中文
相关论文

相关论文: Lagrangian approach to super-sample effects on bia…

200 篇论文

The B-(curl-)mode of the correlation of galaxy ellipticities (shear) can be used to detect a stochastic gravitational wave background, such as that predicted by inflation. In this paper, we derive the tensor mode contributions to shear from…

宇宙学与河外天体物理 · 物理学 2013-11-27 Fabian Schmidt , Donghui Jeong

We present a detection of the intrinsic galaxy alignments in the CAMELS suite of hydrodynamic simulations. We find that the alignment amplitude depends significantly on cosmological and supernova feedback parameters - specifically…

宇宙学与河外天体物理 · 物理学 2026-02-03 Daniel Bilsborrow , Niall Jeffrey

The magnification effects of clustered matter produce variations in the image sizes and number density of galaxies across the sky. This paper advocates the use of these effects in wide field surveys to map large-scale structure and the…

天体物理学 · 物理学 2009-11-07 Bhuvnesh Jain

We develop a new approach to study the nonlinear evolution in the large-scale structure of the Universe both in real space and in redshift space, extending the standard perturbation theory of gravitational instability. Infinite series of…

天体物理学 · 物理学 2008-11-26 Takahiko Matsubara

Photometric large scale structure (LSS) surveys probe the largest volumes in the Universe, but are inevitably limited by systematic uncertainties. Imperfect photometric calibration leads to biases in our measurements of the density fields…

We use weak gravitational lensing to measure mean mass profiles around Locally Brightest Galaxies (LBGs). These are selected from the SDSS/DR7 spectroscopic and photometric catalogues to be brighter than any neighbour projected within 1.0…

宇宙学与河外天体物理 · 物理学 2016-01-15 Wenting Wang , Simon White , Rachel Mandelbaum , Bruno Henriques , Michael E. Anderson , Jiaxin Han

We compute the dipole of the galaxy correlation function at 1-loop in perturbation theory by including all the relevant relativistic contributions. This provides a description and understanding of what the dipole truly measures, in…

宇宙学与河外天体物理 · 物理学 2019-05-06 Enea Di Dio , Uros Seljak

We consider the power spectrum of a biased tracer observed in a finite volume in the presence of a large-scale overdensity and tidal fields. Expanding both the observed power spectrum and the source fields (linear power spectrum, scalar…

宇宙学与河外天体物理 · 物理学 2018-08-08 Chi-Ting Chiang , Anže Slosar

We discuss the constraints on the supersymmetric parameter space from the decay mode b -> s \gamma for large values of tan\beta. We improve the theoretical prediction for the decay rate by summing very large radiative corrections to all…

高能物理 - 唯象学 · 物理学 2008-11-26 M. Carena , D. Garcia , U. Nierste , C. E. M. Wagner

These Lecture Notes are devoted to an introductory description of some of the most widely applied statistical methods for the analysis of the Large-Scale Structure (LSS) of the Universe. Rather than providing technical details about the…

天体物理学 · 物理学 2007-05-23 Stefano Borgani

The power spectrum of density fluctuations measured from galaxy redshift surveys provides important constraints on models for the formation of large-scale structure. I present new results for a redshift sample of 15,000 galaxies, and review…

天体物理学 · 物理学 2007-05-23 Michael S. Vogeley

Examining the nature of the relative clustering of different galaxy types can help tell us how galaxies formed. To measure this relative clustering, I perform a joint counts-in-cells analysis of galaxies of different spectral types in the…

天体物理学 · 物理学 2009-10-31 Michael Blanton

We derive and implement a full Bayesian large scale structure inference method aiming at precision recovery of the cosmological power spectrum from galaxy redshift surveys. Our approach improves over previous Bayesian methods by performing…

宇宙学与河外天体物理 · 物理学 2015-06-16 Jens Jasche , Benjamin D. Wandelt

We present a deep machine learning (ML) approach to constraining cosmological parameters with multi-wavelength observations of galaxy clusters. The ML approach has two components: an encoder that builds a compressed representation of each…

天体物理仪器与方法 · 物理学 2022-02-16 Michelle Ntampaka , Alexey Vikhlinin

We develop a practical methodology to remove modes from a galaxy survey power spectrum that are associated with systematic errors. We apply this to the BOSS CMASS sample, to see if it removes the excess power previously observed beyond the…

宇宙学与河外天体物理 · 物理学 2022-11-25 B. Bahr-Kalus , W. J. Percival , D. J. Bacon , E. -M. Mueller , L. Samushia , L. Verde , A. J. Ross , J. L. Bernal

We consider the growth of primordial dark matter halos seeded by three crossed initial sine waves of various amplitudes. Using a Lagrangian treatment of cosmological gravitational dynamics, we examine the convergence properties of a…

宇宙学与河外天体物理 · 物理学 2018-12-18 Shohei Saga , Atsushi Taruya , Stéphane Colombi

Gravitational lensing is a powerful tool for constraining substructure in the mass distribution of galaxies, be it from the presence of dark matter sub-halos or due to physical mechanisms affecting the baryons throughout galaxy evolution.…

星系天体物理 · 物理学 2020-10-28 Georgios Vernardos , Grigorios Tsagkatakis , Yannis Pantazis

We use galaxy groups selected from the Sloan Digital Sky Survey (SDSS) together with mass models for individual groups to study the galaxy-galaxy lensing signals expected from galaxies of different luminosities and morphological types. We…

天体物理学 · 物理学 2009-04-21 Ran Li , H. J. Mo , Zuhui Fan , Marcello Cacciato , Frank C. van den Bosch , Xiaohu Yang , Surhud More

Primordial, non-Gaussian perturbations can generate scale-dependent bias in the galaxy distribution. This in turn will modify correlations between galaxy positions and peculiar velocities at late times, since peculiar velocities reflect the…

宇宙学与河外天体物理 · 物理学 2015-06-16 Yin-Zhe Ma , James E. Taylor , Douglas Scott

Accurately estimating the informativeness of individual samples in a dataset is an important objective in deep learning, as it can guide sample selection, which can improve model efficiency and accuracy by removing redundant or potentially…

机器学习 · 计算机科学 2025-05-22 Johannes Kaiser , Kristian Schwethelm , Daniel Rueckert , Georgios Kaissis