Peaks In Power Spectrum Research Articles

Ultra slow-roll with a black hole

We investigate ultra slow-roll inflation with a seed black hole in a de Sitter background. By numerically tracking transitions from slow-roll to ultra slow-roll inflation, we find that quasi-normal mode solutions of the scalar field are excited following the decay of the slow-roll attractor, depending on the mass of the black hole. For small black holes, the picture is similar to standard inflation with the usual damping of the scalar field; with a large black hole, we find that the ringing modes dominate. It is believed that the transition to ultra slow-roll in the pure inflationary case enhances the peak of the primordial power spectrum, thereby increasing the likelihood of primordial black hole formation. We comment on how the novel ringing behaviour due to the seed black hole might impact on cosmological perturbations.

Comparative Analysis of Perturbed $f(R)$ Gravity and Perturbed Rastall Gravity Models in Describing Cosmic Evolution from Early to Late Universe Relative to the $\Lambda$CDM Model

Abstract This study conducts a meticulous examination of the cosmological implications inherent in Rastall gravity and $f(R)$ gravity models, assessing their efficacy across distinct cosmic epochs, from early universe structure formation to late-time acceleration. In the initial stages, both models exhibit commendable compatibility with observed features of structure formation, aligning with the established $\Lambda$CDM model. The derived Jeans' wavenumbers for each model support their viability. However, as the cosmic timeline progresses into the late universe, a discernible disparity surfaces. Utilizing the Markov Chain Monte Carlo method, we reconstruct the deceleration parameter $(q)$ and identify Deceleration - Acceleration redshift transition values. For $f(R)$ gravity, our results align closely with previous studies, emphasizing its superior ability to elucidate the recent cosmic acceleration. In contrast, Rastall gravity exhibits distinct redshift transition values. Our rigorous analysis underscores the prowess of $f(R)$ gravity in capturing the observed cosmic acceleration, positioning it as a compelling alternative to the conventional $\Lambda$CDM model. The discernible shifts observed in the peaks of the CMB power spectrum and evolution of deceleration parameter (q) for both $f(R)$ gravity and Rastall gravity models in the Early and Late universe, in relation to the $\Lambda $CDM model, provide compelling evidence supporting the proposition that these alternative gravity models can account for the anisotropy of the universe without invoking the need for dark energy.

A New Puzzling Periodic Signal in GeV Energies of the γ-Ray Binary LS I+61°303

LS I+61°303 is a high-mass X-ray binary system comprising a massive Be star and a rapidly rotating neutron star. Its spectral energy distribution across multiwavelengths categorizes it as a γ-ray binary system. In our analysis of LS I+61°303 using Fermi Large Area Telescope observations, we not only confirmed the three previously discussed periodicities of orbital, superorbital, and orbital–superorbital beat periods observed in multiwavelength observations, but also identified an additional periodic signal. This newly discovered signal exhibits a period of ∼26.3 days at a ∼7σ confidence level. Moreover, the power spectrum peak of the new signal gradually decreases as the energy increases across the energy ranges of 0.1–0.3, 0.3–1.0, and 1.0–500.0 GeV. Interestingly, a potential signal with a similar period was found in data obtained from the Owens Valley Radio Observatory 40 m telescope. We suggest that the newly discovered periodic signal may originate from a coupling between the orbital period and the retrograde stellar precession period.

Non-Gaussianity consistency relations and their consequences for the peaks

Strong deviations from scale invariance and the appearance of high peaks in the primordial power spectrum have been extensively studied for generating primordial black holes (PBHs) or gravitational waves (GWs). It is also well-known that the effect of non-linearities can be significant in both phenomena. In this paper, we advocate the existence of a general single-field consistency relation that relates the amplitude of non-Gaussianity in the squeezed limit f NL to the power spectrum and remains valid when almost all other consistency relations are violated. In particular, it is suitable for studying scenarios where scale invariance is strongly violated.We discuss the general and model-independent consequences of the consistency relation on the behavior of f NL at different scales.Specifically, we study the size, sign and slope of f NL at the scales where the power spectrum peaks and argue that generally the peaks of f NL and the power spectrum occur at different scales.As an implication of our results, we argue that non-linearities can shift or extend the range of scales responsible for the production of PBHs or GWs, relative to the window as determined by the largest peak of the power spectrum, and may also open up new windows for both phenomena.

