scholarly journals Analytic Calculation of Covariance between Cosmological Parameters from Correlated Data Sets, with an Application to SPTpol

2019 ◽  
Vol 888 (1) ◽  
pp. 26 ◽  
Author(s):  
Joshua A. Kable ◽  
Graeme E. Addison ◽  
Charles L. Bennett
Keyword(s):  
Cosmological Parameters ◽  
Correlated Data ◽  
Analytic Calculation ◽  
Data Sets

scholarly journals Comparison of SMMR and SSM/I passive microwave data collected over Antarctica

1993 ◽  
Vol 17 ◽  
pp. 131-136 ◽  
Author(s):  
Kenneth C. Jezek ◽  
Carolyn J. Merry ◽  
Don J. Cavalieri
Keyword(s):  
Brightness Temperature ◽  
Microwave Radiometer ◽  
Correlated Data ◽  
Data Sets ◽  
Microwave Brightness Temperature ◽  
Brightness Temperatures ◽  
Scatter Plots ◽  
Microwave Imager ◽  
Highly Correlated ◽  
The Antarctic

Spaceborne data are becoming sufficiently extensive spatially and sufficiently lengthy over time to provide important gauges of global change. There is a potentially long record of microwave brightness temperature from NASA's Scanning Multichannel Microwave Radiometer (SMMR), followed by the Navy's Special Sensor Microwave Imager (SSM/I). Thus it is natural to combine data from successive satellite programs into a single, long record. To do this, we compare brightness temperature data collected during the brief overlap period (7 July-20 August 1987) of SMMR and SSM/I. Only data collected over the Antarctic ice sheet are used to limit spatial and temporal complications associated with the open ocean and sea ice. Linear regressions are computed from scatter plots of complementary pairs of channels from each sensor revealing highly correlated data sets, supporting the argument that there are important relative calibration differences between the two instruments. The calibration scheme was applied to a set of average monthly brightness temperatures for a sector of East Antarctica.

Time Series Control Charts for Correlated and Contaminated Data

1986 ◽  
Vol 108 (3) ◽  
pp. 219-226 ◽  
Author(s):  
B. D. Notohardjono ◽  
D. S. Ermer
Keyword(s):  
Time Series ◽  
Control Charts ◽  
Maximum Principal Stress ◽  
Correlated Data ◽  
Model Parameters ◽  
Data Sets ◽  
Process Change ◽  
Contaminated Data ◽  
Model Residuals ◽  
Control Limits

This paper discusses the development of control charts for correlated and contaminated data. For illustration the charts were applied to a set of maximum principal-stress data at two locations on a blast furnace shell. The Dynamic Data System (DDS) approach was used to model the correlated data which contained several types of discrepancies. After the standard DDS models were found, control charts for the averages and variances of the model residuals were constructed for two data sets. For more effective analysis, two methods for calculating the control limits for both charts are given. With this approach, dynamic process change, such as an increase in the production rate or the wearing out of the sacrificial lining, can be detected and separated from data with collection errors from instrument malfunctions. Furthermore, the tap hole opening timing is identified from the DDS model parameters, to help verify the time series model.

scholarly journals Comparison of SMMR and SSM/I passive microwave data collected over Antarctica

1993 ◽  
Vol 17 ◽  
pp. 131-136 ◽  
Author(s):  
Kenneth C. Jezek ◽  
Carolyn J. Merry ◽  
Don J. Cavalieri
Keyword(s):  
Brightness Temperature ◽  
Microwave Radiometer ◽  
Correlated Data ◽  
Data Sets ◽  
Microwave Brightness Temperature ◽  
Brightness Temperatures ◽  
Scatter Plots ◽  
Microwave Imager ◽  
Highly Correlated ◽  
The Antarctic

Spaceborne data are becoming sufficiently extensive spatially and sufficiently lengthy over time to provide important gauges of global change. There is a potentially long record of microwave brightness temperature from NASA's Scanning Multichannel Microwave Radiometer (SMMR), followed by the Navy's Special Sensor Microwave Imager (SSM/I). Thus it is natural to combine data from successive satellite programs into a single, long record. To do this, we compare brightness temperature data collected during the brief overlap period (7 July-20 August 1987) of SMMR and SSM/I. Only data collected over the Antarctic ice sheet are used to limit spatial and temporal complications associated with the open ocean and sea ice. Linear regressions are computed from scatter plots of complementary pairs of channels from each sensor revealing highly correlated data sets, supporting the argument that there are important relative calibration differences between the two instruments. The calibration scheme was applied to a set of average monthly brightness temperatures for a sector of East Antarctica.

