Noise Regime Research Articles

Gaspery: Optimized Scheduling of Radial Velocity Follow-up Observations for Active Host Stars

Radial velocity (RV) follow-up is a critical complement of transiting exoplanet surveys like the Transiting Exoplanet Survey Satellite, both for validating discoveries of exoplanets and measuring their masses. Stellar activity introduces challenges to interpreting these measurements because the noise from the host star, which is often correlated in time, can result in high RV uncertainty. A robust understanding of stellar activity and how its timescales interact with the observing cadence can optimize limited RV resources. For this reason, in the era of oversubscribed, high-precision RV measurements, folding stellar activity timescales into the scheduling of observation campaigns is ideal. We present gaspery, an open-source code implementation to enable the optimization of RV observing strategies. Gaspery employs a generalized formulation of the Fisher information for RV time series, which also incorporates information about stellar correlated noise. We show that the information contained in an observing strategy can be significantly affected by beat frequencies between the orbital period of the planet, the stellar rotation period, and the observation epochs. We investigate how the follow-up observing strategy will affect the resulting RV uncertainty, as a function of stellar properties such as the spot decay timescale and rotation period. We then describe two example use cases for gaspery: (1) calculating the minimum number of observations to reach an uncertainty tolerance in a correlated noise regime and (2) finding an optimal strategy given a fixed observing budget. Finally, we outline a prescription for selecting an observing strategy that is generalizable to different targets.

Self-gated free-running 5D whole-heart MRI using blind source separation for automated cardiac motion extraction.

To compare two blind source separation (BSS) techniques to principal component analysis and the electrocardiogram for the identification of cardiac triggers in self-gated free-running 5D whole-heart MRI. To ascertain the precision and robustness of the techniques, they were compared in three different noise and contrast regimes. The repeated superior-inferior (SI) projections of a 3D radial trajectory were used to extract the physiological signals in three cardiac MRI cohorts: (1) 9 healthy volunteers without contrast agent injection at 1.5T, (2) 30 ferumoxytol-injected congenital heart disease patients at 1.5T, and (3) 12 gadobutrol-injected patients with suspected coronary artery disease at 3T. Self-gated cardiac triggers were extracted with the three algorithms (principal component analysis [PCA], second-order blind identification [SOBI], and independent component analysis [ICA]) and the difference with the electrocardiogram triggers was calculated. PCA and SOBI triggers were retained for image reconstruction. The image sharpness was ascertained on whole-heart 5D images obtained with PCA and SOBI and compared among the three cohorts. SOBI resulted in smaller trigger differences in Cohorts 1 and 3 compared to PCA (p < 0.01) and in all cohorts compared to ICA (p < 0.04). In Cohorts 1 and 3, the sharpness increased significantly in the reconstructed images when using SOBI instead of PCA (p < 0.03), but not in Cohort 2 (p = 0.4). We have shown that SOBI results in more precisely extracted self-gated triggers than PCA and ICA. The validation across three diverse cohorts demonstrates the robustness of the method against acquisition variability.

Experimental characterization of stall noise on a pitching airfoil in an anechoic wind tunnel

Dynamic stall noise is one of the potential source of amplitude modulations associated with wind turbine noise. This phenomenon is related to the periodic separation and reattachment of the boundary layer on the wind turbine blade suction side during its rotation. Within the framework of the PIBE project (Predicting the Impact of Wind Turbine Noise), experiments were conducted in an anechoic wind tunnel in order to characterize stall noise on a pitching airfoil in both static and dynamic conditions. A first set of measurements was performed using an airfoil equiped with pressure taps to measure both steady and unsteady wall pressure as well as far-field acoustic pressure. These measurements were used to identify the noise regimes associated with different angles of attack, and to relate them with the boundary layer evolution. In a second experiment, synchronized flow and acoustic measurements have been obtained to identify the coherent aerodynamic structures responsible for the noise radiation. The velocity fluctuations evaluated from the time-resolved particle image velocimetry (TR-PIV) data are correlated with acoustic pressure to localize aeroacoustic sources, and various tools such as vortex detection methods and spectral proper orthogonal decomposition are applied to identify the nature of these sources.

