arXiv subjects

Kevin Zhou

Publications and source records attributed to Kevin Zhou.

36 records · Page 2Linked to original sources

arXiv2023

Discovering QCD-Coupled Axion Dark Matter with Polarization Haloscopes

In the presence of QCD axion dark matter, atoms acquire time-dependent electric dipole moments. This effect gives rise to an oscillating current in a nuclear spin-polarized dielectric, which can resonantly excite an electromagnetic mode of a microwave cavity. We show that with existing technology such a "polarization haloscope" can explore orders of magnitude of new parameter space for QCD-coupled axions. If any cavity haloscope detects a signal from the axion-photon coupling, an upgraded polarization haloscope has the unique ability to test whether it arises from the QCD axion.

hep-ph↗

arXiv2023

Current Status and Future Prospects for the Light Dark Matter eXperiment

The constituents of dark matter are still unknown, and the viable possibilities span a vast range of masses. The physics community has established searching for sub-GeV dark matter as a high priority and identified accelerator-based experiments as an essential facet of this search strategy. A key goal of the accelerator-based dark matter program is testing the broad idea of thermally produced sub-GeV dark matter through experiments designed to directly produce dark matter particles. The most sensitive way to search for the production of light dark matter is to use a primary electron beam to produce it in fixed-target collisions. The Light Dark Matter eXperiment (LDMX) is an electron-beam fixed-target missing-momentum experiment that realizes this approach and provides unique sensitivity to light dark matter in the sub-GeV range. This contribution provides an overview of the theoretical motivation, the main experimental challenges, how LDMX addresses these challenges, and projected sensitivities. We further describe the capabilities of LDMX to explore other interesting new and standard physics, such as visibly-decaying axion and vector mediators or rare meson decays, and to provide timely electronuclear scattering measurements that will inform the modeling of neutrino-nucleus scattering for DUNE.

hep-ex↗

arXiv2023

DiffuseExpand: Expanding dataset for 2D medical image segmentation using diffusion models

Dataset expansion can effectively alleviate the problem of data scarcity for medical image segmentation, due to privacy concerns and labeling difficulties. However, existing expansion algorithms still face great challenges due to their inability of guaranteeing the diversity of synthesized images with paired segmentation masks. In recent years, Diffusion Probabilistic Models (DPMs) have shown powerful image synthesis performance, even better than Generative Adversarial Networks. Based on this insight, we propose an approach called DiffuseExpand for expanding datasets for 2D medical image segmentation using DPM, which first samples a variety of masks from Gaussian noise to ensure the diversity, and then synthesizes images to ensure the alignment of images and masks. After that, DiffuseExpand chooses high-quality samples to further enhance the effectiveness of data expansion. Our comparison and ablation experiments on COVID-19 and CGMH Pelvis datasets demonstrate the effectiveness of DiffuseExpand. Our code is released at https://github.com/shaoshitong/DiffuseExpand.

eess.IV↗

arXiv2023

Interactions of Particles with "Continuous Spin" Fields

Powerful general arguments allow only a few families of long-range interactions, exemplified by gauge field theories of electromagnetism and gravity. However, all of these arguments presuppose that massless fields have zero spin scale (Casimir invariant) and hence exactly boost invariant helicity. This misses the most general behavior compatible with Lorentz symmetry. We present a Lagrangian formalism describing interactions of matter particles with bosonic "continuous spin" fields with arbitrary spin scale $ρ$. Remarkably, physical observables are well approximated by familiar theories at frequencies larger than $ρ$, with calculable deviations at low frequencies and long distances. For example, we predict specific $ρ$-dependent modifications to the Lorentz force law and the Larmor formula, which lay the foundation for experimental tests of the photon's spin scale. We also reproduce known soft radiation emission amplitudes for nonzero $ρ$. The particles' effective matter currents are not fully localized to their worldlines when $ρ\neq 0$, which motivates investigation of manifestly local completions of our theory. Our results also motivate the development of continuous spin analogues of gravity and non-Abelian gauge theories. Given the correspondence with familiar gauge theory in the small $ρ$ limit, we conjecture that continuous spin particles may in fact mediate known long-range forces, with testable consequences.

