Stochastic Signals Research Articles

Constraints on the Primordial Curvature Power Spectrum and Reheating Temperature from the NANOGrav 15-Year Dataset

The stochastic signal observed by collaborations such as NANOGrav, PPTA, EPTA +InPTA, and CPTA may originate from gravitational waves induced by primordial curvature perturbations during inflation. This study investigates small-scale properties of inflation and reheating, assuming a log-normal form for the power spectrum of the primordial curvature and a reheating phase equation of state w=1/9. Inflation and reheating scenarios are thoroughly examined using Bayesian methods applied to the NANOGrav 15-year dataset. The analysis establishes constraints on the reheating temperature, suggesting Trh≳0.1Gev, consistent with Big Bang nucleosynthesis constraints. Additionally, the NANOGrav 15-year dataset requires the amplitude (A∼0.1) and width (Δ≲0.001) of the primordial curvature power spectrum to be within specific ranges. A notable turning point in the energy density of scalar-induced gravitational waves occurs due to a change in the equation of state w. This turning point signifies a transition from the reheating epoch to radiation domination. Further observations of scalar-induced gravitational waves could provide insights into the precise timing of this transition, enhancing our understanding of early Universe dynamics.

Gravitational wave signals from early matter domination: interpolating between fast and slow transitions

An epoch of matter domination in the early universe can enhance the primordial stochastic gravitational wave signal, potentially making it detectable to upcoming gravitational wave experiments. However, the resulting gravitational wave signal is quite sensitive to the end of the early matter-dominated epoch. If matter domination ends gradually, a cancellation results in an extremely suppressed signal, while in the limit of an instantaneous transition, there is a resonant-like enhancement. The end of the matter dominated epoch cannot be instantaneous, however, and previous analyses have used a Gaussian smoothing technique to account for this, and consider only a limited regime around the fast transition limit. In this work, we present a study of the enhanced gravitational wave signal from early matter domination without making either approximation and show how the signal smoothly evolves from the strongly suppressed to strongly enhanced regimes.

Exploring the NANOGrav signal and planet-mass primordial black holes through Higgs inflation

The data recently released by the North American Nanohertz Observatory for Gravitational Waves (NANOGrav) provides compelling evidence supporting the existence of a stochastic signal that aligns with a gravitational-wave background. We show that the scalar-induced gravitational waves from the Higgs inflation model with the parametric amplification mechanism can explain this signal. Such a gravitational-wave background naturally predicts the substantial existence of planet-mass primordial black holes, which can be planet 9 in our solar system and the lensing objects for the ultrashort-timescale microlensing events observed by the Optical Gravitational Lensing Experiment. Therefore, the NANOGrav signal, the potential Planet 9 in our solar system, and the Optical Gravitational Lensing Experiment can be explained within the framework of Higgs inflation.

Phases of Pseudo-Nambu-Goldstone bosons

We study the vacuum dynamics of pseudo-Nambu-Goldstone bosons (pNGBs) for SO(N + 1) → SO(N) spontaneous and explicit symmetry breaking. We determine the magnitude of explicit symmetry breaking consistent with an EFT description of the effective potential at zero and finite temperatures. We expose and clarify novel additional vacuum transitions that can arise for generic pNGBs below the initial scale of SO(N + 1) → SO(N) spontaneous symmetry breaking, which may have phenomenological relevance. In this respect, two phenomenological scenarios are analyzed: thermal and supercooled dark sector pNGBs. In the thermal scenario the vacuum transition is first-order but very weak. For a supercooled dark sector we find that, depending on the sign of the explicit symmetry breaking, one can have a symmetry-restoring vacuum transition SO(N – 1) → SO(N) which can be strongly first-order, with a detectable stochastic gravitational wave background signal.

Rhythm-adaptive statistical estimation methods of probabilistic characteristics of cyclic random processes

The paper is devoted to the rhythm-adaptive statistical estimation methods of cyclic random processes probabilistic characteristics. The main goal of the article is development and research of rhythm-adaptive methods of cyclic random processes statistical processing. Based on the fundamental fact of invariance of probabilistic characteristics of a cyclic random process to time shifts determined by its rhythm function, rhythm-adaptive statistical estimations of initial, central and mixed moment functions of cyclic random processes of discrete parameter were obtained. The article analytically proves the unbiasedness and consistency of rhythm-adaptive statistical estimates of the mathematical expectation and the initial moment functions of the higher order of cyclic random process. By conducting a series of computer simulation experiments, it has been demonstrated, that the absolute errors of rhythm-adaptive methods of statistical estimation are significantly smaller than the corresponding errors for classical non-rhythm adaptive methods of statistical estimation of cyclic stochastic signals probabilistic characteristics. The effectiveness of using rhythm-adaptive methods of statistical estimation of probabilistic characteristics of ECG signals has been demonstrated. The results obtained in the work clearly demonstrate the essential advantages of rhythm-adaptive methods of statistical processing of cyclic stochastic signals with a variable (irregular) rhythm in comparison with non-rhythm-adaptive analogues.

