scholarly journals Understanding matched filters for precision cosmology

2021 ◽  
Vol 507 (4) ◽  
pp. 4852-4863
Author(s):  
Íñigo Zubeldia ◽  
Aditya Rotti ◽  
Jens Chluba ◽  
Richard Battye
Keyword(s):  
Noisy Data ◽  
Approximate Expression ◽  
Positive Bias ◽  
Signal To Noise ◽  
Cluster Number ◽  
Matched Filters ◽  
Unit Variance ◽  
Non Gaussian ◽  
Number Counts ◽  
The Impact

Abstract Matched filters are routinely used in cosmology in order to detect galaxy clusters from mm observations through their thermal Sunyaev–Zeldovich (tSZ) signature. In addition, they naturally provide an observable, the detection signal-to-noise or significance, which can be used as a mass proxy in number counts analyses of tSZ-selected cluster samples. In this work, we show that this observable is, in general, non-Gaussian, and that it suffers from a positive bias, which we refer to as optimization bias. Both aspects arise from the fact that the signal-to-noise is constructed through an optimization operation on noisy data, and hold even if the cluster signal is modelled perfectly well, no foregrounds are present, and the noise is Gaussian. After reviewing the general mathematical formalism underlying matched filters, we study the statistics of the signal-to-noise with a set Monte Carlo mock observations, finding it to be well-described by a unit-variance Gaussian for signal-to-noise values of 6 and above, and quantify the magnitude of the optimization bias, for which we give an approximate expression that may be used in practice. We also consider the impact of the bias on the cluster number counts of Planck and the Simons Observatory (SO), finding it to be negligible for the former and potentially significant for the latter.

scholarly journals Combining cosmological constraints from cluster counts and galaxy clustering

2014 ◽  
Vol 10 (S306) ◽  
pp. 216-218 ◽  
Author(s):  
F. Lacasa
Keyword(s):  
Large Scale ◽  
Harmonic Space ◽  
Cluster Number ◽  
Dark Energy Survey ◽  
Ongoing Work ◽  
Cross Covariance ◽  
Diagrammatic Method ◽  
Non Gaussian ◽  
Number Counts ◽  
Halo Occupation Distribution

AbstractPresent and future large scale surveys offer promising probes of cosmology. For example the Dark Energy Survey (DES) is forecast to detect ~300 millions galaxies and thousands clusters up to redshift ~1.3. I here show ongoing work to combine two probes of large scale structure : cluster number counts and galaxy 2-point function (in real or harmonic space). The halo model (coupled to a Halo Occupation Distribution) can be used to model the cross-covariance between these probes, and I introduce a diagrammatic method to compute easily the different terms involved. Furthermore, I compute the joint non-Gaussian likelihood, using the Gram-Charlier series. Then I show how to extend the methods of Bayesian hyperparameters to Poissonian distributions, in a first step to include them in this joint likelihood.

scholarly journals Calibration of bias and scatter involved in cluster mass measurements using optical weak gravitational lensing

2021 ◽  
Author(s):  
Sebastian Grandis ◽  
Sebastian Bocquet ◽  
Joseph J Mohr ◽  
Matthias Klein ◽  
Klaus Dolag
Keyword(s):  
Gravitational Lensing ◽  
Initial Conditions ◽  
Stage Iv ◽  
Weak Lensing ◽  
Mass Relation ◽  
Cluster Number ◽  
Mass Calibration ◽  
Hydrodynamical Simulations ◽  
Number Counts ◽  
The Impact

Abstract Cosmological inference from cluster number counts is systematically limited by the accuracy of the mass calibration, i.e. the empirical determination of the mapping between cluster selection observables and halo mass. In this work we demonstrate a method to quantitatively determine the bias and uncertainties in weak-lensing mass calibration. To this end, we extract a library of projected matter density profiles from hydrodynamical simulations. Accounting for shear bias and noise, photometric redshift uncertainties, mis-centering, cluster member contamination, cluster morphological diversity, and line-of-sight projections, we produce a library of shear profiles. Fitting a one-parameter model to these profiles, we extract the so-called weak lensing mass MWL. Relating the weak-lensing mass to the halo mass from gravity-only simulations with the same initial conditions as the hydrodynamical simulations allows us to estimate the impact of hydrodynamical effects on cluster number counts experiments. Creating new shear libraries for ∼1000 different realizations of the systematics, provides a distribution of the parameters of the weak-lensing to halo mass relation, reflecting their systematic uncertainty. This result can be used as a prior for cosmological inference. We also discuss the impact of the inner fitting radius on the accuracy, and determine the outer fitting radius necessary to exclude the signal from neighboring structures. Our method is currently being applied to different Stage III lensing surveys, and can easily be extended to Stage IV lensing surveys.