Constraining the Clustering and 21 cm Signature of Radio Galaxies at Cosmic Dawn

The efficiency of radio emission is an important unknown parameter of early galaxies at cosmic dawn, as models with high efficiency have been shown to modify the cosmological 21 cm signal substantially, deepening the absorption trough and boosting the 21 cm power spectrum. Such models have been previously directly constrained by the overall extragalactic radio background, as observed by Absolute Radiometer for Cosmology, Astrophysics, and Diffuse Emission 2 and Long Wavelength Array. In this work, we constrain the clustering of high-redshift radio sources by utilizing the observed upper limits on arcminute-scale anisotropy from the Very Large Array at 4.9 GHz and Australia Telescope Compact Array at 8.7 GHz. Using a seminumerical simulation of a plausible astrophysical model for illustration, we show that the clustering constraints on the radio efficiency are much stronger than those from the overall background intensity by a factor that varies between 18 and 55 in the redshift range of 7–22. As a result, the predicted maximum depth of the global 21 cm signal is lowered by a factor of 6 (to 1400 mK), and the maximum 21 cm power spectrum peak at cosmic dawn is lowered by a factor of 45 (to 1.3 × 105 mK2). We conclude that the observed clustering is the strongest current direct constraint on such models, but strong early radio emission from galaxies remains viable for producing a strongly enhanced 21 cm signal from cosmic dawn.

Effects of train speed and passenger capacity on ground vibration of underground suburban railways

This study aims to explore the optimal driving speed for ground vibration in suburban railway underground sections. We focused on the ground surface of suburban railway underground sections and developed a 3D finite element dynamic coupling model for the tunnel–soil system. Subsequently, considering factors such as train speed and passenger load, we analyzed the propagation characteristics of ground vibration responses in urban railway underground sections. The research results indicate a significant amplification phenomenon in the peak power spectrum of measurement points near the tunnels in underground sections. The high-frequency components of the power spectrum between measurement points are noticeably higher between the two tunnels. Furthermore, as the train speed increases, this amplification phenomenon becomes more pronounced, and the power spectrum of each measurement point mainly concentrates on several frequency bands, with the amplitude of the power spectrum near the prominent frequencies also increasing. However, when the train speed is between 100 and 120 km/h, the impact on the amplitude of the power spectrum at measurement points above the running tunnel is minimal. Additionally, the amplitude of the middle-to-high frequency components in the power spectrum increases with the increase in passenger numbers. The impact on the peak acceleration amplitude at each measurement point is minimal when the train speed is 80 km/h or below. However, once the train speed exceeds 80 km/h, the peak acceleration amplitude above the running tunnel rapidly increases, reaching its maximum value at 113 km/h, and then gradually decreasing.

Improved extended empirical wavelet transform for accurate multivariate oscillation detection and characterisation in plant-wide industrial control loops

The conventional extended empirical wavelet transform (EEWT) proposed recently is intended to decompose multivariate signals with clear peaks in power spectra without considering the cases where the signals contain high noise levels. Even when dealing with signals with distinct peaks, the EEWT method can still encounter challenges in properly decomposing the signals. However, plant-wide data from industrial control loops, including controllers’ outputs, process variables, and manipulated variables, are commonly corrupted by high levels of noise, which can be introduced at various stages of data acquisition, transmission, and processing within the control system. To address these limitations and ensure the applicability of the EEWT to real-world industrial data with diverse and challenging characteristics, this paper presents an improved version called the improved extended empirical wavelet transform (IEEWT). The IEEWT incorporates noise-reduced power spectra and detrended fluctuation analysis techniques to enhance the decomposition. The proposed method demonstrates accurate multivariate data decomposition for both simulated and real data sets, surpassing the limitations associated with the conventional EEWT.