Fault-Tolerant Control of Stochastic High-Order Fully Actuated Systems.

In recent years, high-order fully actuated (HOFA) systems, founded by Prof. GR Duan, have recorded rapid progress for deterministic systems. However, the control issue of stochastic fully actuated systems is still an open problem. This study develops a novel stochastic HOFA system model that complements the existing HOFA methodology. Notably, stochastic signals can be considered in the proposed model, different from the case in the deterministic model. By adopting a high-order operator, equivalent control and stabilization control laws are realized to guarantee the global asymptotic stability in probability of the closed-loop system. For the system with sensor gain faults, an observer-based fault-tolerant control law is designed. Finally, the simulation results validate the effectiveness of the proposed control schemes.

On a Closer Look of a Doppler Tolerant Noise Radar Waveform in Surveillance Applications.

The prevalence of Low Probability of Interception (LPI) and Low Probability of Exploitation (LPE) radars in contemporary Electronic Warfare (EW) presents an ongoing challenge to defense mechanisms, compelling constant advances in protective strategies. Noise radars are examples of LPI and LPE systems that gained substantial prominence in the past decade despite exhibiting a common drawback of limited Doppler tolerance. The Advanced Pulse Compression Noise (APCN) waveform is a stochastic radar signal proposed to amalgamate the LPI and LPE attributes of a random waveform with the Doppler tolerance feature inherent to a linear frequency modulation. In the present work, we derive closed-form expressions describing the APCN signal's ambiguity function and spectral containment that allow for a proper analysis of its detection performance and ability to remove range ambiguities as a function of its stochastic parameters. This paper also presents a more detailed address of the LPI/LPE characteristic of APCN signals claimed in previous works. We show that sophisticated Electronic Intelligence (ELINT) systems that employ Time Frequency Analysis (TFA) and image processing methods may intercept APCN and estimate important parameters of APCN waveforms, such as bandwidth, operating frequency, time duration, and pulse repetition interval. We also present a method designed to intercept and exploit the unique characteristics of the APCN waveform. Its performance is evaluated based on the probability of such an ELINT system detecting an APCN radar signal as a function of the Signal-to-Noise Ratio (SNR) in the ELINT system. We evaluated the accuracy and precision of the random variables characterizing the proposed estimators as a function of the SNR. Results indicate a probability of detection close to 1 and show good performance, even for scenarios with a SNR slightly less than -10 dB. The contributions in this work offer enhancements to noise radar capabilities while facilitating improvements in ESM systems.

A PINN approach for identifying governing parameters of noisy thermoacoustic systems

Identifying the governing parameters of self-sustained oscillation is crucial for the diagnosis, prediction and control of thermoacoustic instabilities. In this paper, we propose and validate a novel method for computing the parameters of thermoacoustic oscillation in a stochastic environment, which exploits a physics-informed neural network (PINN). Specifically, we introduce a negative log-likelihood loss function that integrates the stochastic samples and the solution of the Fokker–Planck equation. The proposed framework is validated using the numerically generated signal and the experimental data obtained from an annular combustor, both before and after the supercritical Hopf bifurcation. The results of PINN-based system identification show good agreement with the actual system parameters and the original stochastic signal, with improved accuracy compared to established methods. To the best of our knowledge, this study constitutes the first demonstration of the PINN-inverse approach that uses the noise-induced dynamics of thermoacoustic systems, opening up new pathways for diagnosing and predicting the thermoacoustic behaviour of various combustion systems.

DOA Estimation Approach Based on Parallel Stochastic Resonance System and Signal Correlation Calculation

DOA Estimation Approach Based on Parallel Stochastic Resonance System and Signal Correlation Calculation

Impacts of gravitational-wave background from supermassive black hole binaries on the detection of compact binaries by LISA* *Supported by the NSFC (12250010, 11991052) and Key Research Program of Frontier Sciences, CAS, (ZDBS-LY-7009)

In the frequency band of the Laser Interferometer Space Antenna (LISA), extensive research has been conducted on the impact of foreground confusion noise generated by galactic binaries within the Milky Way Galaxy. Additionally, recent evidence of a stochastic signal, announced by the NANOGrav, EPTA, PPTA, CPTA, and InPTA, indicates that the stochastic gravitational-wave background (SGWB) generated by supermassive black hole binaries (SMBHBs) can contribute strong background noise within the LISA band. Given the presence of such strong noise, it is expected to have significant impacts on LISA's scientific missions. In this study, we investigate the impacts of the SGWB generated by SMBHBs on the detection of individual massive black hole binaries, verified galactic binaries, and extreme mass ratio inspirals in the context of LISA. We find it essential to resolve and eliminate the excess noise from the SGWB to guarantee the success of LISA's missions.