hep-th↗

arXiv2022

"COVID-19 was a FIFA conspiracy #curropt": An Investigation into the Viral Spread of COVID-19 Misinformation

The outbreak of the infectious and fatal disease COVID-19 has revealed that pandemics assail public health in two waves: first, from the contagion itself and second, from plagues of suspicion and stigma. Now, we have in our hands and on our phones an outbreak of moral controversy. Modern dependency on social medias has not only facilitated access to the locations of vaccine clinics and testing sites but also-and more frequently-to the convoluted explanations of how "COVID-19 was a FIFA conspiracy"[1]. The MIT Media Lab finds that false news "diffuses significantly farther, faster, deeper, and more broadly than truth, in all categories of information, and by an order of magnitude"[2]. The question is, how does the spread of misinformation interact with a physical epidemic disease? In this paper, we estimate the extent to which misinformation has influenced the course of the COVID-19 pandemic using natural language processing models and provide a strategy to combat social media posts that are likely to cause widespread harm.

cs.CY↗

arXiv2022

Comment on "Poynting vector controversy in axion modified electrodynamics"

In a recent publication, Tobar, McAllister, and Goryachev claim that using an alternative definition of the Poynting vector can dramatically enhance the sensitivity of their proposed axion dark matter detector. While the choice of Poynting vector is indeed ambiguous, it cannot affect physical results, such as the reading on a voltmeter, as long as the same definition is used consistently. I explain this point in detail, and locate the specific errors in their calculation.

hep-ph↗

arXiv2022

Searches for New Particles, Dark Matter, and Gravitational Waves with SRF Cavities

This is a Snowmass white paper on the utility of existing and future superconducting cavities to probe fundamental physics. Superconducting radio frequency (SRF) cavity technology has seen tremendous progress in the past decades, as a tool for accelerator science. With advances spear-headed by the SQMS center at Fermilab, they are now being brought to the quantum regime becoming a tool in quantum science thanks to the high degree of coherence. The same high quality factor can be leveraged in the search for new physics, including searches for new particles, dark matter, including the QCD axion, and gravitational waves. We survey some of the physics opportunities and the required directions of R&D. Given the already demonstrated integration of SRF cavities in large accelerator systems, this R&D may enable larger scale searches by dedicated experiments.

hep-ph↗

arXiv2022

Probing Invisible Vector Meson Decays with NA64 and LDMX

Electron beam fixed target experiments such as NA64 and LDMX use missing energy-momentum to detect the production of dark matter and other long-lived states. The most studied production mechanism is dark Bremsstrahlung through a vector mediator. In this work, we explore a complementary source of missing energy-momentum signals: Bremsstrahlung photons can convert to hard vector mesons in exclusive photoproduction processes, which then decay to dark matter or other invisible particles, such as neutrinos. We find that existing NA64 data can improve the leading constraints on invisible light vector meson decays, while a future run of LDMX could improve them by up to $5$ orders of magnitude. For the examples of a dark photon and a $U(1)_B$ gauge boson mediator, accounting for meson decays substantially enhances these experiments' sensitivity, especially to thermal relic dark matter of mass $m_χ\gtrsim 0.1\ \mathrm{GeV}$.

hep-ph↗

arXiv2022

Stellar Shocks From Dark Matter Asteroid Impacts

Macroscopic dark matter is almost unconstrained over a wide "asteroid-like" mass range, where it could scatter on baryonic matter with geometric cross section. We show that when such an object travels through a star, it produces shock waves which reach the stellar surface, leading to a distinctive transient optical, UV and X-ray emission. This signature can be searched for on a variety of stellar types and locations. In a dense globular cluster, such events occur far more often than flare backgrounds, and an existing UV telescope could probe orders of magnitude in dark matter mass in one week of dedicated observation.

