scholarly journals A High-Resolution Foreground Model for the MWA EoR1 Field: Model and Implications for EoR Power Spectrum Analysis

2017 ◽  
Vol 34 ◽  
Author(s):  
P. Procopio ◽  
R. B. Wayth ◽  
J. Line ◽  
C. M. Trott ◽  
H. T. Intema ◽  
...  
Keyword(s):  
Power Spectrum ◽  
Large Scale ◽  
Field Model ◽  
Single Point ◽  
Neutral Hydrogen ◽  
Point Sources ◽  
Systematic Effects ◽  
Calibration Accuracy ◽  
Compact Sources ◽  
Residual Power

AbstractThe current generation of experiments aiming to detect the neutral hydrogen signal from the Epoch of Reionisation (EoR) is likely to be limited by systematic effects associated with removing foreground sources from target fields. In this paper, we develop a model for the compact foreground sources in one of the target fields of the MWA’s EoR key science experiment: the ‘EoR1’ field. The model is based on both the MWA’s GLEAM survey and GMRT 150 MHz data from the TGSS survey, the latter providing higher angular resolution and better astrometric accuracy for compact sources than is available from the MWA alone. The model contains 5 049 sources, some of which have complicated morphology in MWA data, Fornax A being the most complex. The higher resolution data show that 13% of sources that appear point-like to the MWA have complicated morphology such as double and quad structure, with a typical separation of 33 arcsec. We derive an analytic expression for the error introduced into the EoR two-dimensional power spectrum due to peeling close double sources as single point sources and show that for the measured source properties, the error in the power spectrum is confined to highk⊥modes that do not affect the overall result for the large-scale cosmological signal of interest. The brightest 10 mis-modelled sources in the field contribute 90% of the power bias in the data, suggesting that it is most critical to improve the models of the brightest sources. With this hybrid model, we reprocess data from the EoR1 field and show a maximum of 8% improved calibration accuracy and a factor of two reduction in residual power ink-space from peeling these sources. Implications for future EoR experiments including the SKA are discussed in relation to the improvements obtained.

scholarly journals Clustering properties of TGSS radio sources

2019 ◽  
Vol 623 ◽  
pp. A148 ◽  
Author(s):  
Arianna Dolfi ◽  
Enzo Branchini ◽  
Maciej Bilicki ◽  
Andrés Balaguera-Antolínez ◽  
Isabella Prandoni ◽  
...  
Keyword(s):  
Power Spectrum ◽  
Large Scale ◽  
Angular Spectrum ◽  
Radio Sources ◽  
Angular Power Spectrum ◽  
Sky Survey ◽  
Systematic Effects ◽  
Angular Correlation Function ◽  
Multiple Component ◽  
Anomalous Nature

We investigate the clustering properties of radio sources in the Alternative Data Release 1 of the TIFR GMRT Sky Survey (TGSS), focusing on large angular scales, where previous analyses have detected a large clustering signal. After appropriate data selection, the TGSS sample we use contains ∼110 000 sources selected at 150 MHz over ∼70% of the sky. The survey footprint is largely superimposed on that of the NRAO VLA Sky Survey (NVSS) with the majority of TGSS sources having a counterpart in the NVSS sample. These characteristics make TGSS suitable for large-scale clustering analyses and facilitate the comparison with the results of previous studies. In this analysis we focus on the angular power spectrum, although the angular correlation function is also computed to quantify the contribution of multiple-component radio sources. We find that on large angular scales, corresponding to multipoles 2 ≤ ℓ ≤ 30, the amplitude of the TGSS angular power spectrum is significantly larger than that of the NVSS. We do not identify any observational systematic effects that may explain this mismatch. We have produced a number of physically motivated models for the TGSS angular power spectrum and found that all of them fail to match observations, even when taking into account observational and theoretical uncertainties. The same models provide a good fit to the angular spectrum of the NVSS sources. These results confirm the anomalous nature of the TGSS large-scale power, which has no obvious physical origin and seems to indicate that unknown systematic errors are present in the TGSS dataset.

scholarly journals Robust Radiation Sources Localization Based on the Peak Suppressed Particle Filter for Mixed Multi-Modal Environments

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3784 ◽  
Author(s):  
Wenrui Gao ◽  
Weidong Wang ◽  
Hongbiao Zhu ◽  
Guofu Huang ◽  
Dongmei Wu ◽  
...  
Keyword(s):  
Particle Filter ◽  
Radiation Field ◽  
Particle Filtering ◽  
Large Scale ◽  
Single Point ◽  
Source Parameters ◽  
Mean Shift ◽  
Sequential Estimation ◽  
Point Sources ◽  
Non Parametric

