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We perform the first search for ultralight dark matter using a magnetically levitated particle. A submillimeter permanent magnet is levitated in a superconducting trap with a measured force sensitivity of $0.2\mathrm{fN}/\sqrt{\mathrm{Hz}}$. 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 $(1.10360-1.10485)\times {10}^{-13}\mathrm{eV}/c^2$. 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, which features short-, medium-, and long-term upgrades that will give us leading sensitivity in a wide mass range, demonstrating the promise of this novel quantum sensing technology in the hunt for dark matter. Published by the American Physical Society2025 

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 (i.e., inverse-problem regression). 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 (deep) neural network. The resulting neural network, termed 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.