scholarly journals Adaptive density estimator for galaxy surveys

2014 ◽  
Vol 11 (S308) ◽  
pp. 242-247
Author(s):  
Enn Saar
Keyword(s):  
Data Driven ◽  
Classification Algorithms ◽  
Density Estimator ◽  
Galaxy Surveys ◽  
Additional Information ◽  
Galaxy Distribution ◽  
Local Sample ◽  
The Galaxy ◽  
Local Properties ◽  
Anisotropic Kernel

AbstractGalaxy number or luminosity density serves as a basis for many structure classification algorithms. Several methods are used to estimate this density. Among them kernel methods have probably the best statistical properties and allow also to estimate the local sample errors of the estimate. We introduce a kernel density estimator with an adaptive data-driven anisotropic kernel, describe its properties and demonstrate the wealth of additional information it gives us about the local properties of the galaxy distribution.

scholarly journals Constraining the growth rate of structure with phase correlations

2020 ◽  
Vol 497 (2) ◽  
pp. 1765-1790
Author(s):  
Joyce Byun ◽  
Felipe Oliveira Franco ◽  
Cullan Howlett ◽  
Camille Bonvin ◽  
Danail Obreschkow
Keyword(s):  
Growth Rate ◽  
Correlation Function ◽  
Power Spectrum ◽  
Large Scale ◽  
Large Scale Structure ◽  
Density Field ◽  
Galaxy Surveys ◽  
Additional Information ◽  
Forecasting Method ◽  
The Galaxy

ABSTRACT We show that correlations between the phases of the galaxy density field in redshift space provide additional information about the growth rate of large-scale structure that is complementary to the power-spectrum multipoles. In particular, we consider the multipoles of the line correlation function (LCF), which correlates phases between three collinear points, and use the Fisher forecasting method to show that the LCF multipoles can break the degeneracy between the measurement of the growth rate of structure f and the amplitude of perturbations σ8 that is present in the power-spectrum multipoles at large scales. This leads to an improvement in the measurement of f and σ8 by up to 220 per cent for $k_{\rm max} = 0.15 \, h\, \mathrm{Mpc}^{-1}$ and up to 50 per cent for $k_{\rm max} = 0.30 \, h\, \mathrm{Mpc}^{-1}$ at redshift z = 0.25, with respect to power-spectrum measurements alone for the upcoming generation of galaxy surveys like DESI and Euclid. The average improvements in the constraints on f and σ8 for $k_{\rm max} = 0.15 \, h\, \mathrm{Mpc}^{-1}$ are ∼90 per cent for the DESI BGS sample with mean redshift $\overline{z}=0.25$, ∼40 per cent for the DESI ELG sample with $\overline{z}=1.25$, and ∼40 per cent for the Euclid Hα galaxies with $\overline{z}=1.3$. For $k_{\rm max} = 0.30 \, h\, \mathrm{Mpc}^{-1}$, the average improvements are ∼40 per cent for the DESI BGS sample and ∼20 per cent for both the DESI ELG and Euclid Hα galaxies.

Hybrid-Data Approach for Estimating Trip Purposes

2021 ◽  
pp. 036119812110184
Author(s):  
Xiaoling Luo ◽  
Adrian Cottam ◽  
Yao-Jan Wu ◽  
Yangsheng Jiang
Keyword(s):  
Transportation Systems ◽  
Data Driven ◽  
Trip Purpose ◽  
Additional Information ◽  
Point Of Interest ◽  
Average Accuracy ◽  
Hybrid Data ◽  
Added Benefit ◽  
Data Source ◽  
High Level

Trip purpose information plays a significant role in transportation systems. Existing trip purpose information is traditionally collected through human observation. This manual process requires many personnel and a large amount of resources. Because of this high cost, automated trip purpose estimation is more attractive from a data-driven perspective, as it could improve the efficiency of processes and save time. Therefore, a hybrid-data approach using taxi operations data and point-of-interest (POI) data to estimate trip purposes was developed in this research. POI data, an emerging data source, was incorporated because it provides a wealth of additional information for trip purpose estimation. POI data, an open dataset, has the added benefit of being readily accessible from online platforms. Several techniques were developed and compared to incorporate this POI data into the hybrid-data approach to achieve a high level of accuracy. To evaluate the performance of the approach, data from Chengdu, China, were used. The results show that the incorporation of POI information increases the average accuracy of trip purpose estimation by 28% compared with trip purpose estimation not using the POI data. These results indicate that the additional trip attributes provided by POI data can increase the accuracy of trip purpose estimation.

scholarly journals Analysis of the Galaxy Distribution using Multiscale Methods

2002 ◽  
Author(s):  
Philippe Querre ◽  
Jean-Luc Starck ◽  
Vicent J. Martinez
Keyword(s):  
Multiscale Methods ◽  
Galaxy Distribution ◽  
The Galaxy

Connectionist Systems and Signal Processing Techniques Applied to the Parameterization of Stellar Spectra