New data on bioacoustics and courtship behaviour in grasshoppers (Orthoptera, Acrididae, Gomphocerinae) from Russia and adjacent countries.

The songs of seven grasshopper species of subfamily Gomphocerinae from Russia, Ukraine, Georgia, and Kazakhstan were studied. We analysed not only the sound, but also the stridulatory movements of the hind legs to more entirely describe the songs. In Mesasippuskozhevnikovi, Chorthippusmacrocerus, and C.hammarstroemi, the legs are moved in a relatively simple pattern; four other species, Myrmeleotettixpalpalis, Stenobothrusnewskii, C.pullus, and Megaulacobothrusaethalinus demonstrate more complex leg movements. In six of the seven species studied, the courtship songs contain more sound elements than the calling songs. The highest number of courtship sound elements was found in M.palpalis and M.aethalinus. The different parts of a remarkably long stridulatory file in M.aethalinus are thought to participate in the production of different sound elements. The songs in S.newskii are shown for the first time. This species produces sound not only by common stridulation but also by wing beats. A relationship of S.newskii to some other species of the genus Stenobothrus, which are able to crepitate, is discussed. We also analyse the frequency spectra of the songs. A maximum energy of the song power spectra in six species studied lies in ultrasound range (higher than 20 kHz). In only M.aethalinus, the main peaks in the song power spectra lie lower than 20 kHz. The courtship behaviour in M.palpalis, C.macrocerus, and C.hammarstroemi was shown to include conspicuous visual display (movements of antennae, palps and the whole body).

Autonomous quantum heat engine based on non-Markovian dynamics of an optomechanical Hamiltonian

We propose a recipe for demonstrating an autonomous quantum heat engine where the working fluid consists of a harmonic oscillator, the frequency of which is tuned by a driving mode. The working fluid is coupled two heat reservoirs each exhibiting a peaked power spectrum, a hot reservoir peaked at a higher frequency than the cold reservoir. Provided that the driving mode is initialized in a coherent state with a high enough amplitude and the parameters of the utilized optomechanical Hamiltonian and the reservoirs are appropriate, the driving mode induces an approximate Otto cycle for the working fluid and consequently its oscillation amplitude begins to increase in time. We build both an analytical and a non-Markovian quasiclassical model for this quantum heat engine and show that reasonably powerful coherent fields can be generated as the output of the quantum heat engine. This general theoretical proposal heralds the in-depth studies of quantum heat engines in the non-Markovian regime. Further, it paves the way for specific physical realizations, such as those in optomechanical systems, and for the subsequent experimental realization of an autonomous quantum heat engine.

Nanohertz gravitational waves from supergravity inflationary model with double-inflection-point

Recently, the worldwide pulsar timing array(PTA) collaborations, such as the Chinese Pulsar Timing Array (CPTA), the European PulsarTiming Array (EPTA), the North American Nanohertz Observatory for Gravitational Waves (NANOGrav) and the Parkers Pulsar Timing Array (PPTA) published the analysis of PTA data, which is consistent with the Hellings–Downs curve, thus provides evidence for the existence of stochastic gravitational wave backgrounds (SGWB). In this paper, we will show that such SGWB signal observed by PTA can be explained by the gravitational waves (GWs) induced from double-inflection-point inflationary model in the framework of supergravity with a single chiral superfield. In this model, one of the inflection points leads to a large peak in the scalar power spectrum at small scales, and when this peak re-enters the horizon, it will induce GWs with the frequencies around nanohertz. In addition, we show that the high-density regions corresponding to the peak can collapse into planet-mass primordial black holes (PBHs), thus act as a component of dark matter (DM).

