arXiv subjects

Christopher D. Tunnell

Publications and source records attributed to Christopher D. Tunnell.

7 recordsLinked to original sources

arXiv2026

First Search for Ultraheavy Dark Matter Using a Magnetically Levitated Particle

We present the first search for ultraheavy dark matter using a magnetically levitated particle. The POLONAISE experiment uses a milligram-scale ferromagnet levitated in a superconducting trap, admitting a force sensitivity of $0.07\,\mathrm{fN\,Hz^{-1/2}}$ and resolving impulses as small as $1\,\mathrm{TeV}/c$. Treating every candidate impulse as a possible dark matter event, we set optimum-interval upper limits on the neutron coupling $α_n$ for dark matter interacting through a new light mediator. For dark matter masses $10^6\,\mathrm{GeV}/c^2\text{-}10^{15}\,\mathrm{GeV}/c^2$ and mediators lighter than $30\,\mathrm{meV}/c^2$, we exclude couplings as low as $α_n = 3.2\times 10^{-9}$ at $95\%$ confidence level and set leading constraints on the dark matter-neutron cross section for composite dark matter. Our results extend levitated sensing beyond the mass reach of optical levitation by seven orders of magnitude into the ultraheavy dark matter frontier.

hep-ph↗

arXiv2026

Towards the Direct Detection of Composite Ultraheavy Dark Matter in Quantum Sensor Arrays

Quantum sensor arrays have recently been proposed as a promising platform for the direct detection of ultraheavy dark matter, which is typically assumed to behave as a point-like particle. However, particles with masses at or above the Planck scale cannot be elementary; instead, they must exist as composite objects with finite spatial extent. Such spatially extended dark matter models lead to distinctive phenomenology in these detectors, particularly when the dark matter also interacts through long-range forces with their own characteristic length scales. In this work, we study the sensitivity of quantum sensor arrays to composite, ultraheavy dark matter interacting via both gravity and a novel Yukawa force. We consider three phenomenologically motivated density profiles -- a tophat, a Gaussian, and an exponential -- and contrast their signals with the point-like limit. Using a Monte Carlo analysis based on the predicted impulse signals and estimates of thermal and quantum noise, we obtain sensitivity projections for a future realization of a quantum sensor array. We find a non-trivial interplay between the dark-matter scale radius, the inter-sensor spacing, and the Yukawa screening length. Future accelerometer arrays would provide valuable information about the mass and size of composite ultraheavy dark matter, and our work will help to characterize the signatures of different theoretical models of ultraheavy dark matter.

hep-ph↗

arXiv2026

Magnetic Levitation as a New Probe of Non-Newtonian Gravity

We present MORRIS (Magnetic Oscillatory Resonator for Rare-Interaction Studies) and propose the first tabletop search for non-Newtonian gravity due to a Yukawa-like fifth force using a magnetically levitated particle. Our experiment comprises a levitated sub-millimeter magnet in a superconducting trap that is driven by a time-periodic source. Featuring short-, medium-, and long-term stages, MORRIS will admit increasing sensitivities to the force coupling strength $α$, optimally probing screening lengths of $λ\sim 1\,\mathrm{mm}$. Our short-term setup provides a proof-of-principle study, with our medium- and long-term stages respectively constraining $α\lesssim 10^{-4}$ and $α\lesssim 10^{-5}$, leading over existing bounds. Our projections are readily recastable to concrete models predicting the existence of fifth forces, and our statistical analysis is generally applicable to well-characterized sinusoidal driving forces. By leveraging ultralow dissipation and heavy test masses, MORRIS opens a new window onto tests of small-scale gravity and searches for physics beyond the Standard Model.