Filtering and smoothing estimation algorithms from uncertain nonlinear observations with time-correlated additive noise and random deception attacks

ABSTRACT This paper discusses the problem of estimating a stochastic signal from nonlinear uncertain observations with time-correlated additive noise described by a first-order Markov process. Random deception attacks are assumed to be launched by an adversary, and both this phenomenon and the uncertainty in the observations are modelled by two sets of Bernoulli random variables. Under the assumption that the evolution model generating the signal to be estimated is unknown and only the mean and covariance functions of the processes involved in the observation equation are available, recursive algorithms based on linear approximations of the real observations are proposed for the least-squares filtering and fixed-point smoothing problems. Finally, the feasibility and effectiveness of the developed estimation algorithms are verified by a numerical simulation example, where the impact of uncertain observation and deception attack probabilities on estimation accuracy is evaluated.

Spectral analysis of solar-irradiance fluctuations

Solar-irradiance fluctuations possess a power-law spectrum with two different slopes in the intermediate (1/day <f<1/h) and high [1/h <f<1/(2 min)] frequency (f) regimes. This spectrum is a combination of a deterministic (latitude-dependent) variation of daylight duration and a stochastic component arising from various environmental attenuation factors. We propose a principled approach to decompose the solar-irradiance time series into the deterministic location-dependent clear sky and the stochastic environmental attenuation signals. Such decomposition permits one to systematically analyze the solar photovoltaic power-fluctuation spectra after accounting for the strong geographic dependence, failing which no baseline standard exists with which to compare spectra between disparate geographic locations. Our analysis for two different locations shows that the stochastic environmental factors determine the spectral power-law slope in the high-frequency range, while the deterministic clear-sky signal plays a role in the intermediate frequency range. We close by discussing its implications for solar photovoltaic power production, in particular, for the geographic smoothing of fluctuations. Published by the American Physical Society 2024

Direct electrochemical evidence suggests that aqueous microdroplets spontaneously produce hydrogen peroxide

Recent reports have detailed the striking observation that electroactive molecules, such as hydrogen peroxide (H2O2) and radical water species (H2O.+/H2O.-), are spontaneously produced in aqueous microdroplets. Stochastic electrochemistry allows one to study reactions in real-time occurring inside subfemtoliter droplets, one droplet at a time, when a microdroplet irreversibly adsorbs to an ultramicroelectrode surface (radius ~ 5 µm). Here, we use stochastic electrochemistry to probe the formation of hydrogen peroxide (H2O2) in single aqueous microdroplets suspended in 1,2-dichloroethane. The oxidation of H2O2 at alkaline pH (11.5) differs from near-neutral conditions (6.4), allowing us to create a digital, turn-off sensing modality for the presence of H2O2. Further, we show that the stochastic electrochemical signal is highest at the mass transfer limitation of the H2O2 couple and is dampened when the potential nears the formal potential. We validate these results by showing that the addition of a H2O2 selective probe, luminol, decreases the stochastic electrochemical response at alkaline pH (11.5). Our results support the observation that H2O2 is generated in water microdroplets at concentrations of ~100 s of µM.

Cosmological interpretation for the stochastic signal in pulsar timing arrays

Cosmological interpretation for the stochastic signal in pulsar timing arrays

Testing strengths, limitations, and biases of current pulsar timing arrays’ detection analyses on realistic data

State-of-the-art searches for gravitational waves (GWs) in pulsar timing array (PTA) datasets model the signal as an isotropic, Gaussian, and stationary process described by a power law. In practice, none of these properties are expected to hold for an incoherent superposition of GWs generated by a cosmic ensemble of supermassive black hole binaries (SMBHBs). This stochastic signal is usually referred to as the GW background (GWB) and is expected to be the primary signal in the PTA band. We performed a systematic investigation of the performance of current search algorithms, using a simple power-law model to characterise GW signals in realistic datasets. We used, as the baseline dataset, synthetic realisations of timing residuals mimicking the European PTA (EPTA) second data release (DR2). Thus, we included in the dataset uneven time stamps, achromatic and chromatic red noise, and multi-frequency observations. We then injected timing residuals from an ideal isotropic, Gaussian, single power-law stochastic process and from a realistic population of SMBHBs, performing a methodical investigation of the recovered signal. We found that current search models are efficient at recovering the GW signal, but several biases can be identified due to the signal-template mismatch, which we identified via probability-probability (P–P) plots and quantified using Kolmogorov-Smirnov (KS) statistics. We discuss our findings in light of the signal observed in the EPTA DR2 and corroborate its consistency with a SMBHB origin.