hep-ph↗

arXiv2020

Heterodyne Broadband Detection of Axion Dark Matter

We propose a new broadband search strategy for ultralight axion dark matter that interacts with electromagnetism. An oscillating axion field induces transitions between two quasi-degenerate resonant modes of a superconducting cavity. In two broadband runs optimized for high and low masses, this setup can probe unexplored parameter space for axion-like particles covering fifteen orders of magnitude in mass, including astrophysically long-ranged fuzzy dark matter.

hep-ph↗

arXiv2019

Axion Dark Matter Detection by Superconducting Resonant Frequency Conversion

We propose an approach to search for axion dark matter with a specially designed superconducting radio frequency cavity, targeting axions with masses $m_a \lesssim 10^{-6} \text{ eV}$. Our approach exploits axion-induced transitions between nearly degenerate resonant modes of frequency $\sim$ GHz. A scan over axion mass is achieved by varying the frequency splitting between the two modes. Compared to traditional approaches, this allows for parametrically enhanced signal power for axions lighter than a GHz. The projected sensitivity covers unexplored parameter space for QCD axion dark matter for $10^{-8} \text{ eV} \lesssim m_a \lesssim10^{-6} \text{ eV}$ and axion-like particle dark matter as light as $m_a \sim 10^{-14} \text{ eV}$.

hep-ph↗

arXiv2018

Learning to recognize Abnormalities in Chest X-Rays with Location-Aware Dense Networks

Chest X-ray is the most common medical imaging exam used to assess multiple pathologies. Automated algorithms and tools have the potential to support the reading workflow, improve efficiency, and reduce reading errors. With the availability of large scale data sets, several methods have been proposed to classify pathologies on chest X-ray images. However, most methods report performance based on random image based splitting, ignoring the high probability of the same patient appearing in both training and test set. In addition, most methods fail to explicitly incorporate the spatial information of abnormalities or utilize the high resolution images. We propose a novel approach based on location aware Dense Networks (DNetLoc), whereby we incorporate both high-resolution image data and spatial information for abnormality classification. We evaluate our method on the largest data set reported in the community, containing a total of 86,876 patients and 297,541 chest X-ray images. We achieve (i) the best average AUC score for published training and test splits on the single benchmarking data set (ChestX-Ray14), and (ii) improved AUC scores when the pathology location information is explicitly used. To foster future research we demonstrate the limitations of the current benchmarking setup and provide new reference patient-wise splits for the used data sets. This could support consistent and meaningful benchmarking of future methods on the largest publicly available data sets.

cs.CV↗

arXiv2018

Learning to Prune Filters in Convolutional Neural Networks

Many state-of-the-art computer vision algorithms use large scale convolutional neural networks (CNNs) as basic building blocks. These CNNs are known for their huge number of parameters, high redundancy in weights, and tremendous computing resource consumptions. This paper presents a learning algorithm to simplify and speed up these CNNs. Specifically, we introduce a "try-and-learn" algorithm to train pruning agents that remove unnecessary CNN filters in a data-driven way. With the help of a novel reward function, our agents removes a significant number of filters in CNNs while maintaining performance at a desired level. Moreover, this method provides an easy control of the tradeoff between network performance and its scale. Per- formance of our algorithm is validated with comprehensive pruning experiments on several popular CNNs for visual recognition and semantic segmentation tasks.

cs.CV↗

arXiv2017

Casimir Meets Poisson: Improved Quark/Gluon Discrimination with Counting Observables