hep-ph↗

arXiv2025

First Search for Ultralight Dark Matter Using a Magnetically Levitated Particle

We perform the first search for ultralight dark matter using a magnetically levitated particle. A sub-millimeter permanent magnet is levitated in a superconducting trap with a measured force sensitivity of $0.2\,\mathrm{fN/\sqrt{Hz}}$. We find no evidence of a signal and derive limits on dark matter coupled to the difference between baryon and lepton number, $B - L$, in the mass range $(1.10360 \text{ - } 1.10485) \times 10^{-13}\,\mathrm{eV} / c^2$. Our most stringent limit on the coupling strength is $g_{B - L} \lesssim 2.98 \times 10^{-21}$. We propose the POLONAISE (Probing Oscillations using Levitated Objects for Novel Accelerometry in Searches of Exotic physics) experiment, featuring short-, medium-, and long-term upgrades that will give us leading sensitivity in a wide mass range and demonstrating the promise of this novel quantum sensing technology in the hunt for dark matter.

hep-ph↗

arXiv2022

Domain-informed neural networks for interaction localization within astroparticle experiments

This work proposes a domain-informed neural network architecture for experimental particle physics, using particle interaction localization with the time-projection chamber (TPC) technology for dark matter research as an example application. A key feature of the signals generated within the TPC is that they allow localization of particle interactions through a process called reconstruction. While multilayer perceptrons (MLPs) have emerged as a leading contender for reconstruction in TPCs, such a black-box approach does not reflect prior knowledge of the underlying scientific processes. This paper looks anew at neural network-based interaction localization and encodes prior detector knowledge, in terms of both signal characteristics and detector geometry, into the feature encoding and the output layers of a multilayer neural network. The resulting Domain-informed Neural Network (DiNN) limits the receptive fields of the neurons in the initial feature encoding layers in order to account for the spatially localized nature of the signals produced within the TPC. This aspect of the DiNN, which has similarities with the emerging area of graph neural networks in that the neurons in the initial layers only connect to a handful of neurons in their succeeding layer, significantly reduces the number of parameters in the network in comparison to an MLP. In addition, in order to account for the detector geometry, the output layers of the network are modified using two geometric transformations to ensure the DiNN produces localizations within the interior of the detector. The end result is a neural network architecture that has 60% fewer parameters than an MLP, but that still achieves similar localization performance and provides a path to future architectural developments with improved performance because of their ability to encode additional domain knowledge into the architecture.

hep-ex↗

arXiv2022

A Method for Quantifying Position Reconstruction Uncertainty in Astroparticle Physics using Bayesian Networks

Robust position reconstruction is paramount for enabling discoveries in astroparticle physics as backgrounds are significantly reduced by only considering interactions within the fiducial volume. In this work, we present for the first time a method for position reconstruction using a Bayesian network which provides per interaction uncertainties. We demonstrate the utility of this method with simulated data based on the XENONnT detector design, a dual-phase xenon time-projection chamber, as a proof-of-concept. The network structure includes variables representing the 2D position of the interaction within the detector, the number of electrons entering the gaseous phase, and the hits measured by each sensor in the top array of the detector. The precision of the position reconstruction (difference between the true and expectation value of position) is comparable to the state-of-the-art methods -- an RMS of 0.69 cm, ~0.09 of the sensor spacing, for the inner part of the detector (<60 cm) and 0.98 cm, ~0.12 of the sensor spacing, near the wall of the detector (>60 cm). More importantly, the uncertainty of each interaction position was directly computed, which is not possible with other reconstruction methods. The method found a median 3-$σ$ confidence region of 11 cm$^2$ for the inner part of the detector and 21 cm$^2$ near the wall of the detector. We found the Bayesian network framework to be well suited to the problem of position reconstruction. The performance of this proof-of-concept, even with several simplifying assumptions, shows that this is a promising method for providing per interaction uncertainty, which can be extended to energy reconstruction and signal classification.

astro-ph.IM↗

arXiv2011

Sensitivity to eV-scale Neutrinos of Experiments at a Very Low Energy Neutrino Factory

The results of LSND have yet to be confirmed at the $5 σ$-level. An experiment is proposed utilizing a 3 GeV muon storage ring that would allow for both disappearance and appearance channels to be explored at short-baselines. The appearance channel could provide well over $5 σ$ confirmation or rejection of the LSND result. Other physics could also be performed at such a facility such as the measurement of electron-neutrino cross sections. The sensitivity of experiments at a Very Low Energy Neutrino Factory (VLENF) to neutrinos at the eV-scale is presented.

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