A general Bayesian framework for foreground modelling and chromaticity correction for global 21 cm experiments
TLDR
A new physics-motivated method of modelling the foregrounds of 21 cm experiments in order to fit the chromatic distortions as part of the foregrounding and it is demonstrated that fitting this model for varying N using a Bayesian nested sampling algorithm allows the 21 cm signal to be reliably detected in data of a relatively smooth conical log spiral antenna.Abstract:
The HI 21cm absorption line is masked by bright foregrounds and systematic distortions that arise due to the chromaticity of the antenna used to make the observation coupling to the spectral inhomogeneity of these foregrounds. We demonstrate that these distortions are sufficient to conceal the 21cm signal when the antenna is not perfectly achromatic and that simple corrections assuming a constant spatial distribution of foreground power are insufficient to overcome them. We then propose a new physics-motivated method of modelling the foregrounds of 21cm experiments in order to fit the chromatic distortions as part of the foregrounds. This is done by generating a simulated sky model across the observing band by dividing the sky into $N$ regions and scaling a base map assuming a distinct uniform spectral index in each region. The resulting sky map can then be convolved with a model of the antenna beam to give a model of foregrounds and chromaticity parameterised by the spectral indices of the $N$ regions. We demonstrate that fitting this model for varying $N$ using a Bayesian nested sampling algorithm and comparing the results using the evidence allows the 21cm signal to be reliably detected in data of a relatively smooth conical log spiral antenna. We also test a much more chromatic conical sinuous antenna and find this model will not produce a reliable signal detection, but in a manner that is easily distinguishable from a true detection.read more
Citations
More filters
The American statistician
TL;DR: This chapter discusses Statistical Training and Curricular Revision, which aims to provide a history of the discipline and some of the techniques used to train teachers.
Journal ArticleDOI
Nested sampling for physical scientists
Gregory Ashton,Noam Bernstein,Johannes Buchner,Xi Chen,Gabor Csanyi,Andrew Fowlie,Farhan Feroz,Matthew Griffiths,Will Handley,Michael Habeck,Edward Higson,Michael P. Hobson,Anthony Lasenby,David Parkinson,Lívia B. Pártay,Matthew Pitkin,Doris Schneider,Joshua S. Speagle,Leah F. South,John Veitch,Philipp Wacker,David J. Wales,David Paul Yallup +22 more
TL;DR: In this paper , the authors review Skilling's nested sampling algorithm for Bayesian inference and more broadly multi-dimensional integration and make recommendations for best practice when using NS and by summarizing potential limitations and optimizations of NS.
Journal ArticleDOI
MAXSMOOTH: rapid maximally smooth function fitting with applications in Global 21-cm cosmology
H T J Bevins,Will Handley,Anastasia Fialkov,E. de Lera Acedo,Lincoln J. Greenhill,Danny C. Price,Danny C. Price +6 more
TL;DR: The efficiency and reliability of maxsmooth are demonstrated by comparison to commonly used fitting routines, and it is shown that by using quadratic programming the fitting time can be reduced by approximately two orders of magnitude.
Journal ArticleDOI
The REACH radiometer for detecting the 21-cm hydrogen signal from redshift z ≈ 7.5–28
Eloy de Lera Acedo,Dirk I. L. de Villiers,Nima Razavi-Ghods,Will Handley,Anastasia Fialkov,Alessio Magro,D. A. Anstey,H.T.J. Bevins,Riccardo Chiello,J. Cumner,Alec Josaitis,I. L. V. Roque,Peter Sims,Kilian H. Scheutwinkel,Paul Alexander,Gianni Bernardi,Steven Carey,Jean Cavillot,W. Croukamp,John Ely,Thomas Gessey-Jones,Quentin Gueuning,R. Hills,Gauri V. Kulkarni,Roberto Maiolino,P. Daniel Meerburg,Shikhar Mittal,Jonathan K. Pritchard,Ewald Puchwein,A. Saxena,E Shen,Oleg Smirnov,M. Spinelli,Kristian Zarb-Adami +33 more
TL;DR: The Radio Experiment for the Analysis of Cosmic Hydrogen (REACH) is a sky-averaged 21 cm experiment aiming at improving the current observations by tackling the issues faced by current instruments related to residual systematic signals in the data as mentioned in this paper .
Journal ArticleDOI
Quantifying ionospheric effects on global 21-cm observations
TL;DR: In this paper, the two major layers of Earth's ionosphere, the F-layer and D-layer, were modelled by a simplified spatial model with temporal variance to study the chromatic ionospheric effects on global 21-cm observations.
References
More filters
Journal ArticleDOI
emcee: The MCMC Hammer
TL;DR: The emcee algorithm as mentioned in this paper is a Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by Goodman & Weare (2010).
Journal ArticleDOI
emcee: The MCMC Hammer
TL;DR: This document introduces a stable, well tested Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by Goodman & Weare (2010).
Journal ArticleDOI
Understanding the Metropolis-Hastings Algorithm
Siddhartha Chib,Edward Greenberg +1 more
TL;DR: A detailed, introductory exposition of the Metropolis-Hastings algorithm, a powerful Markov chain method to simulate multivariate distributions, and a simple, intuitive derivation of this method is given along with guidance on implementation.
