arXiv subjects

Juehang Qin

Publications and source records attributed to Juehang Qin.

12 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↗

arXiv2025

Physics-informed continuous normalizing flows to learn the electric field within a time-projection chamber

Accurate position reconstruction in noble-element time-projection chambers (TPCs) is critical for rare-event searches in astroparticle physics, yet is systematically limited by electric field distortions arising from charge accumulation on detector surfaces. Conventional data-driven field corrections suffer from three fundamental limitations: discretization artifacts that break smoothness and differentiability, lack of guaranteed consistency with Maxwell's equations, and statistical requirements of $\mathcal{O}(10^7)$ calibration events. We introduce a physics-informed continuous normalizing flow architecture that learns the electric field transformation directly from calibration data while enforcing the constraint of field conservativity through the model structure itself. Applied to simulated $^{83\mathrm{m}}$Kr calibration data in an XLZD-like dual-phase xenon TPC, our method achieves superior reconstruction accuracy compared to histogram-based corrections when trained on identical datasets, demonstrating viable performance with only $6\times10^5$ events$\unicode{x2013}$an order of magnitude reduction in calibration requirements. This approach enables practical monthly field monitoring campaigns, propagation of position uncertainties through differentiable transformations, and enhanced background discrimination in next-generation rare-event searches.

physics.ins-det↗

arXiv2025

Mechanical Sensors for Ultraheavy Dark Matter Searches via Long-range Forces

Dark matter candidates with masses around the Planck-scale are theoretically well-motivated, and it has been suggested that it might be possible to search for dark matter solely via gravitational interactions in this mass range. In this work, we explore the pathway towards searching for dark matter candidates with masses around the Planck-scale using mechanical sensors while considering realistic experimental constraints, and develop analysis techniques needed to conduct such searches. These dark matter particles are expected to leave tracks as their signature in mechanical sensor arrays, and we show that we can effectively search for such tracks using statistical approaches to track-finding. We analyze a range of possible experimental setups and compute sensitivity projections for searches for ultraheavy dark matter coupling to the Standard Model via long-range forces. We find that while a search for Planck-scale dark matter purely via gravitational couplings would be exceedingly difficult, requiring $\sim 80\,\mathrm{dB}$ of quantum noise reduction with a $100^3$ array of devices, there is a wide range of currently unexplored dark matter candidates which can be searched for with already existing or near-term experimental platforms.

hep-ph↗

arXiv2025

FlowVAT: Normalizing Flow Variational Inference with Affine-Invariant Tempering

Multi-modal and high-dimensional posteriors present significant challenges for variational inference, causing mode-seeking behavior and collapse despite the theoretical expressiveness of normalizing flows. Traditional annealing methods require temperature schedules and hyperparameter tuning, falling short of the goal of truly black-box variational inference. We introduce FlowVAT, a conditional tempering approach for normalizing flow variational inference that addresses these limitations. Our method tempers both the base and target distributions simultaneously, maintaining affine-invariance under tempering. By conditioning the normalizing flow on temperature, we leverage overparameterized neural networks' generalization capabilities to train a single flow representing the posterior across a range of temperatures. This preserves modes identified at higher temperatures when sampling from the variational posterior at $T = 1$, mitigating standard variational methods' mode-seeking behavior. In experiments with 2, 10, and 20 dimensional multi-modal distributions, FlowVAT outperforms traditional and adaptive annealing methods, finding more modes and achieving better ELBO values, particularly in higher dimensions where existing approaches fail. Our method requires minimal hyperparameter tuning and does not require an annealing schedule, advancing toward fully-automatic black-box variational inference for complicated posteriors.

stat.ML↗

arXiv2025

Fast Bayesian Inference for Neutrino Non-Standard Interactions at Dark Matter Direct Detection Experiments

Multi-dimensional parameter spaces are commonly encountered in physics theories that go beyond the Standard Model. However, they often possess complicated posterior geometries that are expensive to traverse using techniques traditional to astroparticle physics. Several recent innovations, which are only beginning to make their way into this field, have made navigating such complex posteriors possible. These include GPU acceleration, automatic differentiation, and neural-network-guided reparameterization. We apply these advancements to dark matter direct detection experiments in the context of non-standard neutrino interactions and benchmark their performances against traditional nested sampling techniques when conducting Bayesian inference. Compared to nested sampling alone, we find that these techniques increase performance for both nested sampling and Hamiltonian Monte Carlo, accelerating inference by factors of $\sim 100$ and $\sim 60$, respectively. As nested sampling also evaluates the Bayesian evidence, these advancements can be exploited to improve model comparison performance while retaining compatibility with existing implementations that are widely used in the natural sciences. Using these techniques, we perform the first scan in the neutrino non-standard interactions parameter space for direct detection experiments whereby all parameters are allowed to vary simultaneously. We expect that these advancements are broadly applicable to other areas of astroparticle physics featuring multi-dimensional parameter spaces.