This paper addresses a detection problem where sparse measurements are utilized to estimate the source parameters in a mixed multi-modal radiation field. As the limitation of dimensional scalability and the unimodal characteristic, most existing algorithms fail to detect the multi-point sources gathered in narrow regions, especially with no prior knowledge about intensity and source number. The proposed Peak Suppressed Particle Filter (PSPF) method utilizes a hybrid scheme of multi-layer particle filter, mean-shift clustering technique and peak suppression correction to solve the major challenges faced by current existing algorithms. Firstly, the algorithm realizes sequential estimation of multi-point sources in a cross-mixed radiation field by using particle filtering and suppressing intensity peak value, while existing algorithms could just identify single point or spatially separated point sources. Secondly, the number of radioactive sources could be determined in a non-parametric manner as the fact that invalid particle swarms would disperse automatically. In contrast, existing algorithms either require prior information or rely on expensive statistic estimation and comparison. Additionally, to improve the prediction stability and convergent performance, distance correction module and configuration maintenance machine are developed to sustain the multimodal prediction stability. Finally, simulations and physical experiments are carried out in aspects such as different noise level, non-parametric property, processing time and large-scale estimation, to validate the effectiveness and robustness of the PSPF algorithm.

scholarly journals Using strong gravitational lensing to probe the post-reionization H i power spectrum

2020 ◽  
Vol 498 (3) ◽  
pp. 3275-3282
Author(s):  
Urvashi Arora ◽  
Prasun Dutta
Keyword(s):  
Power Spectrum ◽  
Gravitational Lensing ◽  
Neutral Hydrogen ◽  
Point Sources ◽  
Dark Matter Halo ◽  
Strong Gravitational Lensing ◽  
The Galaxy ◽  
Formation And Evolution ◽  
The Universe ◽  
Expected Signal

ABSTRACT Probing statistical distribution of the neutral hydrogen (H i) using the redshifted 21-cm hyperfine-transition spectral line holds the key to understand the formation and evolution of the matter density in the Universe. The two-point statistics of the H i distribution can be estimated by measuring the power spectrum of the redshifted 21-cm signal using visibility correlation. A major challenge in this regard is that the expected signal is weak compared to the foreground contribution from the Galactic synchrotron emission and extragalactic point sources in the observing frequencies. In this work, we investigate the possibility of detecting the power spectrum of the redshifted 21-cm signal by using strong gravitational lensing of the galaxy clusters. This method has the advantage that it only enhances the H i signal and not the diffuse Galactic foreground. Based on four simple models of the cluster potentials, we show that the strong lenses at relatively lower redshifts with more than one dark matter halo significantly enhance the 21-cm signal from the post-reionization era. We discuss the merits and demerits of the method and the future studies required for further investigations.

scholarly journals Foreground modelling via Gaussian process regression: an application to HERA data

2020 ◽  
Vol 495 (3) ◽  
pp. 2813-2826
Author(s):  
Abhik Ghosh ◽  
Florent Mertens ◽  
Gianni Bernardi ◽  
Mário G Santos ◽  
Nicholas S Kern ◽  
...  
Keyword(s):  
Power Spectrum ◽  
Gaussian Process ◽  
Spectral Properties ◽  
Real Life ◽  
Gaussian Process Regression ◽  
Line Of Sight ◽  
Periodic Signal ◽  
Variance Model ◽  
Systematic Effects ◽  
Residual Power

ABSTRACT The key challenge in the observation of the redshifted 21-cm signal from cosmic reionization is its separation from the much brighter foreground emission. Such separation relies on the different spectral properties of the two components, although, in real life, the foreground intrinsic spectrum is often corrupted by the instrumental response, inducing systematic effects that can further jeopardize the measurement of the 21-cm signal. In this paper, we use Gaussian Process Regression to model both foreground emission and instrumental systematics in ∼2 h of data from the Hydrogen Epoch of Reionization Array. We find that a simple co-variance model with three components matches the data well, giving a residual power spectrum with white noise properties. These consist of an ‘intrinsic’ and instrumentally corrupted component with a coherence scale of 20 and 2.4 MHz, respectively (dominating the line-of-sight power spectrum over scales k∥ ≤ 0.2 h cMpc−1) and a baseline-dependent periodic signal with a period of ∼1 MHz (dominating over k∥ ∼ 0.4–0.8 h cMpc−1), which should be distinguishable from the 21-cm Epoch of Reionization signal whose typical coherence scale is ∼0.8 MHz.

scholarly journals Calibration requirements for epoch of reionization 21-cm signal observations – I. Effect of time-correlated gains

2020 ◽  
Vol 495 (4) ◽  
pp. 3683-3694
Author(s):  
Jais Kumar ◽  
Prasun Dutta ◽  
Nirupam Roy
Keyword(s):  
Power Spectrum ◽  
Dynamic Range ◽  
Neutral Hydrogen ◽  
High Dynamic Range ◽  
Time Correlation ◽  
Brightness Distribution ◽  
Gain Error ◽  
High Dynamic ◽  
Gain Errors ◽  
Compact Sources