2010 ◽  
pp. 187-203
Author(s):  
Diego Ordóñez ◽  
Carlos Dafonte ◽  
Bernardino Arcay ◽  
Minia Manteiga
Keyword(s):  
Effective Temperature ◽  
Wavelet Transforms ◽  
European Space Agency ◽  
Wavelength Region ◽  
Atmospheric Parameter ◽  
Automated Classification ◽  
Stellar Spectra ◽  
Additional Information ◽  
Space Agency ◽  
The Galaxy

A stellar spectrum is the finger-print identification of a particular star, the result of the radiation transport through its atmosphere. The physical conditions in the stellar atmosphere, its effective temperature, surface gravity, and the presence and abundance of chemical elements explain the observed features in the stellar spectra, such as the shape of the overall continuum and the presence and strength of particular lines and bands. The derivation of the atmospheric stellar parameters from a representative sample of stellar spectra collected by ground-based and spatial telescopes is essential when a realistic view of the Galaxy and its components is to be obtained. In the last decade, extensive astronomical surveys recording information of large portions of the sky have become a reality since the development of robotic or semi-automated telescopes. The Gaia satellite is one of the key missions of the European Space Agency (ESA) and its launch is planned for 2011. Gaia will carry out the so-called Galaxy Census by extracting precise information on the nature of its main constituents, including the spectra of objects (Wilkinson, 2005). Traditional methods for the extraction of the fundamental atmospheric stellar parameters (effective temperature (Teff), gravity (log G), metallicity ([Fe/H]), and abundance of alpha elements [a/Fe], elements integer multiples of the mass of the helium nucleus) are time-consuming and unapproachable for a massive survey involving 1 billion objects (about 1% of the Galaxy constituents) such as Gaia. This work presents the results of the authors’ study and shows the feasibility of an automated extraction of the previously mentioned stellar atmospheric parameters from near infrared spectra in the wavelength region of the Gaia Radial Velocity Spectrograph (RVS). The authors’ approach is based on a technique that has already been applied to problems of the non-linear parameterization of signals: artificial neural networks. It breaks ground in the consideration of transformed domains (Fourier and Wavelet Transforms) during the preprocessing stage of the spectral signals in order to select the frequency resolution that is best suited for each atmospheric parameter. The authors have also progressed in estimating the noise (SNR) that blurs the signal on the basis of its power spectrum and the application of noise-dependant algorithms of parameterization. This study has provided additional information that allows them to progress in the development of hybrid systems devoted to the automated classification of stellar spectra.

scholarly journals A study on the statistical significance of mutual information between morphology of a galaxy and its large-scale environment

2020 ◽  
Vol 497 (4) ◽  
pp. 4077-4090 ◽  
Author(s):  
Suman Sarkar ◽  
Biswajit Pandey
Keyword(s):  
Mutual Information ◽  
Large Scale ◽  
Statistical Significance ◽  
Sloan Digital Sky Survey ◽  
Data Sets ◽  
Information Theoretic ◽  
Galaxy Distribution ◽  
Sky Survey ◽  
The Galaxy ◽  
Physical Correlations

ABSTRACT A non-zero mutual information between morphology of a galaxy and its large-scale environment is known to exist in Sloan Digital Sky Survey (SDSS) upto a few tens of Mpc. It is important to test the statistical significance of these mutual information if any. We propose three different methods to test the statistical significance of these non-zero mutual information and apply them to SDSS and Millennium run simulation. We randomize the morphological information of SDSS galaxies without affecting their spatial distribution and compare the mutual information in the original and randomized data sets. We also divide the galaxy distribution into smaller subcubes and randomly shuffle them many times keeping the morphological information of galaxies intact. We compare the mutual information in the original SDSS data and its shuffled realizations for different shuffling lengths. Using a t-test, we find that a small but statistically significant (at $99.9{{\ \rm per\ cent}}$ confidence level) mutual information between morphology and environment exists upto the entire length-scale probed. We also conduct another experiment using mock data sets from a semi-analytic galaxy catalogue where we assign morphology to galaxies in a controlled manner based on the density at their locations. The experiment clearly demonstrates that mutual information can effectively capture the physical correlations between morphology and environment. Our analysis suggests that physical association between morphology and environment may extend to much larger length-scales than currently believed, and the information theoretic framework presented here can serve as a sensitive and useful probe of the assembly bias and large-scale environmental dependence of galaxy properties.

scholarly journals Structure of Neighboring Superclusters: A Quantitative Analysis

1983 ◽  
Vol 104 ◽  
pp. 265-271 ◽  
Author(s):  
J. Einasto ◽  
A. Klypin ◽  
S. Shandarin
Keyword(s):  
Quantitative Analysis ◽  
Numerical Methods ◽  
Correlation Analysis ◽  
Quantitative Method ◽  
Galaxy Distribution ◽  
The Galaxy

So far the galaxy correlation analysis was the only quantitative method used to describe the distribution of galaxies in space. Here we consider other numerical methods to treat impersonally various aspects of the galaxy distribution.

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