Mechanical Fault Sound Source Localization Estimation in a Multisource Strong Reverberation Environment

Aiming at the sound source localization of mechanical faults in a strong reverberation scenario with multiple sound sources, this paper investigates a mechanical fault source localization method using the U-net deep convolutional neural network. The method utilizes the SRP-PHAT algorithm to calculate the response power spectra of the collected multichannel fault signals. Through the utilization of the U-net neural network, the response power spectra containing spurious peaks are transformed into “clean” estimated source distribution maps. By employing interpolation search, the estimated source distribution maps are processed to obtain location estimations for multiple fault sources. To validate the effectiveness of the proposed method, this paper constructs an experimental dataset using mechanical fault data from electromechanical equipment relays and conducts sound source localization experiments. The experimental results show that the U-net network under 0.2 s/0.5 s/0.7 s reverberation time can effectively eliminate spurious peak interference in the response power spectrum. As the signal-to-noise ratio decreases, it can still distinguish the sound sources with a distance of 0.2 m. In the context of multifault source localization, the method is capable of simultaneously locating the positions of four fault sources, with an average localization error of less than 0.02 m. The method in this paper effectively eliminates spurious peaks in the response power spectra under conditions of multisource strong reverberation. It accurately locates multiple mechanical fault sources, thereby significantly enhancing the efficiency of mechanical fault detection.

Design and experimental verification of phase-reversal Fresnel lens for contact stress characterization

The accurate characterization of the contact stress on the mating surfaces of assembly components is a key means to ensure the assembly accuracy and operational safety of aero-engines. In this paper, a phase-reversal Fresnel lens for contact stress characterization is designed to solve the problem of low spatial resolution of contact stress characterization by the non water immersion ultrasonic method. The phase-reversal Fresnel lens is used to focus the ultrasonic wave on the mating surface of the assembly components to improve the spatial resolution of the contact stress characterization. The finite element simulation method is used to analyze the influence of the number of lens rings, the piezoelectric plate's diameter, the lens's thickness, and the groove depth on the focusing characteristics of Fresnel lenses. The power spectrum peak is used to analyze the residual reflected wave energy of the mating surface. According to the correlation between the residual reflected wave energy and the contact stress, a correlation model between the two is built to realize the characterization of the contact stress. This study provides a promising method for the characterization of contact stress on the mating surface of aero-engine assembly components by non water immersion focused ultrasound.

Prospects for detecting proto-neutron star rotation and spin-down using supernova neutrinos

ABSTRACT After a successful supernova, a proto-neutron star (PNS) cools by emitting neutrinos on ∼1–100 s time-scales. Provided that there are neutrino emission ‘hotspots’ or ‘cold-spots’ on the surface of the rotating PNS, we can expect a periodic modulation in the number of neutrinos observable by detectors. We show that Fourier transform techniques can be used to determine the PNS rotation rate from the neutrino arrival times. Provided there is no spin-down, a 1-parameter Discrete Fourier Transform (DFT) is sufficient to determine the spin period of the PNS. If the PNS is born as a magnetar with polar magnetic field strength B0 ≳ 1015 G and is ‘slowly’ rotating with an initial spin period ≳100 ms, then it can spin-down to periods of the order of seconds during the cooling phase. We propose a modified DFT technique with three frequency parameters to detect spin-down. Due to lack of neutrino data from a nearby supernova except the ∼20 neutrinos detected from SN1987A, we use toy models and one physically motivated modulating function to generate neutrino arrival times. We use the false alarm rate (FAR) to quantify the significance of the Fourier power spectrum peaks. We show that PNS rotation and spin-down are detected with $\rm FAR\,\lt\, 2~{{\ \rm per\ cent}}$ (2σ) for periodic signal content $\rm M\gtrsim 13-15~{{\ \rm per\ cent}}$ if 5 × 103 neutrinos are detected in ∼3 s and with $\rm FAR\,\lt\, 1{{\ \rm per\ cent}}$ for $\rm M\,\ge 5{{\ \rm per\ cent}}$ if 5 × 104 neutrinos are detected in ∼3 s. Since we can expect ∼104−105 neutrino detections from a supernova at 10 kpc, detection of PNS rotation and spin-down is possible using the neutrinos from the next Galactic supernova.

