Cosmology from HSC Y1 weak lensing data with combined higher-order statistics and simulation-based inference
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Novaes, Camila P. (ORCID:0000000258453649)↗
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Detection of the significant impact of source clustering on higher order statistics with DES Year 3 weak gravitational lensing data
We measure the impact of source galaxy clustering on higher order summary statistics of weak gravitational lensing data. By comparing simulated data with galaxies that either trace or do not trace the underlying density field, we show that this effect can exceed measurement uncertainties for common higher order statistics for certain analysis choices. We evaluate the impact on different weak lensing observables, finding that third moments and wavelet phase harmonics are more affected than peak count statistics. Using Dark Energy Survey (DES) Year 3 (Y3) data, we construct null tests for the source-clustering-free case, finding a p-value of p = 4 × 10 −3 (2.6σ) using third-order map moments and p = 3 × 10 −11 (6.5σ) using wavelet phase harmonics. The impact of source clustering on cosmological inference can be either included in the model or minimized through ad hoc procedures (e.g. scale cuts). We verify that the procedures adopted in existing DES Y3 cosmological analyses were sufficient to render this effect negligible. Failing to account for source clustering can significantly impact cosmological inference from higher order gravitational lensing statistics, e.g. higher order N-point functions, wavelet-moment observables, and deep learning or field-level summary statistics of weak lensing maps.
79 ASTRONOMY AND ASTROPHYSICS↗
Map-level baryonification: unified treatment of weak lensing two-point and higher-order statistics
Precision cosmology benefits from extracting maximal information from cosmic structures, motivating the use of higher-order statistics (HOS) at small spatial scales. However, predicting how baryonic processes modify matter statistics at these scales has been challenging. The baryonic correction model (BCM) addresses this by modifying dark-matter-only simulations to mimic baryonic effects, providing a flexible, simulation-based framework for predicting both two-point and HOS. We show that a 3-parameter version of the BCM can jointly fit weak lensing maps' two-point statistics, wavelet phase harmonics coefficients, scattering coefficients, and the third and fourth moments to within 2% accuracy across all scales ℓ < 2000 and tomographic bins for a DES-Y3-like redshift distribution ( z ≲ 2), using the FLAMINGO simulations. These results demonstrate the viability of BCM-assisted, simulation-based weak lensing inference of two-point and HOS, paving the way for robust cosmological constraints that fully exploit non-Gaussian information on small spatial scales.
79 ASTRONOMY AND ASTROPHYSICS↗
Higher-Order Statistical Moments of Predicted Sonic Boom Waveforms Through Turbulence
Sonic booms generated by supersonic aircraft are affected by turbulence in the atmospheric boundary layer through which they propagate. Turbulence effects lead to random variability of the sonic boom waveforms measured on the ground, complicating the prediction of such waveforms. As an initial effort to predict the waveform variability, the solution of the coherent or mean sonic boom waveform has previously been formulated by the author. The current paper extends the formulation to derive an expression of the second-order statistical moment necessary to calculate the variance of the spectral amplitudes. Since the derivation uses a full wave equation, results using the derived expression will be compared with those obtained using a parabolic approximation. In order to fully quantify the uncertainty of our predictions, the higher order statistical moments are also formulated and are shown to be approximately zero. Consequently, the probability density function (pdf) of the spectral amplitudes is predicted to be Gaussian. The formulation is further extended to determine the pdf of the loudness of sonic booms and quantify the uncertainties associated with the loudness prediction. Results from the formulation are compared with available flight test data.
sonic booms↗
Higher order statistics of planetary gravities and topographies
The statistical properties of Earth, Venus, Mars, Moon, and a 3-D mantle convection model are compared. The higher order properties are expressed by third and fourth moments: i.e., as mean products over equilateral triangles (defined as coskewance) and equilateral quadrangles (defined as coexance). For point values, all the fields of real planets have positive skewness, ranging from slightly above zero for Lunar gravity to 2.6 sigma(exp 3) for Martian gravity (sigma is rms magnitude). Six of the eight excesses are greater than Gaussian (3 sigma(exp 4)), ranging from 2.0 sigma(exp 4) for Earth topography to 18.6 sigma(exp 4), for Martian topography. The coskewances and coexances drop off to zero within 20 deg arc in most cases. The mantle convective model has zero skewness and excess slightly less than Gaussian, probably arising from viscosity variations being only radial.
