Searching for Bosonic Dark Matter with Nuclear Magnetic Resonance
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Abstract
The nature of dark matter, the invisible substance making up over 80% of
the matter in the Universe, is one of the most fundamental mysteries of
modern physics. Ultralight bosons such as axions, axion-like particles or
dark photons could make up most of the dark matter. Couplings between
such bosons and nuclear spins may enable their direct detection via nuclear
magnetic resonance (NMR) spectroscopy: as nuclear spins move through the
galactic dark-matter halo, they couple to dark-matter and behave as if they
were in an oscillating magnetic field, generating a dark-matter-driven NMR
signal.
In the first chapter of this thesis, we review the predicted couplings
of axions and axion-like particles with baryonic matter that enable their
detection via NMR. We then describe two measurement schemes being
implemented in the Cosmic Axion Spin Precession Experiment (CASPEr), an
NMR experiment seeking to detect axion and axion-like particles. The first
method, presented in the original CASPEr proposal, consists of a resonant
search via continuous-wave NMR spectroscopy. This method offers the
highest sensitivity for frequencies ranging from a few Hz to hundreds of MHz,
corresponding to masses ma ∼ 10−14–10−6 eV. However, Sub-Hz frequencies
are typically difficult to probe with NMR due to the diminishing sensitivity
of magnetometers in this region. To circumvent this limitation, we suggest
new detection and data processing modalities: a non-resonant frequencymodulation
detection scheme, enabling searches from mHz to Hz frequencies
(ma ∼ 10−17–10−14 eV).As a second part of this thesis, we apply the above mentioned
non-resonant method and use ultralow-field NMR to probe the axionfermion
“wind” coupling and dark-photon couplings to nuclear spins.
No dark matter signal was detected above background, establishing new
experimental bounds for dark-matter bosons with masses ranging from
1.8 × 10−16 to 7.8 × 10−14 eV.In the last chapter of this thesis, we use Deep Neural Networks (DNNs)
to disentangle components of oscillating time series, arguably the most
common form of signals acquired during dark-matter searches. We show
that the regression and denoising performance is similar to those of leastsquare
curve fittings (LS-fit). We then explore various applications in which
we believe our architecture could prove useful for time-series processing,
when prior knowledge is incomplete. Because the Autoencoder needs no
prior information about the physical model, the remaining unknown latent
parameters can still be captured, thus making use of partial prior knowledge,
while leaving space for data exploration and discoveries.