Quantum-classical separations in shallow-circuit-based learning with and without noises

An essential problem in quantum machine learning is to find quantum-classical separations between learning models. However, rigorous and unconditional separations are lacking for supervised learning. Here we construct a classification problem defined by a noiseless constant depth (i.e., shallow) quantum circuit and rigorously prove that any classical neural network with bounded connectivity requires logarithmic depth to output correctly with a larger-than-exponentially-small probability. This unconditional near-optimal quantum-classical representation power separation originates from the quantum nonlocality property that distinguishes quantum circuits from their classical counterparts. We further characterize the noise regimes for demonstrating such a separation on near-term quantum devices under the depolarization noise model. In addition, for quantum devices with constant noise strength, we prove that no super-polynomial classical-quantum separation exists for any classification task defined by Clifford circuits, independent of the structures of the circuits that specify the learning models.

Denoising Drug Discovery Data for Improved Absorption, Distribution, Metabolism, Excretion, and Toxicity Property Prediction.

Predicting absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of small molecules is a key task in drug discovery. A major challenge in building better ADMET models is the experimental error inherent in the data. Furthermore, ADMET predictors are typically regression tasks due to the continuous nature of the data, which makes it difficult to apply existing denoising methods from other domains as they largely focus on classification tasks. Here, we develop denoising schemes based on deep learning to address this. We find that the training error (TE) can be used to identify the noise in regression tasks while ensemble-based and forgotten event-based metrics fail to detect the noise. The most significant performance increase occurs when the original model is finetuned with the denoised data using TE as the noise detection metric. Our method has the ability to improve models with medium noise and does not degrade the performance of models with noise outside this range (low noise and high noise regimes). To our knowledge, our denoising scheme is the first to improve model performance for ADMET data and has implications for improving models for experimental assay data in general.

A statistical framework for recovering intensity mapping autocorrelations from cross-correlations

ABSTRACT Intensity mapping experiments will soon have surveyed large swathes of the sky, providing information about the underlying matter distribution of our early Universe. The resulting maps can be used to recover statistical information, such as the power spectrum, about the measured spectral lines (for example, H i, [C ii], and [O iii]). However precise power spectrum measurements, such as the 21 cm autocorrelation, continue to be challenged by the presence of bright foregrounds and non-trivial systematics. By cross-correlating different data sets, it may be possible to mitigate the effects of both foreground uncertainty and uncorrelated instrumental systematics. Beyond their own merit, cross-correlations could also be used to recover autocorrelation information. Such a technique has been proposed in the literature for recovering the 21 cm power spectrum. Generalizing this result, we develop a statistical framework for combining multiple cross-correlation signals in order to infer information about the corresponding autocorrelations. We do this first within the least squares estimator framework, and show how one can derive their estimator, along with several alternative estimators. We also investigate the posterior distribution of recovered autocorrelation and associated model parameters. We find that for certain noise regimes and cosmological signal modelling assumptions this procedure is effective at recovering autospectra from a set of cross-correlations. Finally, we showcase our framework in the context of several near-future line intensity mapping experiments.

Noise correlations behind superdiffusive quantum walks.

We study how discrete-time quantum walks behave under short-range correlated noise. By considering noise as a source of inhomogeneity of quantum gates, we introduce a primitive relaxation in the assumption of uncorrelated stochastic noise: binary pair correlations manifesting in the random distribution. Using different quantum gates, we examined the transport properties for both spatial and temporal noise regimes. For spatial inhomogeneities, we unveil noise correlations driving quantum walks from the well-known exponentially localized regime to superdiffusive spreading. This scenario displays an intriguing performance in which the superdiffusive exponent is almost invariant to the degree of inhomogeneity. The time-asymptotic regime and the finite-size scaling also unveil an emergent superdiffusive behavior for quantum walks undergoing temporal noise correlation, replacing the diffusive regime exhibited when noise is random and uncorrelated. However, some quantum gates are insensitive to correlations, contrasting with the spatial noise scenario. Numerical and analytical results provide valuable insights into the underlying mechanism of superdiffusive quantum walks, including those arising from deterministic aperiodic inhomogeneities.