scholarly journals Cosmological model parameter dependence of the matter power spectrum covariance from the DEUS-PUR Cosmo simulations

2020 ◽  
Vol 500 (2) ◽  
pp. 2532-2542
Author(s):  
Linda Blot ◽  
Pier-Stefano Corasaniti ◽  
Yann Rasera ◽  
Shankar Agarwal
Keyword(s):  
Dark Energy ◽  
Power Spectrum ◽  
Cold Dark Matter ◽  
Matter Density ◽  
Density Fluctuations ◽  
Matrix Analysis ◽  
Model Parameter ◽  
Matter Power Spectrum ◽  
Non Gaussian ◽  
The Impact

ABSTRACT Future galaxy surveys will provide accurate measurements of the matter power spectrum across an unprecedented range of scales and redshifts. The analysis of these data will require one to accurately model the imprint of non-linearities of the matter density field. In particular, these induce a non-Gaussian contribution to the data covariance that needs to be properly taken into account to realize unbiased cosmological parameter inference analyses. Here, we study the cosmological dependence of the matter power spectrum covariance using a dedicated suite of N-body simulations, the Dark Energy Universe Simulation–Parallel Universe Runs (DEUS-PUR) Cosmo. These consist of 512 realizations for 10 different cosmologies where we vary the matter density Ωm, the amplitude of density fluctuations σ8, the reduced Hubble parameter h, and a constant dark energy equation of state w by approximately $10{{\ \rm per\ cent}}$. We use these data to evaluate the first and second derivatives of the power spectrum covariance with respect to a fiducial Λ-cold dark matter cosmology. We find that the variations can be as large as $150{{\ \rm per\ cent}}$ depending on the scale, redshift, and model parameter considered. By performing a Fisher matrix analysis we explore the impact of different choices in modelling the cosmological dependence of the covariance. Our results suggest that fixing the covariance to a fiducial cosmology can significantly affect the recovered parameter errors and that modelling the cosmological dependence of the variance while keeping the correlation coefficient fixed can alleviate the impact of this effect.

Examining the Impact of and Sensitivity of Fit Indices to Omitting Covariates Interaction Effect in Multilevel Multiple-Indicator Multiple-Cause Models

2021 ◽  
pp. 001316442199240
Author(s):  
Chunhua Cao ◽  
Eun Sook Kim ◽  
Yi-Hsin Chen ◽  
John Ferron
Keyword(s):  
Root Mean Square ◽  
Interaction Effect ◽  
Intraclass Correlation ◽  
Information Criteria ◽  
Parameter Estimates ◽  
Mean Square ◽  
Fit Indices ◽  
Cluster Number ◽  
Multiple Indicator ◽  
The Impact

This study examined the impact of omitting covariates interaction effect on parameter estimates in multilevel multiple-indicator multiple-cause models as well as the sensitivity of fit indices to model misspecification when the between-level, within-level, or cross-level interaction effect was left out in the models. The parameter estimates produced in the correct and the misspecified models were compared under varying conditions of cluster number, cluster size, intraclass correlation, and the magnitude of the interaction effect in the population model. Results showed that the two main effects were overestimated by approximately half of the size of the interaction effect, and the between-level factor mean was underestimated. None of comparative fit index, Tucker–Lewis index, root mean square error of approximation, and standardized root mean square residual was sensitive to the omission of the interaction effect. The sensitivity of information criteria varied depending majorly on the magnitude of the omitted interaction, as well as the location of the interaction (i.e., at the between level, within level, or cross level). Implications and recommendations based on the findings were discussed.

scholarly journals Impact of accelerometer based in physical layer wireless networks

2018 ◽  
Vol 7 (2.5) ◽  
pp. 15
Author(s):  
Zuhanis Mansor ◽  
Muhammad Khairulanwar bin Zulkafli
Keyword(s):  
Wireless Communication ◽  
Landscape Position ◽  
Line Of Sight ◽  
Noise Power ◽  
Signal Power ◽  
Propagation Channel ◽  
Signal To Noise ◽  
Downlink Throughput ◽  
The Impact ◽  
Non Line Of Sight