Modeling trends and periodic components in geodetic time series: a unified approach

Geodetic time series are usually modeled with a deterministic approach that includes trend, annual, and semiannual periodic components having constant amplitude and phase-lag. Although simple, this approach neglects the time-variability or stochasticity of trend and seasonal components, and can potentially lead to inadequate interpretations, such as an overestimation of global navigation satellite system (GNSS) station velocity uncertainties, up to masking important geophysical phenomena. In this contribution, we generalize previous methods for determining trends and seasonal components and address the challenge of their time-variability by proposing a novel linear additive model, according to which (i) the trend is allowed to evolve over time, (ii) the seasonality is represented by a fractional sinusoidal waveform process (fSWp), accounting for possible non-stationary cyclical long-memory, and (iii) an additional serially correlated noise captures the short term variability. The model has a state space representation, opening the way for the evaluation of the likelihood and signal extraction with the support of the Kalman filter (KF) and the associated smoothing algorithm. Suitable enhancements of the basic methodology enable handling data gaps, outliers, and offsets. We demonstrate the advantage of our method with respect to the benchmark deterministic approach using both observed and simulated time series and provide a fair comparison with the Hector software. To that end, various geodetic time series are considered which illustrate the ability to capture the time-varying stochastic seasonal signals with the fSWp.

Prospects for Taiji to detect a gravitational-wave background from cosmic strings

Recently, multiple pulsar timing array collaborations have presented compelling evidence for a stochastic signal at nanohertz frequencies, potentially originating from cosmic strings. Cosmic strings are linear topological defects that can arise during phase transitions in the early Universe or as fundamental strings in superstring theory. This paper focuses on investigating the detection capabilities of Taiji, a planned space-based gravitational wave detector, for the gravitational wave background generated by cosmic strings. By analyzing simulated Taiji data and utilizing comprehensive Bayesian parameter estimation techniques, we demonstrate a significant improvement in precision compared to the NANOGrav 15-year data set, surpassing it by an order of magnitude. This highlights the enhanced measurement capabilities of Taiji. Consequently, Taiji can serve as a valuable complementary tool to pulsar timing arrays in validating and exploring the physics of cosmic strings in the early Universe.

Constraining the graviton mass with the NANOGrav 15 year data set

The recently detected stochastic signal by several pulsar timing array collaborations, offers an opportunity to scrutinize the fundamental properties of gravity, including the potential mass of the graviton. In this study, we analyze the North American Nanohertz Observatory for Gravitational Waves 15 year data set to search for a stochastic gravitational wave background with modified Hellings–Downs correlations predicted by massive gravity. While the Bayesian analysis comparing the massive gravity to massless gravity within the effective searchable mass range of mg∈[3×10−25,8×10−24]eVc−2 does not yield an explicit upper bound as all the Bayes factors are smaller than 3, the combined consideration of the minimum frequency inherent in a massive gravity and the observed spectrum leads to an upper limit of mg<8.2×10−24eVc−2 .

Novel compound multistable stochastic resonance weak signal detection

Abstract The research on stochastic resonance (SR) which is used to extract weak signals from noisy backgrounds is of great theoretical significance and promising application. To address the shortcomings of the classical tristable SR model, this article proposes a novel compound multistable stochastic resonance (NCMSR) model by combining the Woods–Saxon (WS) and tristable models. The influence of the parameters of the NCMSR systems on the output response performance is studied under different α stable noises. Meanwhile, the adaptive synchronization optimization algorithm based on the proposed model is employed to achieve periodic and non-periodic signal identifications in α stable noise environments. The results show that the proposed system model outperforms the tristable system in terms of detection performance. Finally, the NCMSR model is applied to 2D image processing, which achieves great noise reduction and image recovery effects.

Spectrum analysis of digital UPWM signals generated from random modulating signals

This work studies the spectrum of discrete-time Uniform-sampling pulse width modulation (UPWM) signals originating from stochastic input signals. It demonstrates that for ergodic input sequences of independent and identically distributed random variables, the Discrete Fourier Transform (DFT) of the UPWM signals can be directly estimated from the input signal’s statistics. Consequently, it is shown that if the input signal can be modeled as such a random sequence, only statistical information of the sequence is required for the accurate estimation of the DFT of the UPWM signal. This is achieved here by proving that the DFT estimators obtained by observation of the input sequence within a time window are consistent estimators of the DFT coefficients of the underlying random process. Moreover, for signals whose generalized probability density functions can be expressed as functions of a small number of parameters, the DFT coefficients can be estimated or even calculated via closed-form expressions with linear complexity. Examples are given for input signals derived from symmetric and asymmetric distributions. The results are validated by comparison with evaluations of the UPWM signal’s DFT via the Fast Fourier Transform (FFT). The proposed method provides a mathematical framework for the analysis and design of UPWM systems whose inputs have known statistical properties.