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At least 217 records · Page 12

Unraveling cosmological anisotropies within stochastic gravitational wave backgrounds

Identifying the anisotropies in a cosmologically sourced stochastic gravitational wave background (SGWB) would be of significance in shedding light on the nature of primordial inhomogeneities. For example, if SGWB carries isocurvature fluctuations, it would provide evidence for a multi-field inflationary origin of these inhomogeneities. However, this is challenging in practice due to finite detector sensitivity and also the presence of the astrophysical foregrounds that can compete with the cosmological signal. In this work, we explore the prospects for measuring cosmological SGWB anisotropies in the presence of an astrophysical counterpart and detector noise. To illustrate the main idea, we perform a Fisher analysis using a well-motivated cosmological SGWB template corresponding to a first order phase transition, and an astrophysical SGWB template corresponding to extra-galactic binary mergers, and compute the uncertainty with which various parameters characterizing the isotropic and anisotropic components can be extracted. Finally, we also discuss some subtleties and caveats involving shot noise in the astrophysical foreground. Overall, we show that upcoming experiments, e.g., LISA, Taiji, Einstein Telescope, Cosmic Explorer, and BBO, can all be effective in discovering plausible anisotropic cosmological SGWBs.

79 ASTRONOMY AND ASTROPHYSICS↗

Triton Field Trials (TFiT) underwater noise - University of New Hampshire Living Bridge turbine Processed Data

In July 2021, a commercial-off-the-shelf hydrophone was deployed in a free-drifting configuration to measure underwater acoustic emissions and characterize a 25 kW-rated tidal turbine at the University of New Hampshire's Living Bridge Project in Portsmouth, New Hampshire. Sampling methods and analysis were performed in alignment with the recently published IEC 62600-40 Technical Specification for acoustic characterization of marine energy converters. Results from this study indicate acoustic emissions from the turbine were below ambient sound levels and therefore did not have a significant impact on the underwater noise levels of the project site. As a component of Pacific Northwest National Laboratory's Triton Field Trials (TFiT) described in a paper published in a Special Issue of Journal of Marine Energy Science and Engineering, this study provides a valuable use case for the IEC 62600-40 Technical Specification framework and further recommendations for cost-effective technologies and methods for measuring underwater noise at future current energy converter project sites. The paper can be accessed in the link bellow.

16 TIDAL AND WAVE POWER↗

Variational quantum state preparation for quantum-enhanced metrology in noisy systems

Here, we investigate optimized quantum state preparation for quantum metrology applications in noisy environments. Using the QFI-OPT package, we simulate a low-depth variational quantum circuit (VQC) composed of a sequence of global rotations and entangling operations applied to a chain of qubits that are subject to dephasing noise. The parameters controlling the VQC are numerically optimized to maximize the quantum Fisher information, which characterizes the ultimate metrological sensitivity of a quantum state with respect to a global rotation. We find that, regardless of the details of the entangling operation implemented in the VQC, the optimal quantum states can be broadly classified into a trio of qualitative regimes, i.e., catlike, squeezed like, and product states, associated with different dephasing rates. Our findings are relevant for designing optimal state-preparation strategies for next-generation quantum sensors exploiting entanglement, such as time and frequency standards and magnetometers, aimed at achieving state-of-the-art performance in the presence of noise and decoherence.

quantum Fisher information↗

Simulation study for particle identification with the dRICH of the ePIC experiment at the EIC

