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We introduce the EMC algorithm for reconstructing a particle's 3D diffraction intensity from very many photon shot-noise limited 2D measurements, when the particle orientation in each measurement is unknown. The algorithm combines a…

数据分析、统计与概率 · 物理学 2010-03-04 Duane Ne-Te Loh , Veit Elser

Line-of-sight distortions of the cosmic microwave background (CMB), including gravitational lensing, cosmic birefringence, and patchy screening, encode crucial cosmological information. While quadratic estimators (QE) have been excellent…

宇宙学与河外天体物理 · 物理学 2025-03-06 Omar Darwish

We present a new algorithm, called Multiresolution Regularized Expectation Maximization (MREM), for the reconstruction of gamma-ray intensity maps from COMPTEL data. The algorithm is based on the iterative Richardson-Lucy scheme to which we…

In this paper we present a power spectrum formalism that combines the full three-dimensional information from the galaxy ellipticity field, with information from the cosmic microwave background (CMB). We include in this approach galaxy…

宇宙学与河外天体物理 · 物理学 2015-06-22 T. D. Kitching , A. F. Heavens , S. Das

Estimation of the angular power spectrum is one of the important steps in Cosmic Microwave Background (CMB) data analysis. Here, we present a nonparametric estimate of the temperature angular power spectrum for the Planck 2013 CMB data. The…

宇宙学与河外天体物理 · 物理学 2015-06-15 Amir Aghamousa , Arman Shafieloo , Mihir Arjunwadkar , Tarun Souradeep

We study reconstruction of the lensing potential power spectrum from CMB temperature data, with an eye to the Planck experiment. We work with the optimal quadratic estimator of Okamoto and Hu, which we characterize thoroughly in application…

宇宙学与河外天体物理 · 物理学 2011-02-23 Duncan Hanson , Anthony Challinor , George Efstathiou , Pawel Bielewicz

The aim of this project is to recover the CMB anisotropies maps in temperature and polarized intensity by means of a deep convolutional neural network (CNN) which, after appropiate training, can remove the foregrounds from Planck and…

宇宙学与河外天体物理 · 物理学 2024-05-29 A. Quintana-Estellés , B. Ruiz-Granados , P. Ruiz-Lapuente

The expectation-maximization (EM) algorithm is an iterative method for finding maximum likelihood estimates when data are incomplete or are treated as being incomplete. The EM algorithm and its variants are commonly used for parameter…

统计计算 · 统计学 2013-06-26 Ryan P. Browne , Sanjeena Subedi , Paul McNicholas

We develop a method of reconstructing the lensing field from lensed CMB temperature and polarization maps in real space as an alternative to the harmonic space estimators currently in use by extending an existing real space lensing…

宇宙学与河外天体物理 · 物理学 2018-02-07 Heather Prince , Kavilan Moodley , Jethro Ridl , Martin Bucher

Power spectrum estimation and evaluation of associated errors in the presence of incomplete sky coverage; non-homogeneous, correlated instrumental noise; and foreground emission is a problem of central importance for the extraction of…

天体物理学 · 物理学 2016-08-30 J. Jewell , S. Levin , C. H. Anderson

The Planck mission, designed for making measurements of the cosmic microwave background (CMB) radiation with unprecedented accuracy and angular resolution, is expected to release its first data set in the near future. For the first time in…

宇宙学与河外天体物理 · 物理学 2015-01-27 Amir Aghamousa , Mihir Arjunwadkar , Tarun Souradeep

CMB lensing from current and upcoming wide-field CMB experiments such as AdvACT, SPT-3G and Simons Observatory relies heavily on temperature (vs. polarization). In this regime, foreground contamination to the temperature map produces…

宇宙学与河外天体物理 · 物理学 2019-05-20 Emmanuel Schaan , Simone Ferraro

The expectation--maximization (EM) algorithm combines global monotonicity, local linear convergence, and strong practical robustness, but these features are usually analyzed separately. Global descent is nonlinear, whereas local convergence…

机器学习 · 统计学 2026-05-11 Qiao Wang

We propose a straightforward and model independent methodology for characterizing the sensitivity of CMB and other experiments to wiggles, irregularities, and features in the primordial power spectrum. Assuming that the primordial…

宇宙学与河外天体物理 · 物理学 2015-06-11 Christopher Gauthier , Martin Bucher

In this paper we study the expectation maximization (EM) technique for one-bit MIMO-OFDM detection (OMOD). Arising from the recent interest in massive MIMO with one-bit analog-to-digital converters, OMOD is a massive-scale problem. EM is an…

信号处理 · 电气工程与系统科学 2024-01-30 Mingjie Shao , Wing-Kin Ma , Junbin Liu , Zihao Huang

The accurate estimation of the noise covariance matrix (NCM) in a dynamic system is critical for state estimation and control, as it has a major influence in their optimality. Although a large number of NCM estimation methods have been…

系统与控制 · 电气工程与系统科学 2023-08-16 Ajith Anil Meera , Pablo Lanillos

The Kaplan--Meier (KM) estimator, which provides a nonparametric estimate of a survival function for time-to-event data, has wide application in clinical studies, engineering, economics and other fields. The theoretical properties of the KM…

统计方法学 · 统计学 2020-11-23 Jiaqi Gu , Yiwei Fan , Guosheng Yin

Residual error in calibration coefficients corresponding to observed CMB maps is an important issue while estimating a pure CMB signal. A component separation method, if these errors in the input foreground contaminated CMB maps are not…

宇宙学与河外天体物理 · 物理学 2020-10-21 Vipin Sudevan , Rajib Saha

The observation of the polarised emission from the Cosmic Microwave Background (CMB) from future ground-based and satellite-borne experiments holds the promise of indirectly detecting the elusive signal from primordial tensor fluctuations…

宇宙学与河外天体物理 · 物理学 2023-03-22 Susanna Azzoni , David Alonso , Maximilian H. Abitbol , Josquin Errard , Nicoletta Krachmalnicoff

Future experiments will produce high-resolution temperature maps of the cosmic microwave background (CMB) and are expected to reveal the signature of gravitational lensing by intervening large-scale structures. We construct all-sky…

天体物理学 · 物理学 2009-11-07 Christopher M. Hirata , Uros Seljak
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