Kaula, William M.↗
On the Stability of Jump-Linear Systems Driven by Finite-State Machines with Markovian Inputs
This paper presents two mean-square stability tests for a jump-linear system driven by a finite-state machine with a first-order Markovian input process. The first test is based on conventional Markov jump-linear theory and avoids the use of any higher-order statistics. The second test is developed directly using the higher-order statistics of the machine s output process. The two approaches are illustrated with a simple model for a recoverable computer control system.
Patilkulkarni, Sudarshan↗
Representation of Probability Density Functions from Orbit Determination using the Particle Filter
Statistical orbit determination enables us to obtain estimates of the state and the statistical information of its region of uncertainty. In order to obtain an accurate representation of the probability density function (PDF) that incorporates higher order statistical information, we propose the use of nonlinear estimation methods such as the Particle Filter. The Particle Filter (PF) is capable of providing a PDF representation of the state estimates whose accuracy is dependent on the number of particles or samples used. For this method to be applicable to real case scenarios, we need a way of accurately representing the PDF in a compressed manner with little information loss. Hence we propose using the Independent Component Analysis (ICA) as a non-Gaussian dimensional reduction method that is capable of maintaining higher order statistical information obtained using the PF. Methods such as the Principal Component Analysis (PCA) are based on utilizing up to second order statistics, hence will not suffice in maintaining maximum information content. Both the PCA and the ICA are applied to two scenarios that involve a highly eccentric orbit with a lower apriori uncertainty covariance and a less eccentric orbit with a higher a priori uncertainty covariance, to illustrate the capability of the ICA in relation to the PCA.
Mashiku, Alinda K.↗
Kurtosis Approach Nonlinear Blind Source Separation
In this paper, we introduce a new algorithm for blind source signal separation for post-nonlinear mixtures. The mixtures are assumed to be linearly mixed from unknown sources first and then distorted by memoryless nonlinear functions. The nonlinear functions are assumed to be smooth and can be approximated by polynomials. Both the coefficients of the unknown mixing matrix and the coefficients of the approximated polynomials are estimated by the gradient descent method conditional on the higher order statistical requirements. The results of simulation experiments presented in this paper demonstrate the validity and usefulness of our approach for nonlinear blind source signal separation Keywords: Independent Component Analysis, Kurtosis, Higher order statistics.
Duong, Vu A.↗
Kurtosis Approach for Nonlinear Blind Source Separation
In this paper, we introduce a new algorithm for blind source signal separation for post-nonlinear mixtures. The mixtures are assumed to be linearly mixed from unknown sources first and then distorted by memoryless nonlinear functions. The nonlinear functions are assumed to be smooth and can be approximated by polynomials. Both the coefficients of the unknown mixing matrix and the coefficients of the approximated polynomials are estimated by the gradient descent method conditional on the higher order statistical requirements. The results of simulation experiments presented in this paper demonstrate the validity and usefulness of our approach for nonlinear blind source signal separation.
kurtosis↗
Cosmological constraints from the Subaru Hyper Suprime-Cam year 1 shear catalogue lensing convergence probability distribution function
Here we utilize the probability distribution function (PDF) of normalized convergence maps reconstructed from the Subaru Hyper Suprime-Cam (HSC) year 1 shear catalogue, in combination with the power spectrum, to measure the matter clustering amplitude S 8 = σ 8 $\sqrt {Ω_{m}/0.3}$. The large-scale structure’s statistical properties are incompletely described by the traditional two-point statistics, motivating our investigation of the PDF—a complementary higher-order statistic. By defining the PDF over the standard-deviation-normalized convergence map, we are able to isolate the non-Gaussian information. We use tailored simulations to compress the data vector and construct a likelihood approximation. We mitigate the impact of survey and astrophysical systematics with cuts on smoothing scales, redshift bins, and data vectors. We find S 8 = $0.860^{+0.066}_{–0.109}$ from the PDF alone and S 8 = 0.798$^{+0.029}_{–0.042}$ from the combination of the PDF and power spectrum (68% confidential level (CL)). The PDF improves the power-spectrum-only constraint by about 10%.