The dual-radiator Imaging Cherenkov detector (dRICH), employing an aerogel and a gas radiator, is a key component of the forward particle identification system for the ePIC experiment at the Electron-Ion Collider (EIC). This study evaluates the dRICH performance using Geant4 simulations in the context of the global ePIC simulation stack, focusing on the optimization of the aerogel radiator and the impact of sensor noise. We compare two aerogel configurations: the initial design (n = 1.019) and the current default (n = 1.026). The latter, characterized by improved optical properties and a higher refractive index, demonstrates enhanced π/K separation at high momenta, effectively extending the operational overlap with the C 2 F 6 gas radiator (n = 1.00076 at 25 °C, as implemented in the simulation software). Additionally, the study investigates the impact of Silicon Photomultiplier (SiPM) dark noise, showing that a 300 kHz noise rate per 3mm x 3mm channel leads to a moderate reduction (approximately 1.5 GeV/c) in the 3σ separation threshold. These results validate the current dRICH design and quantify the purity levels achievable for both radiators under expected experimental conditions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Sensing Few Electrons Floating on Helium with High-Electron-Mobility Transistors

We report on low-frequency measurements of few electrons floating on superfluid helium using a bespoke cryogenic cascode amplifier circuit built with off-the-shelf GaAs high-electron-mobility transistors (HEMTs). We integrate this circuit with a charge-coupled device (CCD) to transport the electrons on helium and characterize its performance. Here, we show that this circuit has a signal-to-noise ratio (SNR) of ~ 2 $\frac{e}{√Hz}$ at 102 kHz, an order of magnitude improvement from previous implementations, and provides a compelling alternative to few electron sensing with high-frequency resonators.

Charge-Coupled Devices↗

Materials Fingerprinting Classification

Significant progress in many classes of materials could be made with the availability of experimentally-derived large datasets composed of atomic identities and three-dimensional coordinates. Methods for visualizing the local atomic structure, such as atom probe tomography (APT), which routinely generate datasets comprised of millions of atoms, are an important step in realizing this goal. However, state-of-the-art APT instruments generate noisy and sparse datasets that provide information about elemental type, but obscure atomic structures, thus limiting their subsequent value for materials discovery. The application of a materials fingerprinting process, a machine learning algorithm coupled with topological data analysis, provides an avenue by which here-to-fore unprecedented structural information can be extracted from an APT dataset. As a proof of concept, the material fingerprint is applied to high-entropy alloy APT datasets containing body-centered cubic (BCC) and face-centered cubic (FCC) crystal structures. A local atomic configuration centered on an arbitrary atom is assigned a topological descriptor, with which it can be characterized as a BCC or FCC lattice with near perfect accuracy, despite the inherent noise in the dataset. Finally, this successful identification of a fingerprint is a crucial first step in the development of algorithms which can extract more nuanced information, such as chemical ordering, from existing datasets of complex materials.

36 MATERIALS SCIENCE↗

Why does a phosphonium-phosphinate ionic liquid protect the contact surfaces from wear and micropitting but increase vibration when used as an additive in rolling-sliding lubrication?

A phosphonium-phosphinate ionic liquid (IL) was studied as a lubricant additive for rolling-sliding contacts. The bench-scale test was designed to simulate automotive rear axle operation during cold start, highway towing, and overload conditions. Adding such an IL (2%) into a base oil significantly reduced wear loss and rolling contact fatigue, e.g., microcracking and micropitting, but made the vibrational noise notably higher under a low (-1.5%) sliding roll ratio (SRR). Additionally, worn surface characterization revealed an interesting texture pattern with alternating smoother plateaus and rougher valleys, which is believed to cause the high vibration. No increased vibration was observed at a high (-30%) SRR, possibly because the more aggressive sliding abrasion prevented such a surface texture from forming.