Charged track multiplicity is among the most powerful observables for discriminating quark- from gluon-initiated jets. Despite its utility, it is not infrared and collinear (IRC) safe, so perturbative calculations are limited to studying the energy evolution of multiplicity moments. While IRC-safe observables, like jet mass, are perturbatively calculable, their distributions often exhibit Casimir scaling, such that their quark/gluon discrimination power is limited by the ratio of quark to gluon color factors. In this paper, we introduce new IRC-safe counting observables whose discrimination performance exceeds that of jet mass and approaches that of track multiplicity. The key observation is that track multiplicity is approximately Poisson distributed, with more suppressed tails than the Sudakov peak structure from jet mass. By using an iterated version of the soft drop jet grooming algorithm, we can define a "soft drop multiplicity" which is Poisson distributed at leading-logarithmic accuracy. In addition, we calculate the next-to-leading-logarithmic corrections to this Poisson structure. If we allow the soft drop groomer to proceed to the end of the jet branching history, we can define a collinear-unsafe (but still infrared-safe) counting observable. Exploiting the universality of the collinear limit, we define generalized fragmentation functions to study the perturbative energy evolution of collinear-unsafe multiplicity.

hep-ph↗

arXiv2017

Generalized Fragmentation Functions for Fractal Jet Observables

We introduce a broad class of fractal jet observables that recursively probe the collective properties of hadrons produced in jet fragmentation. To describe these collinear-unsafe observables, we generalize the formalism of fragmentation functions, which are important objects in QCD for calculating cross sections involving identified final-state hadrons. Fragmentation functions are fundamentally nonperturbative, but have a calculable renormalization group evolution. Unlike ordinary fragmentation functions, generalized fragmentation functions exhibit nonlinear evolution, since fractal observables involve correlated subsets of hadrons within a jet. Some special cases of generalized fragmentation functions are reviewed, including jet charge and track functions. We then consider fractal jet observables that are based on hierarchical clustering trees, where the nonlinear evolution equations also exhibit tree-like structure at leading order. We develop a numeric code for performing this evolution and study its phenomenological implications. As an application, we present examples of fractal jet observables that are useful in discriminating quark jets from gluon jets.

hep-ph↗

arXiv2017

Scene Labeling using Gated Recurrent Units with Explicit Long Range Conditioning

Recurrent neural network (RNN), as a powerful contextual dependency modeling framework, has been widely applied to scene labeling problems. However, this work shows that directly applying traditional RNN architectures, which unfolds a 2D lattice grid into a sequence, is not sufficient to model structure dependencies in images due to the "impact vanishing" problem. First, we give an empirical analysis about the "impact vanishing" problem. Then, a new RNN unit named Recurrent Neural Network with explicit long range conditioning (RNN-ELC) is designed to alleviate this problem. A novel neural network architecture is built for scene labeling tasks where one of the variants of the new RNN unit, Gated Recurrent Unit with Explicit Long-range Conditioning (GRU-ELC), is used to model multi scale contextual dependencies in images. We validate the use of GRU-ELC units with state-of-the-art performance on three standard scene labeling datasets. Comprehensive experiments demonstrate that the new GRU-ELC unit benefits scene labeling problem a lot as it can encode longer contextual dependencies in images more effectively than traditional RNN units.

cs.CV↗

arXiv2017

Minimum energetic cost to maintain a target nonequilibrium state

In the absence of external driving, a system exposed to thermal fluctuations will relax to equilibrium. However, the constant input of work makes it possible to counteract this relaxation, and maintain the system in a nonequilibrium steady state. In this Article, we use the stochastic thermodynamics of Markov jump processes to compute the minimum rate at which energy must be supplied and dissipated to maintain an arbitrary nonequilibrium distribution in a given energy landscape. This lower bound depends on two factors: the undriven probability current in the equilibrium state, and the distance from thermal equilibrium of the target distribution. By showing the consequences of this result in a few simple examples, we suggest general implications for the required energetic costs of macromolecular repair and cytosolic protein localization.

cond-mat.stat-mech↗

arXiv2013

Imperfect Construction of Microclusters

Microclusters are the basic building blocks used to construct cluster states capable of supporting fault-tolerant quantum computation. In this paper, we explore the consequences of errors on microcluster construction using two error models. To quantify the effect of the errors we calculate the fidelity of the constructed microclusters and the fidelity with which two such microclusters can be fused together. Such simulations are vital for gauging the capability of an experimental system to achieve fault tolerance.

quant-ph↗