The Accuracy of Frequency Estimation of Structure Vibration under Ambient Excitation: Problems, Causes, and Methods

Accurate identification of building structure frequencies forms the basis for damage detection. The structural dynamic response signal, under ambient excitation, can be transformed into a superposition of multiple single-frequency exponentially damped sinusoids combined with random white noise. However, the peak power spectrum of the response signal tends to exhibit line splitting, compromising the precision of frequency identification. This study examines the accuracy characteristics of the single-frequency free damping vibration signal (SFFDVS) and derives the Cramer–Rao lower bound for the frequency estimator. It thoroughly analyzes the factors influencing the accuracy of SFFDVS frequency identification. The study reveals that the primary cause of spectral line splitting is the random delay inherent in SFFDVS. Based on the maximum likelihood method (MLM), this research introduces the MLM algorithm for SFFDVS and provides a simulation analysis. The findings indicate that the MLM estimation algorithm for frequency parameters effectively addresses spectral line splitting and offers robust noise resistance and recognition accuracy.

Constraints on the primordial curvature power spectrum by pulsar timing array data: a polynomial parameterization approach

The recent stochastic signal observed jointly by NANOGrav, parkes pulsar timing array, European pulsar timing array, and Chinese pulsar timing array can be accounted for by scalar-induced gravitational waves (SIGWs). The source of the SIGWs is from the primordial curvature perturbations, and the main contribution to the SIGWs is from the peak of the primordial curvature power spectrum. To effectively model this peak, we apply the Taylor expansion to parameterize it. With the Taylor expansion parameterization, we apply Bayesian methods to constrain the primordial curvature power spectrum based on the NANOGrav 15 year data set. The constraint on the primordial curvature power spectrum possesses a degree of generality, as the Taylor expansion can effectively approximate a wide range of function profiles.

Composition, physicochemical property and base periodicity for discriminating lncRNA and mRNA.

Annotation of genome data with biological features is a challenging problem. One such problem deals with distinguishing lncRNA from mRNA. In this study, three groups of classification features, namely base periodicity, physicochemical property and nucleotide compositions were considered. We are attempting to propose a simple neural network model to obtain better results using judicious combination of the above said sequence features. Our approach uses balanced dataset, simple prediction model and use of limited features in distinguishing lncRNA and mRNA. Accordingly (a) two properties of base periodicity: peak power spectrum of the signal and noise-to-signal ratio (SNR) of this peak signal (b) three physicochemical properties: solvation, stacking and hydrogen-bonding energy and (c) all dinucleotides and trinucleotides compositions were used. Classification was performed by considering features independently followed by combining these properties for improvement. Classification metric was used to compare the result for seven eukaryotic organisms for various combinations of features. Nucleotide compositions combined with physicochemical property or base periodicity group of features becomes a strong classifier with more than 99 percentage accuracy. Base periodicity analysis with SNR can be used as discriminating feature of lncRNA from mRNA.

Detecting active latitudes of Sun-like stars using asteroseismic a-coefficients

Aims. We introduce a framework to measure the asphericity of Sun-like stars using a1, a2, and a4 coefficients and constrain their latitudes of magnetic activity. Methods. We evaluated systematic errors on the inferred coefficients in function of key physical and seismic parameters (inclination of rotation axis, average rotation, height-to-noise ratio of peaks in power spectrum). The measured a-coefficients account for rotational oblateness and the effect of surface magnetic activity. We used a simple model that assumes a single latitudinal band of activity. Results. Using solar SOHO, VIRGO, and SPM data, we demonstrate the capability of the method to detect the mean active latitude and its intensity changes between 1999 and 2002 (maximum of activity) and 2006 and 2009 (minimum of activity). We further applied the method to study the solar-analogue stars 16 Cyg A and B using Kepler observations. In 16 Cyg A, we detected an equatorial band of activity exhibiting an intensity that could be comparable to that of the Sun. However, 16 Cyg B exhibits a bimodality in a4 that is challenging to explain. We suggest that this could be a manifestation of the transition between a quiet and an active phase of activity. Validating or invalidating this hypothesis may require new observations.