scholarly journals Constraining the ΛCDM and Galileon models with recent cosmological data

2017 ◽  
Vol 600 ◽  
pp. A40 ◽  
Author(s):  
J. Neveu ◽  
V. Ruhlmann-Kleider ◽  
P. Astier ◽  
M. Besançon ◽  
J. Guy ◽  
...  
Keyword(s):  
Cosmological Parameters ◽  
Acoustic Oscillation ◽  
Gravity Models ◽  
Accelerated Expansion ◽  
Data Sets ◽  
Late Time ◽  
Lunar Laser Ranging ◽  
Cosmological Constraints ◽  
Cosmological Data ◽  
Speed Of Propagation

Aims. The Galileon theory belongs to the class of modified gravity models that can explain the late-time accelerated expansion of the Universe. In previous works, cosmological constraints on the Galileon model were derived, both in the uncoupled case and with a disformal coupling of the Galileon field to matter. There, we showed that these models agree with the most recent cosmological data. In this work, we used updated cosmological data sets to derive new constraints on Galileon models, including the case of a constant conformal Galileon coupling to matter. We also explored the tracker solution of the uncoupled Galileon model. Methods. After updating our data sets, especially with the latest Planck data and baryonic acoustic oscillation (BAO) measurements, we fitted the cosmological parameters of the ΛCDM and Galileon models. The same analysis framework as in our previous papers was used to derive cosmological constraints, using precise measurements of cosmological distances and of the cosmic structure growth rate. Results. We show that all tested Galileon models are as compatible with cosmological data as the ΛCDM model. This means that present cosmological data are not accurate enough to distinguish clearly between the two theories. Among the different Galileon models, we find that a conformal coupling is not favoured, contrary to the disformal coupling which is preferred at the 2.3σ level over the uncoupled case. The tracker solution of the uncoupled Galileon model is also highly disfavoured owing to large tensions with supernovae and Planck+BAO data. However, outside of the tracker solution, the general uncoupled Galileon model, as well as the general disformally coupled Galileon model, remain the most promising Galileon scenarios to confront with future cosmological data. Finally, we also discuss constraints coming from the Lunar Laser Ranging experiment and gravitational wave speed of propagation.

scholarly journals Technical Note: Trend estimation from irregularly sampled, correlated data

2009 ◽  
Vol 9 (6) ◽  
pp. 27675-27692
Author(s):  
T. von Clarmann ◽  
G. Stiller ◽  
U. Grabowski ◽  
J. Orphal
Keyword(s):  
Regression Line ◽  
Technical Note ◽  
Correlated Data ◽  
Data Sets ◽  
State Variable ◽  
Error Covariance ◽  
Data Points ◽  
Atmospheric State ◽  
Global Data Sets ◽  
Global Data

Abstract. Estimation of a trend of an atmospheric state variable is often performed by fitting a linear regression line to a set of data of this variable sampled at different times. Often these data are irregularly sampled in space and time and clustered in a sense that error correlations among data points cause a similar error of data points sampled at similar times. Since this can affect the estimated trend, we suggest to take the full error covariance matrix of the data into account. Superimposed periodic variations can be jointly fitted in a straight forward manner, even if the shape of the periodic function is not known. Global data sets, particularly satellite data, can form the basis to estimate the error correlations.

scholarly journals Improving galaxy clustering measurements with deep learning: analysis of the DECaLS DR7 data

2020 ◽  
Vol 495 (2) ◽  
pp. 1613-1640 ◽  
Author(s):  
Mehdi Rezaie ◽  
Hee-Jong Seo ◽  
Ashley J Ross ◽  
Razvan C Bunescu
Keyword(s):  
Dark Energy ◽  
Large Scale ◽  
Cosmological Parameters ◽  
Density Field ◽  
Data Sets ◽  
Galaxy Surveys ◽  
The Neural Network ◽  
Systematic Effects ◽  
Robust Measurements ◽  
Emission Line Galaxies