Timescales of Autogenic Noise in River Bedform Evolution and Stratigraphy

AbstractBedform evolution and preserved cross strata are known to respond to floods. However, it is unclear if autogenic dynamics mask the flood signal in bedform evolution and cross strata. To address this, we characterize the temporal structure of autogenic noise in steady‐state bedform evolution in a physical experiment. Results reveal the existence of bedform groups—quasi‐stable collections of bedforms—that migrate at a similar speed as bedforms. We find that bedform and bedform‐group turnover timescales are the key autogenic timescales of bed evolution that set the transition time‐periods between different noise regimes in bedform evolution. Results suggest that bedform‐group turnover timescale sets the lower limit for detecting flood signals in bedform evolution, and floods with duration shorter than bedform turnover timescale can be severely degraded in bedform evolution and cross strata. Our work provides a new framework for interrogating fluvial cross strata for reconstruction of past floods.

Quantum games under Markovian and non-Markovian quantum noise regime and the nature of phase transitions

Quantum games under Markovian and non-Markovian quantum noise regime and the nature of phase transitions

Measurement of Small Photon Numbers in Circuit QED Resonators.

Off-resonant interaction of fluctuating photons in a resonator with a qubit increases the qubit dephasing rate. We use this effect to measure a small average number of intracavity photons that are coherently or thermally driven. For spectral resolution, we do this by subjecting the qubit to a Carr-Purcell-Meiboom-Gill sequence and record the qubit dephasing rate for various periods between qubit π pulses. The recorded data is then analyzed with formulas for the photon-induced dephasing rate derived for the non-Gaussian noise regime with an arbitrary ratio of the resonator dispersive shift to decay rate. We show that the presented Carr-Purcell-Meiboom-Gill dephasing rate formulas agree well with experimental results and demonstrate measurement of thermal and coherent photon populations at the level of a few 10^{-4}.

Population health implications of exposure to pervasive military aircraft noise pollution.

While the adverse health effects of civil aircraft noise are relatively well studied, impacts associated with more intense and intermittent noise from military aviation have been rarely assessed. In recent years, increased training at Naval Air Station Whidbey Island, USA has raised concerns regarding the public health and well-being implications of noise from military aviation. This study assessed the public health risks of military aircraft noise by developing a systematic workflow that uses acoustic and aircraft operations data to map noise exposure and predict health outcomes at the population scale. Acoustic data encompassing seven years of monitoring efforts were integrated with flight operations data for 2020-2021 and a Department of Defense noise simulation model to characterize the noise regime. The model produced contours for day-night, nighttime, and 24-h average levels, which were validated by field monitoring and mapped to yield the estimated noise burden. Established thresholds and exposure-response relationships were used to predict the population subject to potential noise-related health effects, including annoyance, sleep disturbance, hearing impairment, and delays in childhood learning. Over 74,000 people within the area of aircraft noise exposure were at risk of adverse health effects. Of those exposed, substantial numbers were estimated to be highly annoyed and highly sleep disturbed, and several schools were exposed to levels that place them at risk of delay in childhood learning. Noise in some areas exceeded thresholds established by federal regulations for public health, residential land use and noise mitigation action, as well as the ranges of established exposure-response relationships. This study quantified the extensive spatial scale and population health burden of noise from military aviation.We employed a novel GIS-based workflow for relating mapped distributions of aircraft noise exposure to a suite of public health outcomes by integrating acoustic monitoring and simulation data with a dasymetric population density map. This approach enables the evaluation of population health impacts due to past, current, and future proposed military operations. Moreover, itcanbemodified for application to other environmental noise sources and offers an improved open-sourcetool to assess the population health implications of environmental noise exposure, inform at-risk communities, and guide efforts in noise mitigation and policy governing noise legislation, urban planning, and land use.

Exact solutions for travelling waves using Tanh method for two dimensional stochastic Allen–Cahn equation with multiplicative noise

AbstractThe research outlined here focuses on the hyperbolic tangent (tanh) method for deriving analytical solutions for travelling wave to the two-dimensional stochastic Allen–Cahn equation with multiplicative noise. The novelty of our work is to derive these exact solutions since the previous studies focused on the solutions of numerical nature. The tanh approach, which employs a finite tanh power series, is particularly adept at modeling travelling wave profiles. A key area of interest in this study is the effect of multiplicative noise on these travelling waves dynamics, especially how high levels of noise can lead to propagation failure of waves. The results demonstrate that for weak noise, the propagation of the travelling wave is basically unaffected, while the wave fails to propagate in the strong noise regime.

Dynamics of a Quantum System Interacting with White Non-Gaussian Baths: Poisson Noise Master Equation.

