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相关论文: Robust PCA and MIC statistics of baryons in early …

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(Abridged) Motivated by forthcoming data from the Sloan Digital Sky Survey, we present a theoretical framework that can be used to interpret Principal Component Analysis (PCA) of disk galaxy properties. We use the formalism introduced by…

天体物理学 · 物理学 2007-05-23 Alberto Conti , Barbara S. Ryden , David H. Weinberg

We investigate the suppression of the baryon density fluctuations compared to the dark matter in the linear regime. Previous calculations predict that the suppression occurs up to a characteristic mass scale of ~ 1,000,000 solar masses,…

天体物理学 · 物理学 2008-11-26 Smadar Naoz , Rennan Barkana

We use Principal Component Analysis (PCA) to analyze a volume-limited sample from the SDSS and explore how cosmic web environments affect the interrelations between various galaxy properties, such as $(u-r)$ colour, stellar mass, specific…

星系天体物理 · 物理学 2025-06-24 Anindita Nandi , Biswajit Pandey

We show that the first 10 eigencomponents of the Karhunen-Lo\`eve expansion or Principal Component Analysis (PCA) provide a robust classification scheme for the identification of stars, galaxies and quasi-stellar objects from multi-band…

天体物理学 · 物理学 2009-11-07 Remi A. Cabanac , Valerie de Lapparent , Paul Hickson

We use Principal Component Analysis (PCA) to study the gas dynamics in numerical simulations of typical MCs. Our simulations account for the non-isothermal nature of the gas and include a simplified treatment of the time-dependent gas…

太阳与恒星天体物理 · 物理学 2015-06-18 Erik Bertram , Rahul Shetty , Simon C. O. Glover , Ralf S. Klessen , Julia Roman-Duval , Christoph Federrath

We study the correlations between the structural parameters of dark matter haloes using Principal Component Analysis (PCA). We consider a set of eight parameters, six of which are commonly used to characterize dark matter halo properties:…

宇宙学与河外天体物理 · 物理学 2015-05-27 Ramin A. Skibba , Andrea V. Maccio'

Principal Component Analysis (PCA) is applied to a variety of blazars to examine X-ray spectral variability. Data from nine different objects are analysed in two ways: long-term, which examines variability trends across years or decades,…

高能天体物理现象 · 物理学 2018-08-08 Dennis Gallant , Luigi C. Gallo , Michael L. Parker

Principal component analysis (PCA) is a widely employed statistical tool used primarily for dimensionality reduction. However, it is known to be adversely affected by the presence of outlying observations in the sample, which is quite…

统计方法学 · 统计学 2023-09-26 Subhrajyoty Roy , Ayanendranath Basu , Abhik Ghosh

The Laser Interferometer Space Antenna (LISA) will provide us with a unique opportunity to observe the early inspiral phase of supermassive binary black holes (SMBBHs) in the mass range of $10^5-10^6\,M_{\odot}$, that lasts for several…

广义相对论与量子宇宙学 · 物理学 2023-03-09 Sayantani Datta

Varying fundamental constants (VFC) [e.g., the fine-structure constant, $\alpha_{\rm EM}$] can arise in numerous extended cosmologies. Through their effect on the decoupling of baryons and photons during last scattering and reionisation,…

宇宙学与河外天体物理 · 物理学 2021-10-13 Luke Hart , Jens Chluba

(Abridged) Environmental differences in the stellar populations of early-type galaxies are explored using principal component analysis (PCA), focusing on differences between elliptical galaxies in Hickson Compact Groups (HCGs) and in the…

天体物理学 · 物理学 2009-11-11 I. Ferreras , A. Pasquali , R. R. de Carvalho , I. G. de la Rosa , O. Lahav

We use the measured fluxes of polycyclic aromatic hydrocarbon (PAH) emission features at 6.2, 7.7, 8.6, 11.0 and 11.2 $\mu$m in the reflection nebula NGC 2023 to carry out a principal component analysis (PCA) as a means to study previously…

星系天体物理 · 物理学 2020-10-21 Ameek Sidhu , Els Peeters , Jan Cami , Collin Knight

One of the main tasks for present and future dark energy surveys is to determine whether the dark energy is dynamical or not. To illustrate this from data, it is commonly used to parameterize the dark energy equation of state w as several…

宇宙学与河外天体物理 · 物理学 2015-03-17 Seokcheon Lee

Principal component analysis is a statistical method, which lowers the number of important variables in a data set. The use of this method for the bursts' spectra and afterglows is discussed in this paper. The analysis indicates that three…

天体物理学 · 物理学 2015-05-13 Z. Bagoly , I. Horvath , L. G. Balazs , L. Borgonovo , S. Larsson , A. Meszaros , F. Ryde

The first order behavior of multivariate heavy-tailed random vectors above large radial thresholds is ruled by a limit measure in a regular variation framework. For a high dimensional vector, a reasonable assumption is that the support of…

统计理论 · 数学 2019-06-27 Holger Drees , Anne Sabourin

In this work we investigate the Principal Component Analysis (PCA) sensitivity to the velocity power spectrum in high opacity regimes of the interstellar medium (ISM). For our analysis we use synthetic Position-Position-Velocity (PPV) cubes…

We run very large cosmological N-body hydrodynamical simulations in order to study statistically the baryon fractions in early dark matter halos. We critically examine how differences in the initial conditions affect the gas fraction in the…

宇宙学与河外天体物理 · 物理学 2015-05-19 Smadar Naoz , Naoki Yoshida , Rennan Barkana

We use principal component analysis (PCA) to estimate stellar masses, mean stellar ages, star formation histories (SFHs), dust extinctions and stellar velocity dispersions for ~290,000 galaxies with stellar masses greater than $10^{11}Msun…

Recently Disney et al. (2008) found a striking correlation among the five basic parameters that govern the galactic dynamics: R50, R90, Lr, Md, and MHI . They suggested that this is in conflict with the LCDM model, which predicts the…

星系天体物理 · 物理学 2010-12-30 Yu-Yen Chang , Rikon Chao , Wei-Hao Wang , Pisin Chen

Elemental abundance patterns can provide vital clues to the formation and enrichment history of a stellar population. Here we present an investigation of the Galactic bulge, where we apply principal component abundance analysis (PCAA)---a…

星系天体物理 · 物理学 2012-08-31 Brett H. Andrews , David H. Weinberg , Jennifer A. Johnson , Thomas Bensby , Sofia Feltzing
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