ABSTRACT The residual gain errors add to the systematics of the radio interferometric observations. In case of the high dynamic range observations, these systematic effects dominates over the thermal noise of the observation. In this work, we investigate the effect of time-correlated residual gain errors in the estimation of the power spectrum of the sky brightness distribution in high dynamic range observations. Particularly, we discuss a methodology to estimate the bias in the power spectrum estimator of the redshifted 21-cm signal from neutral hydrogen in the presence of bright extragalactic compact sources. We find, that for the visibility-based power spectrum estimators, particularly those use nearby baseline correlations to avoid noise bias, the bias in the power spectrum arises mainly from the time correlation in the residual gain error. The bias also depends on the baseline distribution for a particular observation. Analytical calculations show that the bias is dominant for certain types of baseline pairs used for the visibility correlation. We perform simulated observation of extragalactic compact sources in the presence of residual gain errors with the Giant Metrewave Radio Telescope like array and estimate the bias in the power spectrum. Our results indicate that in order to estimate the redshifted 21-cm power spectrum, better calibration techniques, and estimator development are required.

Large‐Scale Power Spectrum and Cosmological Parameters from SFI Peculiar Velocities

1999 ◽  
Vol 523 (1) ◽  
pp. 1-15 ◽  
Author(s):  
Wolfram Freudling ◽  
Idit Zehavi ◽  
Luiz N. da Costa ◽  
Avishai Dekel ◽  
Amiram Eldar ◽  
...  
Keyword(s):  
Power Spectrum ◽  
Large Scale ◽  
Cosmological Parameters ◽  
Peculiar Velocities

scholarly journals Estimating CO2 Emissions from Large Scale Coal-Fired Power Plants Using OCO-2 Observations and Emission Inventories

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 811
Author(s):  
Yaqin Hu ◽  
Yusheng Shi
Keyword(s):  
Power Plant ◽  
Co2 Emissions ◽  
Atmospheric Carbon Dioxide ◽  
Power Plants ◽  
Large Scale ◽  
Global Climate ◽  
Gaussian Model ◽  
Point Sources ◽  
Emission Inventories ◽  
Bottom Up

The concentration of atmospheric carbon dioxide (CO2) has increased rapidly worldwide, aggravating the global greenhouse effect, and coal-fired power plants are one of the biggest contributors of greenhouse gas emissions in China. However, efficient methods that can quantify CO2 emissions from individual coal-fired power plants with high accuracy are needed. In this study, we estimated the CO2 emissions of large-scale coal-fired power plants using Orbiting Carbon Observatory-2 (OCO-2) satellite data based on remote sensing inversions and bottom-up methods. First, we mapped the distribution of coal-fired power plants, displaying the total installed capacity, and identified two appropriate targets, the Waigaoqiao and Qinbei power plants in Shanghai and Henan, respectively. Then, an improved Gaussian plume model method was applied for CO2 emission estimations, with input parameters including the geographic coordinates of point sources, wind vectors from the atmospheric reanalysis of the global climate, and OCO-2 observations. The application of the Gaussian model was improved by using wind data with higher temporal and spatial resolutions, employing the physically based unit conversion method, and interpolating OCO-2 observations into different resolutions. Consequently, CO2 emissions were estimated to be 23.06 ± 2.82 (95% CI) Mt/yr using the Gaussian model and 16.28 Mt/yr using the bottom-up method for the Waigaoqiao Power Plant, and 14.58 ± 3.37 (95% CI) and 14.08 Mt/yr for the Qinbei Power Plant, respectively. These estimates were compared with three standard databases for validation: the Carbon Monitoring for Action database, the China coal-fired Power Plant Emissions Database, and the Carbon Brief database. The comparison found that previous emission inventories spanning different time frames might have overestimated the CO2 emissions of one of two Chinese power plants on the two days that the measurements were made. Our study contributes to quantifying CO2 emissions from point sources and helps in advancing satellite-based monitoring techniques of emission sources in the future; this helps in reducing errors due to human intervention in bottom-up statistical methods.

scholarly journals An Accurate Reconstruction of CMB E Mode Signal over Large Angular Scales using Prior Information of CMB Covariance Matrix in ILC Algorithm

2020 ◽  
Author(s):  
Ujjal Purkayastha ◽  
Vipin Sudevan ◽  
Rajib Saha
Keyword(s):  
Power Spectrum ◽  
Linear Combination ◽  
Covariance Matrix ◽  
Large Scale ◽  
Prior Information ◽  
Future Generation ◽  
Temperature Anisotropy ◽  
Satellite Mission ◽  
Angular Power Spectrum ◽  
Accurate Reconstruction

