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DAYENU: a simple filter of smooth foregrounds for intensity mapping power spectra

Ewall-Wice, Aaron and Kern, Nicholas and Dillon, Joshua S. and Liu, Adrian and Parsons, Aaron and Singh, Saurabh and Lanman, Adam and La Plante, Paul and Fagnoni, Nicolas and de Lera Acedo, Eloy and DeBoer, David R. and Nunhokee, Chuneeta and Bull, Philip and Chang, Tzu-Ching and Lazio, T. Joseph W. and Aguirre, James and Weinberg, Sean (2021) DAYENU: a simple filter of smooth foregrounds for intensity mapping power spectra. Monthly Notices of the Royal Astronomical Society, 500 (4). pp. 5195-5213. ISSN 0035-8711. https://resolver.caltech.edu/CaltechAUTHORS:20210128-124522656

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Abstract

We introduce DPSS Approximate lazY filtEriNg of foregroUnds (DAYENU), a linear, spectral filter for H I intensity mapping that achieves the desirable foreground mitigation and error minimization properties of inverse co-variance weighting with minimal modelling of the underlying data. Beyond 21-cm power-spectrum estimation, our filter is suitable for any analysis where high dynamic-range removal of spectrally smooth foregrounds in irregularly (or regularly) sampled data is required, something required by many other intensity mapping techniques. Our filtering matrix is diagonalized by Discrete Prolate Spheroidal Sequences which are an optimal basis to model band-limited foregrounds in 21-cm intensity mapping experiments in the sense that they maximally concentrate power within a finite region of Fourier space. We show that DAYENU enables the access of large-scale line-of-sight modes that are inaccessible to tapered discrete Fourier transform estimators. Since these modes have the largest SNRs,DAYENU significantly increases the sensitivity of 21-cm analyses over tapered Fourier transforms. Slight modifications allow us to use DAYENU as a linear replacement for iterative delay CLEAN ing (DAYENUREST). We refer readers to the Code section at the end of this paper for links to examples and code.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1093/mnras/staa3293DOIArticle
https://arxiv.org/abs/2004.11397arXivDiscussion Paper
https://github.com/HERA-Team/uvtools/blob/master/examples/linear_clean_demo.ipynbRelated ItemInteractive jupyter tutorial
https://github.com/HERA-Team/uvtools/blob/master/uvtools/dspec.pyRelated ItemDayenu’s source code
https://github.com/HERA-Team/aipyRelated ItemAipy python libraries
https://www.astropy.orgRelated ItemAstropy python libraries
https://github.com/jupyter/jupyterRelated ItemJupyter python libraries
ORCID:
AuthorORCID
Ewall-Wice, Aaron0000-0002-0086-7363
Kern, Nicholas0000-0002-8211-1892
Dillon, Joshua S.0000-0003-3336-9958
Liu, Adrian0000-0001-6876-0928
Lanman, Adam0000-0003-2116-3573
La Plante, Paul0000-0002-4693-0102
Fagnoni, Nicolas0000-0001-5300-3166
de Lera Acedo, Eloy0000-0001-8530-6989
DeBoer, David R.0000-0003-3197-2294
Bull, Philip0000-0001-5668-3101
Chang, Tzu-Ching0000-0001-5929-4187
Lazio, T. Joseph W.0000-0002-3873-5497
Aguirre, James0000-0002-4810-666X
Additional Information:© 2020 The Author(s). Published by Oxford University Press on behalf of the Royal Astronomical Society. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model). Accepted 2020 October 20. Received 2020 October 5; in original form 2020 May 5. Published: 23 October 2020. We thank Jacqueline Hewitt, Honggeun Kim, Kevin Bandura, Miguel Morales, Bobby Pascua, Bryna Hazelton, and Ue-Li Pen for helpful discussions. AEW and acknowledges support from the NASA Postdoctoral Program and the Berkeley Center of Cosmological Physics. JSD gratefully acknowledges the support of the NSF AAPF award #1701536. A portion of this work was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. AL acknowledges support from the New Frontiers in Research Fund Exploration grant program, a Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant and a Discovery Launch Supplement, the Sloan Research Fellowship, as well as the Canadian Institute for Advanced Research (CIFAR) Azrieli Global Scholars program. This material is based upon work supported by the National Science Foundation under grants #1636646 and #1836019 and institutional support from the HERA collaboration partners. This research is funded in part by the Gordon and Betty Moore Foundation. HERA is hosted by the South African Radio Astronomy Observatory, which is a facility of the National Research Foundation, an agency of the Department of Science and Innovation. Code: An interactive jupyter tutorial on using dayenu can be found at https://github.com/HERA-Team/uvtools/blob/master/examples/linear_clean_demo.ipynb. dayenu’s source code can be found at https://github.com/HERA-Team/uvtools/blob/master/uvtools/dspec.py This work made use of the numpy (Virtanen et al. 2020), scipy (Virtanen et al. 2020), matplotlib (Hunter 2007), aipy https://github.com/HERA-Team/aipy, and astropy https://www.astropy.org/ and jupyter https://github.com/jupyter/jupyter python libraries along with pyuvdata (Hazelton et al. 2017) and healvis (Lanman & Kern 2019) python packages.
Funders:
Funding AgencyGrant Number
NASA Postdoctoral ProgramUNSPECIFIED
University of California, BerkeleyUNSPECIFIED
NSFAST-1701536
NASA/JPL/CaltechUNSPECIFIED
New Frontiers in Research Fund ExplorationUNSPECIFIED
Natural Sciences and Engineering Research Council of Canada (NSERC)UNSPECIFIED
Alfred P. Sloan FoundationUNSPECIFIED
Canadian Institute for Advanced Research (CIFAR)UNSPECIFIED
NSFAST-1636646
NSFAST-1836019
Gordon and Betty Moore FoundationUNSPECIFIED
National Research Foundation (South Africa)UNSPECIFIED
Subject Keywords:methods: data analysis – techniques: interferometric – techniques: spectroscopic – dark ages, reionization, first stars – large-scale structure of the Universe
Issue or Number:4
Record Number:CaltechAUTHORS:20210128-124522656
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210128-124522656
Official Citation:Aaron Ewall-Wice, Nicholas Kern, Joshua S Dillon, Adrian Liu, Aaron Parsons, Saurabh Singh, Adam Lanman, Paul La Plante, Nicolas Fagnoni, Eloy de Lera Acedo, David R DeBoer, Chuneeta Nunhokee, Philip Bull, Tzu-Ching Chang, T Joseph W Lazio, James Aguirre, Sean Weinberg, DAYENU: a simple filter of smooth foregrounds for intensity mapping power spectra, Monthly Notices of the Royal Astronomical Society, Volume 500, Issue 4, February 2021, Pages 5195–5213, https://doi.org/10.1093/mnras/staa3293
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:107788
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:28 Jan 2021 22:09
Last Modified:28 Jan 2021 22:09

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