The initial deployments of antenna in the handset consist of fixed non-rotated antenna for transmitting and receiving the signal in the wireless communication scenario. However, link correlation at the UE shows very bad performance when the handset rotates in landscape position. This paper evaluates the impact of accelerometer on the downlink propagation channel of 3G smartphone for non-line-of-sight links. The performance average received signal power is studied for user equipment. Results show that the exploitation of an accelerometer provide better performance in terms of received signal power when the handset rotated from portrait to landscape position. It can be concluded that the deployment of accelerometer can be used to improve existing 3G smartphone received signal. Results also indicate that accelerometer can be used to improve downlink throughput since the signal-to-noise-power is increased by approximately 16%.

scholarly journals Assessment of the Impacts of Image Signal-to-Noise Ratios in Impervious Surface Mapping

2019 ◽  
Vol 11 (22) ◽  
pp. 2603
Author(s):  
George Xian ◽  
Hua Shi ◽  
Cody Anderson ◽  
Zhuoting Wu
Keyword(s):  
Spatial Resolution ◽  
Signal To Noise Ratio ◽  
Spatial Scales ◽  
Regression Tree ◽  
Remote Sensing Data ◽  
Impervious Surface ◽  
Landsat 8 ◽  
Signal To Noise ◽  
Study Results ◽  
The Impact

Medium spatial resolution satellite images are frequently used to characterize thematic land cover and a continuous field at both regional and global scales. However, high spatial resolution remote sensing data can provide details in landscape structures, especially in the urban environment. With upgrades to spatial resolution and spectral coverage for many satellite sensors, the impact of the signal-to-noise ratio (SNR) in characterizing a landscape with highly heterogeneous features at the sub-pixel level is still uncertain. This study used WorldView-3 (WV3) images as a basis to evaluate the impacts of SNR on mapping a fractional developed impervious surface area (ISA). The point spread function (PSF) from the Landsat 8 Operational Land Imager (OLI) was used to resample the WV3 images to three different resolutions: 10 m, 20 m, and 30 m. Noise was then added to the resampled WV3 images to simulate different fractional levels of OLI SNRs. Furthermore, regression tree algorithms were incorporated into these images to estimate the ISA at different spatial scales. The study results showed that the total areal estimate could be improved by about 1% and 0.4% at 10-m spatial resolutions in our two study areas when the SNR changes from half to twice that of the Landsat OLI SNR level. Such improvement is more obvious in the high imperviousness ranges. The root-mean-square-error of ISA estimates using images that have twice and two-thirds the SNRs of OLI varied consistently from high to low when spatial resolutions changed from 10 m to 20 m. The increase of SNR, however, did not improve the overall performance of ISA estimates at 30 m.

scholarly journals Subtle changes in crosslinking drive diverse anomalous transport characteristics in actin-microtubule networks

2020 ◽  
Author(s):  
Sylas Anderson ◽  
Jonathan Garamella ◽  
Ryan McGorty ◽  
Rae Robertson-Anderson
Keyword(s):  
Intracellular Transport ◽  
Single Particle Tracking ◽  
Anomalous Transport ◽  
Brownian Diffusion ◽  
Complex Environments ◽  
Materials Engineering ◽  
Microtubule Networks ◽  
Non Gaussian ◽  
Fluid Rheology ◽  
The Impact

Abstract Anomalous diffusion in crowded and complex environments is widely studied due to its importance in intracellular transport, fluid rheology and materials engineering. Specifically, diffusion through the cytoskeleton, a network comprised of semiflexible actin filaments and rigid microtubules that interact both sterically and via crosslinking, plays a principal role in viral infection, vesicle transport and targeted drug delivery. Here, we elucidate the impact of crosslinking on particle diffusion in composites of actin and microtubules with actin-actin, microtubule-microtubule and actin-microtubule crosslinking. We analyze a suite of complementary transport metrics by coupling single-particle tracking and differential dynamic microscopy. Using these orthogonal techniques, we find that particles display non-Gaussian and non-ergodic subdiffusion that is markedly enhanced by cytoskeletal crosslinking of any type, which we attribute to suppressed microtubule mobility. However, the extent to which transport deviates from normal Brownian diffusion depends strongly on the crosslinking motif – with actin-microtubule crosslinking inducing the most pronounced anomalous characteristics – due to increased actin fluctuation heterogeneity. Our results reveal that subtle changes to actin-microtubule interactions can have dramatic impacts on diffusion in the cytoskeleton, and suggest that less mobile and more locally heterogeneous networks lead to more strongly anomalous transport.