Abstract Recently, the internal-linear-combination (ILC) method was investigated extensively in the context of reconstruction of Cosmic Microwave Background (CMB) temperature anisotropy signal using observations obtained by WMAP and Planck satellite missions. In this article, we, for the first time, apply the ILC method to reconstruct the large scale CMB E mode polarization signal, which could probe the ionization history, using simulated observations of 15 frequency CMB polarization maps of future generation Cosmic Origin Explorer (COrE) satellite mission. We find that the clean power spectra, from the usual ILC, are strongly biased due to non zero CMB-foregrounds chance correlations. In order to address the issues of bias and errors we extend and improve the usual ILC method for CMB E mode reconstruction by incorporating prior information of theoretical E mode angular power spectrum while estimating the weights for linear combination of input maps (Sudevan & Saha 2018b). Using the E mode covariance matrix effectively suppresses the CMB-foreground chance correlation power leading to an accurate reconstruction of cleaned CMB E mode map and its angular power spectrum. We compare the performance of the usual ILC and the new method over large angular scales and show that the later produces significantly statistically improved results than the former. The new E mode CMB angular power spectrum contains neither any significant negative bias at the low multipoles nor any positive foreground bias at relatively higher mutlipoles. The error estimates of the cleaned spectrum agree very well with the cosmic variance induced error.

scholarly journals The anisotropy of the power spectrum in periodic cosmological simulations

2021 ◽  
Vol 503 (4) ◽  
pp. 5638-5645
Author(s):  
Gábor Rácz ◽  
István Szapudi ◽  
István Csabai ◽  
László Dobos
Keyword(s):  
Dark Matter ◽  
Power Spectrum ◽  
Large Scale ◽  
Cold Dark Matter ◽  
Initial Conditions ◽  
Power Spectra ◽  
Cosmological Simulations ◽  
Spherical Collapse ◽  
Lower Amplitude ◽  
Hydrodynamical Simulations

ABSTRACT The classical gravitational force on a torus is anisotropic and always lower than Newton’s 1/r2 law. We demonstrate the effects of periodicity in dark matter only N-body simulations of spherical collapse and standard Lambda cold dark matter (ΛCDM) initial conditions. Periodic boundary conditions cause an overall negative and anisotropic bias in cosmological simulations of cosmic structure formation. The lower amplitude of power spectra of small periodic simulations is a consequence of the missing large-scale modes and the equally important smaller periodic forces. The effect is most significant when the largest mildly non-linear scales are comparable to the linear size of the simulation box, as often is the case for high-resolution hydrodynamical simulations. Spherical collapse morphs into a shape similar to an octahedron. The anisotropic growth distorts the large-scale ΛCDM dark matter structures. We introduce the direction-dependent power spectrum invariant under the octahedral group of the simulation volume and show that the results break spherical symmetry.

scholarly journals Demonstrating the Tapered Gridded Estimator (TGE) for the Cosmological HI 21-cm Power Spectrum using 150 MHz GMRT observations

2020 ◽  
Author(s):  
Srijita Pal ◽  
Somnath Bharadwaj ◽  
Abhik Ghosh ◽  
Samir Choudhuri
Keyword(s):  
Power Spectrum ◽  
Unbiased Estimate ◽  
Power Spectra ◽  
Neutral Hydrogen ◽  
Statistical Error ◽  
Temperature Fluctuations ◽  
Radio Frequency Interference ◽  
Rectangular Region ◽  
Angular Power Spectrum ◽  
The Mean

Abstract We apply the Tapered Gridded Estimator (TGE) for estimating the cosmological 21-cm power spectrum from 150 MHz GMRT observations which corresponds to the neutral hydrogen (HI) at redshift z = 8.28. Here TGE is used to measure the Multi-frequency Angular Power Spectrum (MAPS) Cℓ(Δν) first, from which we estimate the 21-cm power spectrum P(k⊥, k∥). The data here are much too small for a detection, and the aim is to demonstrate the capabilities of the estimator. We find that the estimated power spectrum is consistent with the expected foreground and noise behaviour. This demonstrates that this estimator correctly estimates the noise bias and subtracts this out to yield an unbiased estimate of the power spectrum. More than $47\%$ of the frequency channels had to be discarded from the data owing to radio-frequency interference, however the estimated power spectrum does not show any artifacts due to missing channels. Finally, we show that it is possible to suppress the foreground contribution by tapering the sky response at large angular separations from the phase center. We combine the k modes within a rectangular region in the ‘EoR window’ to obtain the spherically binned averaged dimensionless power spectra Δ2(k) along with the statistical error σ associated with the measured Δ2(k). The lowest k-bin yields Δ2(k) = (61.47)2 K2 at k = 1.59 Mpc−1, with σ = (27.40)2 K2. We obtain a 2 σ upper limit of (72.66)2 K2 on the mean squared HI 21-cm brightness temperature fluctuations at k = 1.59 Mpc−1.

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