36 MATERIALS SCIENCE↗

CovTransformer: A transformer model for SARS-CoV-2 lineage frequency forecasting

With hundreds of SARS-CoV-2 lineages circulating in the global population, there is an ongoing need for predicting and forecasting lineage frequencies and thus identifying rapidly expanding lineages. Accurate prediction would allow for more focused experimental efforts to understand pathogenicity of future dominating lineages and characterize the extent of their immune escape. Here, we first show that the inherent noise and biases in lineage frequency data make a commonly-used regression-based approach unreliable. To address this weakness, we constructed a machine learning model for SARS-CoV-2 lineage frequency forecasting, called CovTransformer, based on the transformer architecture. We designed our model to navigate challenges such as a limited amount of data with high levels of noise and bias. We first trained and tested the model using data from the UK and the USA, and then tested the generalization ability of the model to many other countries and US states. Remarkably, the trained model makes accurate predictions two months into the future with high levels of accuracy both globally (in 31 countries with high levels of sequencing effort) and at the US-state level. Our model performed substantially better than a widely used forecasting tool, the multinomial regression model implemented in Nextstrain, demonstrating its utility in SARS-CoV-2 monitoring. Assuming a newly emerged lineage is identified and assigned, our test using retrospective data shows that our model is able to identify the dominating lineages 7 weeks in advance on average before they became dominant. Overall, our work demonstrates that transformer models represent a promising approach for SARS-CoV-2 forecasting and pandemic monitoring.

60 APPLIED LIFE SCIENCES↗

Dark counts in optical superconducting transition-edge sensors for rare-event searches

Superconducting transition-edge sensors (TESs) are a type of quantum sensor known for their high single-photon detection efficiency and low background. This makes TESs ideal for particle-physics experiments searching for rare events. In this work, we present a comprehensive characterization of the background in optical TESs, distinguishing three types of events: electrical-noise, high-energy, and photonlike events. We introduce computational methods to automate the classification of events. We experimentally verify and simulate the source of the high-energy events. We also isolate the photonlike events, the expected signal in dielectric haloscopes searching for dark-matter dark photons, and achieve a photonlike dark-count rate of 3.6× 10 − 4 in the 0.8–3.2 eV energy range.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Classical Non-Markovian Noise in Symmetry-Preserving Quantum Dynamics

In quantum dynamics, symmetries are vital for identifying and assessing conserved quantities that govern the evolution of a quantum system. When promoted to the open quantum system setting, dynamical symmetries can be negatively altered by system-environment interactions, thus, complicating their analysis. Previous work on noisy symmetric quantum dynamics has focused on the Markovian setting, despite the ubiquity of non-Markovian noise in a number of widely used quantum technologies. Here, in this Letter, we develop a framework for quantifying the impact of non-Markovian noise on symmetric quantum evolution via root space decompositions and the filter function formalism. We demonstrate analytically that symmetry-preserving noise maintains the symmetric subspace, while nonsymmetric noise leads to highly specific leakage errors that are block diagonal in the symmetry representation. We support our findings with numerical studies of a transverse-field Ising model and a quantum error detecting code subject to spatiotemporally correlated multiaxis noise. Our results are broadly applicable, providing new analytic insights into the control and characterization of open quantum system dynamics.

decoherence↗

Modeling Noise: Paths toward AI-Enabled Stochastic Earth System Models and Parameterizations

Some of the key challenges in Earth system prediction arise from an uncertain representation of unpredictable natural variability, across time and space scales. This variability, hereafter referred to as noise, represents the quantity against which the strength of a signal of interest is measured to assess predictability. Underestimating or overestimating this noise in Earth system models can lead to issues ranging between overconfidence in an erroneous prediction and a lack thereof in an otherwise accurate one. Noise is part of the physical processes in the Earth system and can amplify or damp a signal of interest in a complex manner that is not systematically characterized. Specifically, while there has been significant progress in our understanding of multiscale interactions among known modes of variability (potentially predictable signals), how these are impacted by the noise is not clear. Recent effort toward stochastic parametrization schemes that provide a representation of noise due to uncertain sub-grid processes in climate models (Berner et al., 2017 and references therein) has shown promise not only in reducing biases and improving probabilistic prediction but also in improving the representation of natural variability in the model toward what is observed. However, today’s standard schemes for stochastic parametrization are still simplistic as they do not take the state of the atmosphere into account (other than by the use of multiplicative noise for model tendencies). Furthermore they require hand-tuning and trial-and-error testing for the magnitude of the added noise, which is difficult in a chaotic system that is as complex as the Earth system.