Quantum systems are unavoidably open to their surrounding degrees of freedom. The theory of open quantum systems is thus crucial to understanding the fluctuations, dissipation, and decoherence of a quantum system of interest. Typically, the bath is modeled as an ensemble of harmonic oscillators, which yields Gaussian statistics of the bath influence on the quantum systems. However, there are also phenomena in which the bath consists of two-state systems, spins, or anharmonic oscillators; therefore, the non-Gaussian properties of the bath become important. Nevertheless, a theoretical framework to describe quantum systems under the influence of such non-Gaussian baths is not well established. Here, we develop a theory to describe quantum dissipative systems affected by Poisson noise properties of the bath, because the Lévi-Itô decomposition theorem asserts that Poisson noise is fundamental in describing arbitrary white noise beyond Gaussian properties. We introduce a quantum bath model that allows for the consistent description of dissipative quantum systems. The obtained master equation reveals non-Gaussian bath effects in the white noise regime, and provides an essential step toward describing open quantum dynamics under the influence of generic baths.

A phase diagram structure determines the optimal sensitivity-precision trade-off in signaling systems

Signal transduction is crucial for many biological functions. However, it is still unclear how signaling systems function accurately under noisy situations. More specifically, such systems operate in a regime of low response noise, while maintaining high sensitivity to signals. To gain further insight on this regime, here we derive a fundamental trade-off between response sensitivity and precision in biological signaling processes under the static noise condition. We find that the optimal trade-off in signaling networks can be better characterized by a phase diagram structure rather than topological structures. We confirm that the patterning network of early Drosophila embryos agrees with our derived relationship, and apply the optimal phase diagram structure to quantitatively predict the patterning position shifts of the downstream genes, including hunchback, Krüppel, giant, knirps and even-skipped, upon the dosage perturbation of the morphogen Bicoid.

Quantum error mitigation in the regime of high noise using deep neural network: Trotterized dynamics

Quantum error mitigation in the regime of high noise using deep neural network: Trotterized dynamics

Directed Graph Mapping exceeds Phase Mapping for the detection of simulated 2D meandering rotors in fibrotic tissue with added noise

Cardiac arrhythmias such as atrial fibrillation (AF) are recognised to be associated with re-entry or rotors. A rotor is a wave of excitation in the cardiac tissue that wraps around its refractory tail, causing faster-than-normal periodic excitation. The detection of rotor centres is of crucial importance in guiding ablation strategies for the treatment of arrhythmia. The most popular technique for detecting rotor centres is Phase Mapping (PM), which detects phase singularities derived from the phase of a signal. This method has been proven to be prone to errors, especially in regimes of fibrotic tissue and temporal noise. Recently, a novel technique called Directed Graph Mapping (DGM) was developed to detect rotational activity such as rotors by creating a network of excitation. This research aims to compare the performance of advanced PM techniques versus DGM for the detection of rotors using 64 simulated 2D meandering rotors in the presence of various levels of fibrotic tissue and temporal noise. Four strategies were employed to compare the performances of PM and DGM. These included a visual analysis, a comparison of F2-scores and distance distributions, and calculating p-values using the mid-p McNemar test. Results indicate that in the case of low meandering, fibrosis and noise, PM and DGM yield excellent results and are comparable. However, in the case of high meandering, fibrosis and noise, PM is undeniably prone to errors, mainly in the form of an excess of false positives, resulting in low precision. In contrast, DGM is more robust against these factors as F2-scores remain high, yielding F2≥0.931 as opposed to the best PM F2≥0.635 across all 64 simulations.

Linear inverse problems with nonnegativity constraints: Singularity of optimisers

We look at continuum solutions in optimisation problems associated to linear inverse problems $ y = Ax $ with non-negativity constraint $ x \geq 0 $. We focus on the case where the noise model leads to maximum likelihood estimation through general divergences, which cover a wide range of common noise statistics such as Gaussian and Poisson. Considering $ x $ as a Radon measure over the domain on which the reconstruction is taking place, we show a general singularity result. In the high noise regime corresponding to $ y \notin\{A x \mid x \geq 0\} $ and under a key assumption on the divergence as well as on the operator $ A $, any optimiser has a singular part with respect to the Lebesgue measure. We hence provide an explanation as to why any possible algorithm successfully solving the optimisation problem will lead to undesirably spiky-looking images when the image resolution gets finer, a phenomenon well documented in the literature. We illustrate these results with several numerical examples inspired by medical imaging.