The backreaction effect of sound speed resonance in DBI inflation

We examine the backreaction effect of the enhanced small-scale scalar perturbations from the sound speed resonance (SSR) mechanism for primordial black hole formation in Dirac-Born-Infeld (DBI) inflation Within the perturbative regime, the backreaction effect of perturbations on the background dynamics can be described by an effective action after integrating out the perturbation sector. Starting with the effective field theory of a specific DBI inflation model that incorporates SSR, we obtain the one-loop effective action by integrating out the scalar perturbations at the quadratic level. Using the effective Friedmann equations derived from this one-loop effective action, we solve the Hubble parameter with backreaction and the effective perturbation dynamics on this background as well. Our numerical findings reveal that, for a viable parameter space, the backreaction effect results in a relative correction to the Hubble parameter of approximately 10−7, whereas the relative correction to the slow-roll parameter can vary between −0.3 and 0.1, before gradually converging to 10−7. Furthermore, our results show that the backreaction effect on SSR sound speed causes a slight reduction in the resonant peak of the curvature power spectrum, and the subsequent PBH formation predicted by the SSR mechanism remains almost unchanged.

Forecasting the power of higher order weak-lensing statistics with automatically differentiable simulations

Aims. We present the fully differentiable physical Differentiable Lensing Lightcone (DLL) model, designed for use as a forward model in Bayesian inference algorithms that require access to derivatives of lensing observables with respect to cosmological parameters. Methods. We extended the public FlowPM N-body code, a particle-mesh N-body solver, while simulating the lensing lightcones and implementing the Born approximation in the Tensorflow framework. Furthermore, DLL is aimed at achieving high accuracy with low computational costs. As such, it integrates a novel hybrid physical-neural (HPN) parameterization that is able to compensate for the small-scale approximations resulting from particle-mesh schemes for cosmological N-body simulations. We validated our simulations in the context of the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) against high-resolution κTNG-Dark simulations by comparing both the lensing angular power spectrum and multiscale peak counts. We demonstrated its ability to recover lensing Cℓ up to a 10% accuracy at ℓ = 1000 for sources at a redshift of 1, with as few as ∼0.6 particles per Mpc h−1. As a first-use case, we applied this tool to an investigation of the relative constraining power of the angular power spectrum and peak counts statistic in an LSST setting. Such comparisons are typically very costly as they require a large number of simulations and do not scale appropriately with an increasing number of cosmological parameters. As opposed to forecasts based on finite differences, these statistics can be analytically differentiated with respect to cosmology or any systematics included in the simulations at the same computational cost of the forward simulation. Results. We find that the peak counts outperform the power spectrum in terms of the cold dark matter parameter, Ωc, as well as on the amplitude of density fluctuations, σ8, and the amplitude of the intrinsic alignment signal, AIA.

Improving constraints on models addressing the Hubble tension with CMB delensing

The Hubble Tension is a well-known issue in modern cosmology that refers to the apparent disagreement in inferences of the Hubble constant H 0 as found through low-redshift observations and those derived from the ΛCDM model utilizing early universe observations. Several extensions to ΛCDM have been proposed to address the Hubble Tension that involve new ingredients or dynamics in the early universe. Reversing the effects of gravitational lensing on cosmic microwave background (CMB) maps produces sharper acoustic peaks in power spectra and allows for tighter constraints on cosmological parameters. We investigate the efficacy of CMB delensing for improving the constraints on parameters used in extensions of the ΛCDM model that are aimed at resolving the Hubble Tension (such as varying fundamental constants, contributions from early dark energy, and self-interacting dark radiation). We use Fisher forecasting to predict the expected constraints with and without this delensing procedure. We demonstrate that CMB delensing improves constraints on H 0 by ∼ 20% for viable models and significantly improves constraints on parameters across the board in the low-noise regime.