-mass axions are a viable dark matter candidate and may form a coherently oscillating classical field. Nuclear spins in experiments on Earth might couple to this oscillating axion dark-matter field, when propagating on Earth’s trajectory through our Galaxy. This spin coupling resembles an oscillating pseudo-magnetic field which modulates the spin precession of nuclear spins. Here we report on the null result of a demonstration experiment searching for a frequency modulation of the free spin-precession signal of 199Hg in a magnetic field. Our search covers the axion mass range 10^{-16} \textrm{eV} \lesssim m_a \lesssim 10^{-13} \textrm{eV}10−16eV≲ma≲10−13eV and achieves a peak sensitivity to the axion-nucleon coupling of g_{aNN} \approx 3.5 \times 10^{-6} \textrm{GeV}^{-1}gaNN≈3.5×10−6GeV−1.
More
Translated text
Key words
Spin-Exchange Relaxation,Axions
PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Try using models to generate summary,it takes about 60s
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Related Papers
1868

被引用1924 | 浏览

1983

被引用3341 | 浏览

2014

被引用1256 | 浏览

Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
GPU is busy, summary generation fails
Rerequest