NA-SODINN: A deep learning algorithm for exoplanet image detection based on residual noise regimes

Context. Supervised deep learning was recently introduced in high-contrast imaging (HCI) through the SODINN algorithm, a con-volutional neural network designed for exoplanet detection in angular differential imaging (ADI) datasets. The benchmarking of HCI algorithms within the Exoplanet Imaging Data Challenge (EIDC) showed that (i) SODINN can produce a high number of false positives in the final detection maps, and (ii) algorithms processing images in a more local manner perform better. Aims. This work aims to improve the SODINN detection performance by introducing new local processing approaches and adapting its learning process accordingly. Methods. We propose NA-SODINN, a new deep learning binary classifier based on a convolutional neural network (CNN) that better captures image noise correlations in ADI-processed frames by identifying noise regimes. The identification of these noise regimes is based on a novel technique, named PCA-pmaps, which allowed us to estimate the distance from the star in the image from which background noise started to dominate over residual speckle noise. NA-SODINN was also fed with local discriminators, such as signal-to-noise ratio (S/N) curves, which complement spatio-temporal feature maps during the model’s training. Results. Our new approach was tested against its predecessor, as well as two SODINN-based hybrid models and a more standard annular-PCA approach, through local receiving operating characteristics (ROC) analysis of ADI sequences from the VLT/SPHERE and Keck/NIRC-2 instruments. Results show that NA-SODINN enhances SODINN in both sensitivity and specificity, especially in the speckle-dominated noise regime. NA-SODINN is also benchmarked against the complete set of submitted detection algorithms in EIDC, in which we show that its final detection score matches or outperforms the most powerful detection algorithms. Conclusions. Throughout the supervised machine learning case, this study illustrates and reinforces the importance of adapting the task of detection to the local content of processed images.

The most likely transition path for a class of distribution-dependent stochastic systems

Distribution-dependent stochastic dynamical systems arise widely in engineering and science. We consider a class of such systems which model the limit behaviors of interacting particles moving in a vector field with random fluctuations. We aim to examine the most likely transition path between equilibrium stable states of the vector field. In the small noise regime, the action functional does not involve the solution of the skeleton equation which describes the unperturbed deterministic flow of the vector field shifted by the interaction at zero distance. As a result, we are led to study the most likely transition path for a stochastic differential equation without distribution dependency. This enables the computation of the most likely transition path for these distribution-dependent stochastic dynamical systems by the adaptive minimum action method and we illustrate our approach in two examples.

Monolithic VECSEL for stable kHz linewidth.

Vertical-external-cavity surface-emitting semiconductor lasers (VECSELs) are of increasing interest for applications requiring ultra-coherence and/or low noise at novel wavelengths; performance that is currently achieved via high-Q, air-spaced resonators to achieve long intra-cavity photon lifetimes (for the so-called class-A low noise regime), power scaling and high beam quality. Here, we report on the development of a compact, electronically tunable, monolithic-cavity, class-A VECSEL (monolithic VECSEL) for ultra-narrow free-running linewidths. A multi-quantum-well, resonant periodic gain structure with integrated distributed Bragg reflector (DBR) was optically-bonded to an air-gap-free laser resonator created inside a right-angle fused-silica prism to suppress the influence of environmental noise on the external laser oscillation, thus achieving high stability. Mode-hop-free wavelength tuning is performed via the stabilized temperature; or electronically, and with low latency, via a shear piezo-electric transducer mounted on the top of the prism. The free-running linewidth, estimated via the frequency power spectral density (PSD), is sub-kHz over ms timescales and <1.9 kHz for time sampling as long as 1s, demonstrating at least two orders-of-magnitude improvement in noise performance compared to previously reported single frequency VECSELs. The stable, total internal reflection resonator concept is akin to the prevalent monolithic non-planar ring oscillator (NPRO), however the monolithic VECSEL has several important advantages: tailored emission wavelength (via semiconductor bandgap engineering), no relaxation oscillations, no applied magnetic field, and low requirements on the pump beam quality. This approach is power-scalable in principle and could be applied to VECSELs at any of the wavelengths from the visible to the mid-infrared at which they are already available, to create a range of robust, ultra-coherent laser systems with reduced bulkiness and complexity. This is of particular interest for remote metrology and the translation of quantum technologies, such as optical clocks, from research laboratories into real world applications.