79 ASTRONOMY AND ASTROPHYSICS↗
Towards Solving the Mixing Problem in the Decomposition of Geophysical Time Series by Independent Component Analysis
The use of the Principal Component Analysis technique for the analysis of geophysical time series has been questioned in particular for its tendency to extract components that mix several physical phenomena even when the signal is just their linear sum. We demonstrate with a data simulation experiment that the Independent Component Analysis, a recently developed technique, is able to solve this problem. This new technique requires the statistical independence of components, a stronger constraint, that uses higher-order statistics, instead of the classical decorrelation a weaker constraint, that uses only second-order statistics. Furthermore, ICA does not require additional a priori information such as the localization constraint used in Rotational Techniques.
Aires, Filipe↗
Rotation of EOFs by the Independent Component Analysis: Towards A Solution of the Mixing Problem in the Decomposition of Geophysical Time Series
The Independent Component Analysis is a recently developed technique for component extraction. This new method requires the statistical independence of the extracted components, a stronger constraint that uses higher-order statistics, instead of the classical decorrelation, a weaker constraint that uses only second-order statistics. This technique has been used recently for the analysis of geophysical time series with the goal of investigating the causes of variability in observed data (i.e. exploratory approach). We demonstrate with a data simulation experiment that, if initialized with a Principal Component Analysis, the Independent Component Analysis performs a rotation of the classical PCA (or EOF) solution. This rotation uses no localization criterion like other Rotation Techniques (RT), only the global generalization of decorrelation by statistical independence is used. This rotation of the PCA solution seems to be able to solve the tendency of PCA to mix several physical phenomena, even when the signal is just their linear sum.
Aires, Filipe↗
Rotation of EOFs by the Independent Component Analysis: Towards a Solution of the Mixing Problem in the Decomposition of Geophysical Time Series
The Independent Component Analysis is a recently developed technique for component extraction. This new method requires the statistical independence of the extracted components, a stronger constraint that uses higher-order statistics, instead of the classical decorrelation, a weaker constraint that uses only second-order statistics. This technique has been used recently for the analysis of geophysical time series with the goal of investigating the causes of variability in observed data (i.e. exploratory approach). We demonstrate with a data simulation experiment that, if initialized with a Principal Component Analysis, the Independent Component Analysis performs a rotation of the classical PCA (or EOF) solution. This rotation uses no localization criterion like other Rotation Techniques (RT), only the global generalization of decorrelation by statistical independence is used. This rotation of the PCA solution seems to be able to solve the tendency of PCA to mix several physical phenomena, even when the signal is just their linear sum.
Aires, Filipe↗
Dark Energy Survey Year 3 results: cosmology with moments of weak lensing mass maps – validation on simulations
ABSTRACT We present a simulated cosmology analysis using the second and third moments of the weak lensing mass (convergence) maps. The second moment, or variances, of the convergence as a function of smoothing scale contains information similar to standard shear two-point statistics. The third moment, or the skewness, contains additional non-Gaussian information. The analysis is geared towards the third year (Y3) data from the Dark Energy Survey (DES), but the methodology can be applied to other weak lensing data sets. We present the formalism for obtaining the convergence maps from the measured shear and for obtaining the second and third moments of these maps given partial sky coverage. We estimate the covariance matrix from a large suite of numerical simulations. We test our pipeline through a simulated likelihood analyses varying 5 cosmological parameters and 10 nuisance parameters and identify the scales where systematic or modelling uncertainties are not expected to affect the cosmological analysis. Our simulated likelihood analysis shows that the combination of second and third moments provides a 1.5 per cent constraint on S8 ≡ σ8(Ωm/0.3)0.5 for DES Year 3 data. This is 20 per cent better than an analysis using a simulated DES Y3 shear two-point statistics, owing to the non-Gaussian information captured by the inclusion of higher order statistics. This paper validates our methodology for constraining cosmology with DES Year 3 data, which will be presented in a subsequent paper.