ABSTRACT Robust measurements of cosmological parameters from galaxy surveys rely on our understanding of systematic effects that impact the observed galaxy density field. In this paper, we present, validate, and implement the idea of adopting the systematics mitigation method of artificial neural networks for modelling the relationship between the target galaxy density field and various observational realities including but not limited to Galactic extinction, seeing, and stellar density. Our method by construction allows a wide class of models and alleviates overtraining by performing k-fold cross-validation and dimensionality reduction via backward feature elimination. By permuting the choice of the training, validation, and test sets, we construct a selection mask for the entire footprint. We apply our method on the extended Baryon Oscillation Spectroscopic Survey (eBOSS) Emission Line Galaxies (ELGs) selection from the Dark Energy Camera Legacy Survey (DECaLS) Data Release 7 and show that the spurious large-scale contamination due to imaging systematics can be significantly reduced by up-weighting the observed galaxy density using the selection mask from the neural network and that our method is more effective than the conventional linear and quadratic polynomial functions. We perform extensive analyses on simulated mock data sets with and without systematic effects. Our analyses indicate that our methodology is more robust to overfitting compared to the conventional methods. This method can be utilized in the catalogue generation of future spectroscopic galaxy surveys such as eBOSS and Dark Energy Spectroscopic Instrument (DESI) to better mitigate observational systematics.

scholarly journals Constraining the astrophysics and cosmology from 21 cm tomography using deep learning with the SKA

2020 ◽  
Vol 494 (4) ◽  
pp. 5761-5774 ◽  
Author(s):  
Sultan Hassan ◽  
Sambatra Andrianomena ◽  
Caitlin Doughty
Keyword(s):  
Thermal Noise ◽  
Cosmological Parameters ◽  
Hubble Constant ◽  
Great Accuracy ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Density Parameter ◽  
Huge Data ◽  
Learning Techniques ◽  
Fluctuation Amplitude

ABSTRACT Future Square Kilometre Array (SKA) surveys are expected to generate huge data sets of 21 cm maps on cosmological scales from the Epoch of Reionization. We assess the viability of exploiting machine learning techniques, namely, convolutional neural networks (CNNs), to simultaneously estimate the astrophysical and cosmological parameters from 21 cm maps from seminumerical simulations. We further convert the simulated 21 cm maps into SKA-like mock maps using the detailed SKA antennae distribution, thermal noise, and a recipe for foreground cleaning. We successfully design two CNN architectures (VGGNet-like and ResNet-like) that are both efficiently able to extract simultaneously three astrophysical parameters, namely the photon escape fraction (fesc), the ionizing emissivity power dependence on halo mass (Cion), and the ionizing emissivity redshift evolution index (Dion), and three cosmological parameters, namely the matter density parameter (Ωm), the dimensionless Hubble constant (h), and the matter fluctuation amplitude (σ8), from 21 cm maps at several redshifts. With the presence of noise from SKA, our designed CNNs are still able to recover these astrophysical and cosmological parameters with great accuracy ($R^{2} \gt 92{{\ \rm per\ cent}}$), improving to $R^{2} \gt 99{{\ \rm per\ cent}}$ towards low-redshift and low neutral fraction values. Our results show that future 21 cm observations can play a key role to break degeneracy between models and tightly constrain the astrophysical and cosmological parameters, using only few frequency channels.

scholarly journals Quantifying Suspiciousness within correlated data sets

2020 ◽  
Vol 496 (4) ◽  
pp. 4647-4653 ◽  
Author(s):  
Pablo Lemos ◽  
Fabian Köhlinger ◽  
Will Handley ◽  
Benjamin Joachimi ◽  
Lorne Whiteway ◽  
...  
Keyword(s):  
Parameter Space ◽  
Internal Consistency ◽  
Bayesian Method ◽  
Correlated Data ◽  
Data Sets ◽  
Summary Statistics ◽  
Photometric Redshift ◽  
Data Space

ABSTRACT We propose a principled Bayesian method for quantifying tension between correlated data sets with wide uninformative parameter priors. This is achieved by extending the Suspiciousness statistic, which is insensitive to priors. Our method uses global summary statistics, and as such it can be used as a diagnostic for internal consistency. We show how our approach can be combined with methods that use parameter space and data space to identify the existing internal discrepancies. As an example, we use it to test the internal consistency of the KiDS-450 data in four photometric redshift bins, and to recover controlled internal discrepancies in simulated KiDS data. We propose this as a diagnostic of internal consistency for present and future cosmological surveys, and as a tension metric for data sets that have non-negligible correlation, such as Large Synoptic Spectroscopic Survey and Euclid.

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