58 GEOSCIENCES↗

DarkNESS: Characterization of Space Multi-Chip Module Skipper-CCDs

The DarkNESS mission is to send a 6U CubeSat fitted with an array of skipper-CCDs to see how well these sensors operate in Low Earth Orbit: take measurements of x-rays from the galactic center and strongly interacting sub-GeV dark matter from Cygnus X-1. Skipper-CCDs allow for low-noise astronomical observations which can extend the discovery reach for direct dark matter search experiments. This paper will discuss the characterization of the space Multi-Chip Module (sMCM) equipped with skipper-CCDs, and the challenges it faces during testing in the lab environment.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Essential barrier height and a probabilistic approach in characterizing potential landscape

In this work we propose a probabilistic approach to investigate the shape of landscapes of multi-dimensional potential functions. Under a suitable coupling scheme, two copies of the overdamped Langevin dynamics associated with the potential function are coupled, and the coupling times are collected. Assuming a set of intuitive yet technically challenging conditions on the coupling scheme, it is shown that the tail distributions of the coupling times exhibit qualitatively different dependencies on the noise magnitude for single-well versus multi-well potential functions. More specifically, for convex single-well potentials, the negative tail exponent of the coupling time distribution is uniformly bounded away from zero by the convexity parameter and is independent of the noise magnitude. In contrast, for multi-well potentials, the negative tail exponent decreases exponentially as the noise vanishes, with the decay rate governed by the essential barrier height, a quantity introduced in this paper to characterize the non-convex nature of the potential function. Numerical investigations are conducted for a variety of examples, including the Rosenbrock function, interacting particle systems, and loss functions arising in artificial neural networks. These examples not only illustrate the theoretical results in various contexts but also provide crucial numerical validation of the conjectured assumptions, which are essential to the theoretical analysis yet lie beyond the reach of standard technical tools.

97 MATHEMATICS AND COMPUTING↗

Improved noise performance from the next-generation buried-channel p-MOSFET SiSeROs

The Single electron Sensitive Read Out (SiSeRO) is a novel on-chip charge detector output stage for charge-coupled device (CCD) image sensors. Developed at MIT Lincoln Laboratory, this technology uses a p-MOSFET transistor with a depleted internal gate beneath the transistor channel. The transistor source-drain current is modulated by the transfer of charge into the internal gate. At Stanford, we have developed a readout module based on the drain current of the on-chip transistor to characterize the device. In our earlier work, we characterized a number of first prototype SiSeROs with the MOSFET transistor channels at the surface layer. An equivalent noise charge (ENC) of around 15 electrons root mean square (RMS) was obtained. In this work, we examine the first buried-channel SiSeRO. Here we have achieved substantially improved noise performance of around 4.5 electrons root mean square (RMS) and a full width half maximum (FWHM) energy resolution of 132 eV at 5.9 keV, for a readout speed of 625 kpixel/s. We also discuss how digital filtering techniques can be used to further improve the SiSeRO noise performance. Additional measurements and device simulations will be essential to further mature the SiSeRO technology. This new device class presents an exciting new technology for the next-generation astronomical X-ray telescopes requiring fast, low-noise, radiation-hard megapixel imagers with moderate spectroscopic resolution.

79 ASTRONOMY AND ASTROPHYSICS↗

Five-second STEM dislocation tomography for 300 nm thick specimen assisted by deep-learning-based noise filtering

Scanning transmission electron microscopy (STEM) is suitable for visualizing the inside of a relatively thick specimen than the conventional transmission electron microscopy, whose resolution is limited by the chromatic aberration of image forming lenses, and thus, the STEM mode has been employed frequently for computed electron tomography based three-dimensional (3D) structural characterization and combined with analytical methods such as annular dark field imaging or spectroscopies. However, the image quality of STEM is severely suffered by noise or artifacts especially when rapid imaging, in the order of millisecond per frame or faster, is pursued. Here we demonstrate a deep-learning-assisted rapid STEM tomography, which visualizes 3D dislocation arrangement only within five-second acquisition of all the tilt-series images even in a 300 nm thick steel specimen. The developed method offers a new platform for various in situ or operando 3D microanalyses in which dealing with relatively thick specimens or covering media like liquid cells are required.