cs.LG↗

arXiv2024

Approximate Differentiable Likelihoods for Astroparticle Physics Experiments

Traditionally, inference in liquid xenon direct detection dark matter experiments has used estimators of event energy or density estimation of simulated data. Such methods have drawbacks compared to the computation of explicit likelihoods, such as an inability to conduct statistical inference in high-dimensional parameter spaces, or a failure to make use of all available information. In this work, we implement a continuous approximation of an event simulator model within a probabilistic programming framework, allowing for the application of high performance gradient-based inference methods such as the No-U-Turn Sampler. We demonstrate an improvement in inference results, with percent-level decreases in measurement uncertainties. Finally, in the case where some observables can be measured using multiple independent channels, such a method also enables the incorporation of additional information seamlessly, allowing for full use of the available information to be made.

hep-ex↗

arXiv2023

Fast estimation of the look-elsewhere effect using Gaussian random fields

We discuss the use of Gaussian random fields to estimate the look-elsewhere effect correction. We show that Gaussian random fields can be used to model the null-hypothesis significance maps from a large set of statistical problems commonly encountered in physics, such as template matching and likelihood ratio tests. Some specific examples are searches for dark matter using pixel arrays, searches for astronomical transients, and searches for fast-radio bursts. Gaussian random fields can be sampled efficiently in the frequency domain, and the excursion probability can be fitted with these samples to extend any estimation of the look-elsewhere effect to lower $p$-values. We demonstrate this using two example template matching problems. Finally, we apply this to estimate the trial factor of a $4^3$ accelerometer array for the detection of dark matter tracks in the Windchime project. When a global significance of $3σ$ is required, the estimated trial factor for such an accelerometer array is $10^{14}$ for a one-second search, and $10^{22}$ for a one-year search.

physics.data-an↗

arXiv2022

Snowmass 2021 White Paper: The Windchime Project

The absence of clear signals from particle dark matter in direct detection experiments motivates new approaches in disparate regions of viable parameter space. In this Snowmass white paper, we outline the Windchime project, a program to build a large array of quantum-enhanced mechanical sensors. The ultimate aim is to build a detector capable of searching for Planck mass-scale dark matter purely through its gravitational coupling to ordinary matter. In the shorter term, we aim to search for a number of other physics targets, especially some ultralight dark matter candidates. Here, we discuss the basic design, open R&D challenges and opportunities, current experimental efforts, and both short- and long-term physics targets of the Windchime project.

hep-ex↗

arXiv2022

Direct Detection Limits on Heavy Dark Matter

Multiply-interacting massive particles (MIMPs) are heavy (>10^10 GeV/c^2) dark matter particles that interact strongly with regular matter, but may have evaded detection due to the low number density required to make up the local dark matter halo. These particles could leave track-like signatures in current experiments, similar to lightly-ionizing particles. We show that previously calculated limits from the MAJORANA Demonstrator on the flux of lightly-ionizing particles can be used to exclude MIMP dark matter parameter space up to a mass of 10^15 GeV/c^2. We also calculate limits from the standard XENON1T analysis in this high-mass regime, properly taking into account flux limitations and multi-scatter effects. Finally, we show that a dedicated MIMP analysis using the XENON1T dark matter search could probe unexplored parameter space up to masses of 10^18 GeV/c^2.

hep-ph↗

arXiv2021

Correlated Single- and Few-Electron Backgrounds Milliseconds after Interactions in Dual-Phase Liquid Xenon Time Projection Chambers

We characterize single- and few-electron backgrounds that are observed in dual-phase liquid xenon time projection chambers at timescales greatly exceeding a maximum drift time after an interaction. These instrumental backgrounds limit a detector's sensitivity to dark matter and cosmogenic neutrinos. Using the ~150g liquid xenon detector at Purdue University, we investigate how these backgrounds, produced after 122keV $^{57}$Co Compton interactions, behave under different detector conditions. We find that the rates of single- and few-electron signals follow power-laws with time after the interaction. We observe linearly increasing rates with increased extraction field, and increased rates in the single-electron background with increased drift field. Normalizing the rates to the primary interaction's measured ionization signal, the rates increase linearly with the depth of the interaction. We test the hypothesis that infrared photons (1550nm) would stimulate and accelerate electron emission via photodetachment from impurities, but find that even 1 Watt of infrared light fails to reduce these backgrounds. We thus provide a characterization that can inform background models for low-energy rare event searches.

physics.ins-det↗

arXiv2020

Mechanical Quantum Sensing in the Search for Dark Matter

Numerous astrophysical and cosmological observations are best explained by the existence of dark matter, a mass density which interacts only very weakly with visible, baryonic matter. Searching for the extremely weak signals produced by this dark matter strongly motivate the development of new, ultra-sensitive detector technologies. Paradigmatic advances in the control and readout of massive mechanical systems, in both the classical and quantum regimes, have enabled unprecedented levels of sensitivity. In this white paper, we outline recent ideas in the potential use of a range of solid-state mechanical sensing technologies to aid in the search for dark matter in a number of energy scales and with a variety of coupling mechanisms.

physics.ins-det↗