scholarly journals Generalized Thermoelastic Functionally Graded on a Thin Slim Strip Non-Gaussian Laser Beam

Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1094 ◽  
Author(s):  
Sayed M. Abo-Dahab ◽  
Ahmed E. Abouelregal ◽  
Marin Marin
Keyword(s):  
Laser Beam ◽  
Pulse Length ◽  
Generalized Thermoelasticity ◽  
Functionally Graded ◽  
Strong Impact ◽  
Problem Solution ◽  
Gaussian Laser Beam ◽  
Laser Parameters ◽  
Non Gaussian ◽  
The Impact

The present study utilizes the generalized thermoelasticity theory, with one thermal relaxation time (TR), to examine the thermoelastic problem of a functionally graded thin slim strip (TSS). The authors heated the plane surface bounding using a non-Gaussian laser beam with a pulse length of 2 ps. The material characteristics varied continually based on exponential functions. Moreover, the equations governing the generalized thermoelasticity for a functionally graded material (FGM) are recognized. The problem’s ideal solution was primarily obtained in the Laplace transform (LT) space. The LTs were converted numerically because of the considerable importance of the response in the transient state. For a hypothetical substance, the numerical procedures calculating the displacement, stress, temperature and strain were given. The analogous problem solution to an isotropic homogeneous material was provided by defining the parameter of non-homogeneity adequately. The obtained results were displayed using graphs to illustrate the extent to which non-homogeneity affected displacement, stress, temperature and strain. A comparison was been made between the present study and those previously obtained by others, when the new parameters vanish to show the impact of the non-homogeneity, TSS and laser parameters on the phenomenon. The results obtained indicate a significant strong impact of FGM, TSS and laser parameters.

scholarly journals Rate-compatible LDPC convolutional codes over non-gaussian noise channel

2020 ◽  
Vol 309 ◽  
pp. 01010
Author(s):  
Qiang Liu
Keyword(s):  
Gaussian Noise ◽  
Iterative Decoding ◽  
Belief Propagation ◽  
Additive White Gaussian Noise ◽  
Convolutional Codes ◽  
Gaussian Mixture ◽  
Performance Study ◽  
Shift Keying ◽  
Non Gaussian ◽  
The Impact

This paper is aimed to study the characteristics of the underwater acoustic channel with non-Gaussian noise channel. And Gaussian mixture model (GMM) is utilized to fit the background noise over the non-Gaussian noise channel. Furthermore, coding techniques which use a sequence of rate-compatible low-density parity-check (RC-LDPC) convolutional codes with separate rates are constructed based on graph extension method. The performance study of RC-LDPC convolutional codes over non-Gaussian noise channel and the additive white Gaussian noise (AWGN) channel is performed. Study implementation of simulation is that modulation with binary phase shift keying (BPSK), and iterative decoding based on pipeline log-likelihood rate belief propagation (LLRBP) algorithm. Finally, it is shown that RC-LDPC convolutional codes have good bit-rate-error (BER) performance and can effectively reduce the impact of noise.

scholarly journals Improving Distantly Supervised Relation Extraction with Neural Noise Converter and Conditional Optimal Selector

2019 ◽  
Vol 33 ◽  
pp. 7273-7280 ◽  
Author(s):  
Shanchan Wu ◽  
Kai Fan ◽  
Qiong Zhang
Keyword(s):  
Transition Matrix ◽  
Noisy Data ◽  
Relation Extraction ◽  
The Other ◽  
Neural Noise ◽  
Distant Supervision ◽  
Other Hand ◽  
The Impact ◽  
Large Corpus

Distant supervised relation extraction has been successfully applied to large corpus with thousands of relations. However, the inevitable wrong labeling problem by distant supervision will hurt the performance of relation extraction. In this paper, we propose a method with neural noise converter to alleviate the impact of noisy data, and a conditional optimal selector to make proper prediction. Our noise converter learns the structured transition matrix on logit level and captures the property of distant supervised relation extraction dataset. The conditional optimal selector on the other hand helps to make proper prediction decision of an entity pair even if the group of sentences is overwhelmed by no-relation sentences. We conduct experiments on a widely used dataset and the results show significant improvement over competitive baseline methods.

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