79 ASTRONOMY AND ASTROPHYSICS↗
Representativeness of CO and O3 Along ATom Transects Derived from GEOS-5 and GMI-CTM Simulations
One major goal for the NASA Atmospheric Tomography Mission (ATom) is producing an observation-based chemical climatology to represent the atmospheric heterogeneity. In this study, we use CO and O3 observations and global atmospheric model simulations to examine the spatial representativeness of the ATom-1 and -2 transects within a 4D framework provided by the NASA GEOS-5 and GMI-CTM models. Based on the probability density functions, we find that the variability of CO and O3 along the flight tracks is well hindcast by the model when sampled per ATom flights. The CO variations along the ATom-transect are likely representative of the typical CO variations over the whole Pacific basin during both the ATom-1 and -2 periods, the northern Atlantic during the ATom-1 period, and the tropical Atlantic in the ATom-2 period. Over southern Atlantic, CO along the ATom-1 transects is likely less well mixed than that of the broader region, but is still representative of the median CO concentration. CO along the ATom-2 transect is likely higher than the median CO concentration over this region. For O3, the agreements between PDFs of O3 sampled along the ATom transects and over the broader regions are fair to good over all six regions (Scores > 0.65) with notable discrepancies over some regions. For example, in ATom-1 over the northern Pacific and Atlantic, the transect samples air masses with higher O3 levels. During ATom-2, the transect over-represents the occurrence of O3 plumes over tropical Pacific. Over the southern Pacific and Atlantic for both ATom-1 and -2, the transects have a less uniform distribution compared to the surrounding basins, but still represent the median O3 abundance. Overall, we conclude in most cases that ATom measurements represent the statistical variations of these two species over the ocean basins at the time of measurement. Higher-order statistics, including covariance of species, has not been tested in this study.
ozone↗
Towards testing the theory of gravity with DESI: summary statistics, model predictions and future simulation requirements
Shortly after its discovery, General Relativity (GR) was applied to predict the behavior of our Universe on the largest scales, and later became the foundation of modern cosmology. Its validity has been verified on a range of scales and environments from the Solar system to merging black holes. However, experimental confirmations of GR on cosmological scales have so far lacked the accuracy one would hope for — its applications on those scales being largely based on extrapolation and its validity there sometimes questioned in the shadow of the discovery of the unexpected cosmic acceleration. Future astronomical instruments surveying the distribution and evolution of galaxies over substantial portions of the observable Universe, such as the Dark Energy Spectroscopic Instrument (DESI), will be able to measure the fingerprints of gravity and their statistical power will allow strong constraints on alternatives to GR. In this paper, based on a set of N-body simulations and mock galaxy catalogs, we study the predictions of a number of traditional and novel summary statistics beyond linear redshift distortions in two well-studied modified gravity models — chameleon f(R) gravity and a braneworld model — and the potential of testing these deviations from GR using DESI. These summary statistics employ a wide array of statistical properties of the galaxy and the underlying dark matter field, including two-point and higher-order statistics, environmental dependence, redshift space distortions and weak lensing. We find that they hold promising power for testing GR to unprecedented precision. The major future challenge is to make realistic, simulation-based mock galaxy catalogs for both GR and alternative models to fully exploit the statistic power of the DESI survey (by matching the volumes and galaxy number densities of the mocks to those in the real survey) and to better understand the impact of key systematic effects. Using these, we identify future simulation and analysis needs for gravity tests using DESI.
79 ASTRONOMY AND ASTROPHYSICS↗
Detector noise statistics in the non-linear regime
The statistical behavior of an idealized linear detector in the presence of threshold and saturation levels is examined. It is assumed that the noise is governed by the statistical fluctuations in the number of photons emitted by the source during an exposure. Since physical detectors cannot have infinite dynamic range, our model illustrates that all devices have non-linear regimes, particularly at high count rates. The primary effect is a decrease in the statistical variance about the mean signal due to a portion of the expected noise distribution being removed via clipping. Higher order statistical moments are also examined, in particular, skewness and kurtosis. In principle, the expected distortion in the detector noise characteristics can be calibrated using flatfield observations with count rates matched to the observations. For this purpose, some basic statistical methods that utilize Fourier analysis techniques are described.
Shopbell, P. L.↗
Study of photon correlation techniques for processing of laser velocimeter signals
The objective was to provide the theory and a system design for a new type of photon counting processor for low level dual scatter laser velocimeter (LV) signals which would be capable of both the first order measurements of mean flow and turbulence intensity and also the second order time statistics: cross correlation auto correlation, and related spectra. A general Poisson process model for low level LV signals and noise which is valid from the photon-resolved regime all the way to the limiting case of nonstationary Gaussian noise was used. Computer simulation algorithms and higher order statistical moment analysis of Poisson processes were derived and applied to the analysis of photon correlation techniques. A system design using a unique dual correlate and subtract frequency discriminator technique is postulated and analyzed. Expectation analysis indicates that the objective measurements are feasible.
Mayo, W. T., Jr.↗