47 OTHER INSTRUMENTATION↗

Origin and characterization of single-electron events in Skipper-CCDs for light dark matter searches

This manuscript summarizes the work carried out within the SENSEI (Sub-Electron-Noise Skipper-CCD Experimental Instrument) collaboration, an effort focused on the search for light dark matter using Skipper-CCDs. Firstly, it presents the data acquisition and processing protocol developed to establish a selection criteria for events compatible with the dark matter signal. This protocol also enabled the structure and tools used for the study and characterization of phenomena that impact the quality of measurements made in the search for dark matter (measurement of the Fano factor, determination of the Compton background, and characterization of the high-energy surface background), which were carried out during this work. Secondly, it discusses the comprehensive study conducted on the origin of single-electron events in Skipper-CCDs, which allowed for the selection of detector operating parameters and improved the detection sensitivity in the search for dark matter. It is highlighted the characterization of the spatial correlation between single-electron events and high-energy events, among other developed event selection criteria, and the optimization of the output device to reduce sources of luminescence during its operation. As a result of this study, the lowest levels of dark current and spurious charge ever measured in a CCD are reported. Finally, the latest published results from SENSEI are presented. The obtained limits for the scattering of a light mediator are the best reported to date across the entire investigated mass range, while for the heavy mediator, they are the best for masses below 10 MeV. In terms of dark matter absorption, the most restringent limits below 10 eV are reported. These results, achieved with only a very small fraction of the total projected exposure for SENSEI, position Skipper-CCDs as leaders in the search for light dark matter worldwide.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Noise robust detection of quantum phase transitions

Quantum computing allows for the manipulation of highly correlated states whose properties quickly go beyond the capacity of any classical method to calculate. Thus one natural problem which could lend itself to quantum advantage is the study of ground-states of condensed matter models, and the transitions between them. However, current levels of hardware noise can require extensive application of error-mitigation techniques to achieve reliable computations. In this work, we use several IBM devices to explore a finite-size spin model with multiple “phaselike” regions characterized by distinct ground-state configurations. Using preoptimized Variational Quantum Eigensolver (VQE) solutions, we demonstrate that in contrast to calculating the energy, where zero-noise extrapolation is required in order to obtain qualitatively accurate yet still unreliable results, calculations of the energy derivative, two-site spin correlation functions, and the fidelity susceptibility yield accurate behavior across multiple regions, even with minimal or no application of error-mitigation approaches. Taken together, these sets of observables could be used to identify level crossings in a simple, noise-robust manner which is agnostic to the method of ground state preparation. This work shows promising potential for near-term application to identifying quantum phase transitions, including avoided crossings and nonadiabatic conical intersections in electronic structure calculations. Published by the American Physical Society 2024

Lively, Kevin (ORCID:0000000320981494)↗

Commissioning the H i Observing Mode of the Beam Former for the Cryogenically Cooled Focal L-band Array for the GBT (FLAG)

We present the results of commissioning observations for a new digital beam-forming back end for the Focal plane L-band Array for the Robert C. Byrd Green Bank Telescope (FLAG), a cryogenically cooled Phased Array Feed (PAF) with the lowest measured T {sub sys}/η of any PAF outfitted on a radio telescope to date. We describe the custom software used to apply beam-forming weights to the raw element covariances to create research-quality spectral-line images for the new fine-channel mode, study the stability of the beam weights over time, characterize FLAG’s sensitivity over a frequency range of 150 MHz, and compare the measured noise properties and observed distribution of neutral hydrogen emission from several extragalactic and Galactic sources with data obtained with the current single-pixel L-band receiver. These commissioning runs establish FLAG as the preeminent PAF receiver currently available for spectral-line observations on the world’s major radio telescopes.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