CHIPS: The Cosmological HI Power Spectrum Estimator
Cathryn M. Trott, Bart Pindor, Pietro Procopio, Randall B. Wayth, Daniel A. Mitchell, Benjamin McKinley, Steven J. Tingay, N. Barry, A. P. Beardsley, G. Bernard… 展开作者
Cathryn M. Trott, Bart Pindor, Pietro Procopio, Randall B. Wayth, Daniel A. Mitchell, Benjamin McKinley, Steven J. Tingay, N. Barry, A. P. Beardsley, G. Bernardi, Judd D. Bowman, F. Briggs, R. J. Cappallo, P. Carroll, A. de Oliveira-Costa, Joshua S. Dillon, A. Ewall-Wice, L. Feng, L. J. Greenhill, B. J. Hazelton, J. N. Hewitt, N. Hurley-Walker, M. Johnston-Hollitt, Daniel C. Jacobs, D. L. Kaplan, HS Kim, E. Lenc, J. Line, A. Loeb, C. J. Lonsdale, M. F. Morales, E. Morgan, A. R. Neben, Nithyanandan Thyagarajan, D. Oberoi, A. R. Offringa, S. M. Ord, S. Paul, J. C. Pober, T. Prabu, J. Riding, N. Udaya Shankar, Shiv K. Sethi, K. S. Srivani, R. Subrahmanyan, I. S. Sullivan, M. Tegmark, R. L. Webster, A. Williams, C. L. Williams, C. Wu, J. S. B. Wyithe
Detection of the cosmological neutral hydrogen signal from the Epoch of Reionization, and estimation of its basic physical parameters, is the principal scientific aim of many current low-frequency radio telescopes. Here we describe the Cosmological HI Power Spectrum Estimator (CHIPS), an algorithm developed and implemented with data from the Murchison Widefield Array (MWA), to compute the two-dimensional and spherically-averaged power spectrum of brightness temperature fluctuations. The principal motivations for CHIPS are the application of realistic instrumental and foreground models to form the optimal estimator, thereby maximising the likelihood of unbiased signal estimation, and allowing a full covariant understanding of the outputs. CHIPS employs an inverse-covariance weighting of the data through the maximum likelihood estimator, thereby allowing use of the full parameter space for signal estimation ("foreground suppression"). We describe the motivation for the algorithm, implementation, application to real and simulated data, and early outputs. Upon application to a set of 3 hours of data, we set a 2$σ$ upper limit on the EoR dimensionless power at $k=0.05$~h.Mpc$^{-1}$ of $Δ_k^2<7.6\times{10^4}$~mK$^2$ in the redshift range $z=[6.2-6.6]$, consistent with previous estimates.
INAF-Osservatorio Astronomico di Trieste（意大利国家天体物理研究所的里雅斯特天文台）；Universitäts-Sternwarte, Fakultät für Physik, Ludwig-Maximilians-Universität München（慕尼黑大学物理学院大学天文台）；Max-Planck-Institut für Astrophysik（马克斯·普朗克天体物理学研究所）；Department of Astronomy and Astrophysics, The University of Chicago（芝加哥大学天体物理学系）；IFPU - Institute for Fundamental Physics of the Universe（宇宙基础物理研究所）；ICSC - Italian Research Center on High Performance Computing, Big Data and Quantum Computing（意大利高性能计算、大数据和量子计算研究中心）；INFN, Sezione di Trieste（意大利国家核物理研究所的里雅斯特分部）；Intel Technology India Private Ltd（英特尔技术印度私人有限公司）；Dipartimento di Fisica e Astronomia "Augusto Righi", Alma Mater Studiorum Università di Bologna（博洛尼亚大学奥古斯托·里吉物理与天文系）
Unlocking Sensitivity for Visibility-based Estimators of the 21 cm Reionization Power Spectrum
为测量宇宙学21厘米功率谱而设计的无线电干涉仪需要高灵敏度。一些现代低频干涉仪的特点是漂移扫描天线放置在一个规则的网格，以最大限度地提高瞬时相干（冗余）测量的数量。然而，即使是这样的最大冗余阵列，重要的敏感性来自基线之间的部分相干性。目前的基于冗余的功率谱管道，虽然显示出易于控制的系统，缺乏利用这种部分冗余的能力。我们介绍了一种方法，利用部分冗余在这样的功率谱流水线漂移扫描阵列。我们的方法交叉乘以基线对在一个时间滞后和量化的敏感性贡献的每对基线。利用128单元的Donald C. Backer精密阵列探测再电离时代（PAPER-128）和分阶段部署的氢再电离时代阵列，我们说明了我们的方法如何适用于不同的阵列，并预测与配对部分相干基线相关的灵敏度提高。随着天线数量的增加，我们发现部分冗余在解锁即将到来的阵列的全部灵敏度方面越来越重要。
Marginal Post Processing of Bayesian Inference Products with Normalizing Flows and Kernel Density Estimators
Cavendish Laboratory（卡文迪许实验室）；Kavli Institute for Cosmology, Cambridge（剑桥卡弗里宇宙学研究所）；University College London（伦敦大学学院）；University of Sussex（萨塞克斯大学）；McGill University（麦吉尔大学）；University of Cambridge（剑桥大学）；Stockholm University（斯德哥尔摩大学）
Learned harmonic mean estimation of the Bayesian evidence with normalizing flows
Mullard Space Science Laboratory, University College London（穆拉德空间科学实验室，伦敦大学学院）；Centre Universitaire d'Informatique, Université de Genève（日内瓦大学计算机中心，日内瓦大学）；Department of Physics, Royal Holloway, University of London（物理系，伦敦大学皇家霍洛威学院）；Alan Turing Institute（艾伦·图灵研究所）
