Axion Magnetic Resonance:
A Novel Enhancement in Axion-Photon Conversion

We identify a new resonance, axion magnetic resonance (AMR), that can greatly enhance the conversion rate between axions and photons. A series of axion search experiments rely on converting them into photons inside a constant magnetic field background. A common bottleneck of such experiments is the conversion amplitude being suppressed by the axion mass when $m_a \gtrsim 10^{-4}$ eV. We point out that a spatial or temporal variation in the magnetic field can cancel the difference between the photon dispersion relation and that of the axion, hence greatly enhancing the conversion probability. We demonstrate that the enhancement can be achieved by both a helical magnetic field profile and a harmonic oscillation of the magnitude. Our approach can extend the projected ALPS II reach in the axion-photon coupling ($g_{a\gamma}$) by two orders of magnitude at $m_a = 10^{-3};\mathrm{eV}$ with moderate assumptions.

This is the numerical code that accompanies the publication.

Requirements

1. Python 3
2. numpy
3. scipy
4. pickle

How to run

To run this code, check out the notebook demo.ipynb. To reproduce the scan in $m_a$ with Gaussian noise, run the following

python scan.py
	-s < initial coordinate >
	 -e < end of propagation >
	 -B < magnetic field in Tesla >
	 -w < laser wavelength in nm >
	 -N < number of domains >
	 -l < lower value of log10ma >
	 -u < lower value of log10ma >
	 -g < grid size >
	 -o < output folder >
	 -n < number of polls>
	 -c < ga in GeV**-1>
	 -v < variation of noise>
	 -f < fraction variation of noise
	 -t < theta dot mean>
	 -p < initial state: photon 0, axion 1>

The results can then be loaded using demo.ipynb. The scans used in this work was produced with the following directive for the axion production mode:

python scan.py -s 0 -e 106 -B 5.3 -w 1064 -N 2000 -l -4.5 -u -2.5 -g 20  -n 100 -c 1.e-11 -f 0.01 -t 1. -p 0 -o chains/run024_prod_N2000_f001
python scan.py -s 0 -e 106 -B 5.3 -w 1064 -N 2000 -l -4.5 -u -2.5 -g 20  -n 100 -c 1.e-11 -f 0.10 -t 1. -p 0 -o chains/run024_prod_N2000_f010
python scan.py -s 0 -e 106 -B 5.3 -w 1064 -N 10 -l -4.5 -u -2.5 -g 20  -n 100 -c 1.e-11 -f 0.01 -t 1. -p 0 -o chains/run024_prod_N10_f001
python scan.py -s 0 -e 106 -B 5.3 -w 1064 -N 10 -l -4.5 -u -2.5 -g 20  -n 100 -c 1.e-11 -f 0.10 -t 1. -p 0 -o chains/run024_prod_N10_f010

and the following for the photon regeneration:

python scan.py -s 0 -e 106 -B 5.3 -w 1064 -N 2000 -l -4.5 -u -2.5 -g 20  -n 100 -c 1.e-11 -f 0.01 -t 1. -p 1 -o chains/run021_N2000_f001
python scan.py -s 0 -e 106 -B 5.3 -w 1064 -N 2000 -l -4.5 -u -2.5 -g 20  -n 100 -c 1.e-11 -f 0.10 -t 1. -p 1 -o chains/run021_N2000_f010
python scan.py -s 0 -e 106 -B 5.3 -w 1064 -N 10 -l -4.5 -u -2.5 -g 20  -n 100 -c 1.e-11 -f 0.01 -t 1. -p 1 -o chains/run021_N10_f001
python scan.py -s 0 -e 106 -B 5.3 -w 1064 -N 10 -l -4.5 -u -2.5 -g 20  -n 100 -c 1.e-11 -f 0.10 -t 1. -p 1 -o chains/run021_N10_f010

It takes about 1-2 hours on a 48 core cluster (Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz) to finish the run. If the cluster in your physics department is jammed, you can first play with the pickled scan results we create. They can be downloaded from here or by request.

Bibtex entry

If you find this study useful and/or use this code for your work, please consider citing Seong, Sun, & Yun 2023. The BiBTeX is the following:

@article{Seong:2023ran,
    author = "Seong, Hyeonseok and Sun, Chen and Yun, Seokhoon",
    title = "{Axion Magnetic Resonance: A Novel Enhancement in Axion-Photon Conversion}",
    eprint = "2308.10925",
    archivePrefix = "arXiv",
    primaryClass = "hep-ph",
    reportNumber = "DESY-23-118, LA-UR-23-29378, CTPU-PTC-23-39",
    month = "8",
    year = "2023"
}