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相关论文: Analytical marginalisation over photometric redshi…

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The fully marginalized likelihood, or Bayesian evidence, is of great importance in probabilistic data analysis, because it is involved in calculating the posterior probability of a model or re-weighting a mixture of models conditioned on…

天体物理仪器与方法 · 物理学 2014-01-30 Fengji Hou , Jonathan Goodman , David W. Hogg

Forthcoming large galaxy cluster surveys will yield tight constraints on cosmological models. It has been shown that in an idealized survey, containing > 10,000 clusters, statistical errors on dark energy and other cosmological parameters…

天体物理学 · 物理学 2008-11-26 Joshua D. Younger , Zoltan Haiman , Greg L. Bryan , Sheng Wang

Photometric redshifts of the source galaxies are a key source of systematic uncertainty in the Rubin Observatory Legacy Survey of Space and Time (LSST)'s galaxy clustering and weak lensing analysis, i.e., the $3\times 2$pt analysis. This…

We introduce a dynamic mechanism for the solution of analytically-tractable substructure in probabilistic programs, using conjugate priors and affine transformations to reduce variance in Monte Carlo estimators. For inference with…

机器学习 · 统计学 2018-03-22 Lawrence M. Murray , Daniel Lundén , Jan Kudlicka , David Broman , Thomas B. Schön

Obtaining the set of cosmological parameters consistent with observational data is an important exercise in current cosmological research. It involves finding the global maximum of the likelihood function in the multi-dimensional parameter…

宇宙学与河外天体物理 · 物理学 2012-07-03 Jayanti Prasad , Tarun Souradeep

We present a novel framework for jointly modelling the weak lensing source galaxy redshift distribution and the intrinsic alignment of galaxies via a shared luminosity function (LF). Considering this framework within the context of a Rubin…

宇宙学与河外天体物理 · 物理学 2025-02-13 Nikolina Šarčević , C. Danielle Leonard , Markus M. Rau , LSST Dark Energy Science Collaboration

Bayesian modelling and computational inference by Markov chain Monte Carlo (MCMC) is a principled framework for large-scale uncertainty quantification, though is limited in practice by computational cost when implemented in the simplest…

统计计算 · 统计学 2020-09-21 Colin Fox , Tiangang Cui , Markus Neumayer

Cosmological parameter estimation is traditionally performed in the Bayesian context. By adopting an "agnostic" statistical point of view, we show the interest of confronting the Bayesian results to a frequentist approach based on…

宇宙学与河外天体物理 · 物理学 2016-07-12 S. Henrot-Versillé , O. Perdereau , S. Plaszczynski , B. Rouillé d'Orfeuil , M. Spinelli , M. Tristram

The observing strategy of a galaxy survey influences the degree to which its resulting data can be used to accomplish any science goal. LSST is thus seeking metrics of observing strategies for multiple science cases in order to optimally…

天体物理仪器与方法 · 物理学 2021-04-19 Alex I. Malz , François Lanusse , John Franklin Crenshaw , Melissa L. Graham

The overdetermination of the mathematical problem underlying ptychography is reduced by a host of experimentally more desirable settings. Furthermore, reconstruction of the sample-induced phase shift is typically limited by uncertainty in…

信号处理 · 电气工程与系统科学 2023-07-19 Marcel Schloz , Thomas C. Pekin , Zhen Chen , Wouter Van den Broek , David A. Muller , Christoph T. Koch

[abridged] We present a statistical exploration of the parameter space of the De Lucia and Blaizot version of the Munich semi-analytic model built upon the millennium dark matter simulation. This is achieved by applying a Monte Carlo Markov…

天体物理学 · 物理学 2009-11-13 Bruno Henriques , Peter Thomas , Seb Oliver , Isaac Roseboom

We study how well perturbative forward modeling can constrain cosmological parameters compared to conventional analyses. We exploit the fact that in perturbation theory the field-level posterior can be computed analytically in the limit of…

宇宙学与河外天体物理 · 物理学 2024-01-31 Giovanni Cabass , Marko Simonović , Matias Zaldarriaga

The CMB's B-mode polarization provides a handle on several cosmological parameters most notably the tensor-to-scalar ratio, $r$, and is sensitive to parameters which govern the growth of large scale structure (LSS) and evolution of the…

天体物理学 · 物理学 2009-03-20 N. J. Miller , M. Shimon , B. G. Keating

We present a heuristic strategy for marginal MAP (MMAP) queries in graphical models. The algorithm is based on a reduction of the task to a polynomial number of marginal inference computations. Given an input evidence, the marginals mass…

人工智能 · 计算机科学 2020-02-13 Alessandro Antonucci , Thomas Tiotto

Exploiting the full statistical power of future cosmic shear surveys will necessitate improvements to the accuracy with which the gravitational lensing signal is measured. We present a framework for calibrating shear with image simulations…

We present a new method to estimate redshift distributions and galaxy-dark matter bias parameters using correlation functions in a fully data driven and self-consistent manner. Unlike other machine learning, template, or correlation…

宇宙学与河外天体物理 · 物理学 2019-09-09 Ben Hoyle , Markus Michael Rau

In this paper, we motivate the use of galaxy clustering measurements using photometric redshift information, including a contribution from flux magnification, as a probe of cosmology. We present cosmological forecasts when clustering data…

宇宙学与河外天体物理 · 物理学 2015-06-16 Christopher Duncan , Benjamin Joachimi , Alan Heavens , Catherine Heymans , Hendrik Hildebrandt

We use the mock catalog of galaxies, constructed based on the COSMOS galaxy catalog including information on photometric redshifts (photo-z) and SED types of galaxies, in order to study how to define a galaxy subsample suitable for weak…

宇宙学与河外天体物理 · 物理学 2015-05-18 Atsushi J. Nishizawa , Masahiro Takada , Takashi Hamana , Hisanori Furusawa

The standard cosmological model, with its six independent parameters, successfully describes our observable Universe. One of these parameters, the optical depth to reionization $\tau_\mathrm{reio}$, represents the scatterings that Cosmic…

宇宙学与河外天体物理 · 物理学 2024-08-06 Paulo Montero-Camacho , Yin Li , Miles Cranmer