Journal ArticleDOI
MultiNest: an efficient and robust Bayesian inference tool for cosmology and particle physics
TL;DR: The developments presented here lead to further improvements in sampling efficiency and robustness, as compared to the original algorit hm presented in Feroz & Hobson (2008), which itself significantly outperformed existi ng MCMC techniques in a wide range of astrophysical inference problems.
Journal ArticleDOI
LOFAR: The LOw-Frequency ARray
M. P. van Haarlem,Michael W. Wise,Michael W. Wise,A. W. Gunst,George Heald,John McKean,Jason W. T. Hessels,Jason W. T. Hessels,de Antonius Bruyn,de Antonius Bruyn,R. Nijboer,John D. Swinbank,Richard Fallows,Michiel A. Brentjens,A. Nelles,Rainer Beck,Heino Falcke,Heino Falcke,Rob Fender,Jörg R. Hörandel,Luitje Koopmans,G. Mann,George K. Miley,Huub Röttgering,B. W. Stappers,Ralph A. M. J. Wijers,Saleem Zaroubi,M. van den Akker,A. Alexov,J. M. Anderson,Kenneth C. Anderson,A. van Ardenne,A. van Ardenne,Michel Arts,Ashish Asgekar,I. M. Avruch,I. M. Avruch,F. Batejat,L. Bähren,Martin Bell,Michael R. Bell,I. van Bemmel,P. Bennema,Mark J. Bentum,Gianni Bernardi,Philip Best,Laura Birzan,Annalisa Bonafede,Albert-Jan Boonstra,Robert Braun,Joel N. Bregman,F. Breitling,R. H. van de Brink,J. W. Broderick,P. C. Broekema,W. N. Brouw,W. N. Brouw,Marcus Brüggen,Harvey Butcher,Harvey Butcher,W. A. van Cappellen,B. Ciardi,T. Coenen,John Conway,A. H. W. M. Coolen,Arthur Corstanje,S. Damstra,O. Davies,Adam Deller,R. J. Dettmar,G. van Diepen,K. Dijkstra,P. Donker,A. Doorduin,J. Dromer,M. Drost,A. van Duin,Jochen Eislöffel,J. van Enst,Chiara Ferrari,Wilfred Frieswijk,H. Gankema,M. A. Garrett,M. A. Garrett,F. de Gasperin,M. Gerbers,E. de Geus,J.-M. Grießmeier,J.-M. Grießmeier,T. Grit,P. Gruppen,J. P. Hamaker,Tim Hassall,Matthias Hoeft,H. A. Holties,A. Horneffer,A. Horneffer,A. J. van der Horst,A. van Houwelingen,A. Huijgen,Marco Iacobelli,Hubertus Intema,Hubertus Intema,N. J. Jackson,Vibor Jelić,A. de Jong,E. Juette,D. Kant,Aris Karastergiou,A. Koers,H. Kollen,V. I. Kondratiev,E. Kooistra,Y. Koopman,A. Koster,M. Kuniyoshi,Michael Kramer,Michael Kramer,G. Kuper,P. Lambropoulos,Casey J. Law,Casey J. Law,J. van Leeuwen,J. van Leeuwen,J. Lemaitre,M. Loose,P. Maat,Giulia Macario,Sera Markoff,J. Masters,J. Masters,Rebecca McFadden,D. McKay-Bukowski,H. Meijering,H. Meulman,M. Mevius,Enno Middelberg,R. Millenaar,James Miller-Jones,James Miller-Jones,R. N. Mohan,J. D. Mol,J. Morawietz,Raffaella Morganti,Raffaella Morganti,D. D. Mulcahy,E. Mulder,H. Munk,Lambert J.M. Nieuwenhuis,R. van Nieuwpoort,J. E. Noordam,M. J. Norden,A. Noutsos,A. R. Offringa,Hans Olofsson,Amitesh Omar,Emanuela Orrú,Emanuela Orrú,R. Overeem,H. Paas,M. Pandey-Pommier,Vishambhar Pandey,Roberto Pizzo,A. G. Polatidis,D. A. Rafferty,Steve Rawlings,W. Reich,J.-P. de Reijer,J. Reitsma,G.A. Renting,P. Riemers,E. Rol,J. W. Romein,J. Roosjen,M. Ruiter,Anna M. M. Scaife,K. van der Schaaf,Bart Scheers,Bart Scheers,Pim Schellart,A. P. Schoenmakers,G. Schoonderbeek,M. Serylak,M. Serylak,Aleksandar Shulevski,J. Sluman,Oleg Smirnov,Charlotte Sobey,H. Spreeuw,Matthias Steinmetz,C. G. M. Sterks,H.-J. Stiepel,K. Stuurwold,Michel Tagger,Y. Tang,C. Tasse,I. Thomas,Satyendra Thoudam,M. C. Toribio,B. van der Tol,O. Usov,M. van Veelen,A.-J. van der Veen,S. ter Veen,Joris P. W. Verbiest,R. Vermeulen,N. J. Vermaas,C. Vocks,C. Vogt,M. de Vos,E. van der Wal,R. J. van Weeren,R. J. van Weeren,H. Weggemans,Patrick Weltevrede,Simon D. M. White,Stefan J. Wijnholds,T. Wilhelmsson,Olaf Wucknitz,Sarod Yatawatta,Philippe Zarka,Anton Zensus,J. E. van Zwieten +222 more
TL;DR: In dit artikel zullen the authors LOFAR beschrijven: van de astronomische mogelijkheden met de nieuwe telescoop tot aan een nadere technische beshrijving of het instrument.