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Design and implementation of the ABRACADABRA-10 cm axion dark matter search

Jonathan L. Ouellet^1,*, Chiara P. Salemi¹, Joshua W. Foster², Reyco Henning^3,4, Zachary Bogorad¹, Janet M. Conrad¹, Joseph A. Formaggio¹, Yonatan Kahn^5,6, Joe Minervini⁷ et al.

Alexey Radovinsky⁷, Nicholas L. Rodd^8,9, Benjamin R. Safdi², Jesse Thaler¹⁰, Daniel Winklehner¹, and Lindley Winslow^1,†

¹Laboratory for Nuclear Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
²Leinweber Center for Theoretical Physics, Department of Physics, University of Michigan, Ann Arbor, Michigan 48109, USA
³University of North Carolina, Chapel Hill, North Carolina 27599, USA
⁴Triangle Universities Nuclear Laboratory, Durham, North Carolina 27708, USA
⁵Princeton University, Princeton, New Jersey 08544, USA
⁶Kavli Institute for Cosmological Physics, University of Chicago, Chicago, Illinois 60637, USA
⁷Plasma Science and Fusion Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
⁸Berkeley Center for Theoretical Physics, University of California, Berkeley, California 94720, USA
⁹Theoretical Physics Group, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
¹⁰Center for Theoretical Physics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA

^*ouelletj@mit.edu
^†lwinslow@mit.edu

Phys. Rev. D 99, 052012 – Published 29 March, 2019

DOI: https://doi.org/10.1103/PhysRevD.99.052012

Abstract

The past few years have seen a renewed interest in the search for light particle dark matter. ABRACADABRA is a new experimental program to search for axion dark matter over a broad range of masses, $10^{- 12} ≲ m_{a} ≲ 10^{- 6} eV$ . ABRACADABRA-10 cm is a small-scale prototype for a future detector that could be sensitive to QCD axion couplings. In this paper, we present the details of the design, construction, and data analysis for the first axion dark matter search with the ABRACADABRA-10 cm detector. We include a detailed discussion of the statistical techniques used to extract the limit from the first result with an emphasis on creating a robust statistical footing for interpreting those limits.

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