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Improved galactic foreground removal for B-mode detection with clustering methods

Puglisi, Giuseppe and Mihaylov, Gueorgui and Panopoulou, Georgia V. and Poletti, Davide and Errard, Josquin and Puglisi, Paola A. and Vianello, Giacomo (2022) Improved galactic foreground removal for B-mode detection with clustering methods. Monthly Notices of the Royal Astronomical Society, 511 (2). pp. 2052-2074. ISSN 0035-8711. doi:10.1093/mnras/stac069. https://resolver.caltech.edu/CaltechAUTHORS:20220419-307548700

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Abstract

Characterizing the sub-mm Galactic emission has become increasingly critical especially in identifying and removing its polarized contribution from the one emitted by the cosmic microwave background (CMB). In this work, we present a parametric foreground removal performed on to sub-patches identified in the celestial sphere by means of spectral clustering. Our approach takes into account efficiently both the geometrical affinity and the similarity induced by the measurements and the accompanying errors. The optimal partition is then used to parametrically separate the Galactic emission encoding thermal dust and synchrotron from the CMB one applied on two nominal observations of forthcoming experiments from the ground and from the space. Moreover, the clustering is performed on tracers that are different from the data used for component separation, e.g. the spectral index maps of dust and synchrotron. Performing the parametric fit singularly on each of the clustering derived regions results in an overall improvement: both controlling the bias and the uncertainties in the CMB B-mode recovered maps. We finally apply this technique using the map of the number of clouds along the line of sight, N_c⁠, as estimated from H i emission data and perform parametric fitting on to patches derived by clustering on this map. We show that adopting the N_c map as a tracer for the patches related to the thermal dust emission, results in reducing the B-mode residuals post-component separation. The code is made publicly available https://github.com/giuspugl/fgcluster.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1093/mnras/stac069DOIArticle
https://arxiv.org/abs/2109.11562arXivDiscussion Paper
https://github.com/giuspugl/fgclusterRelated ItemCode
https://doi.org/10.7910/DVN/XAMJ4XDOINcloud maps
https://doi.org/10.7910/DVN/WJUEFADOIPySM spectral parameters
https://doi.org/10.7910/DVN/02SCHBDOIGNILC dust parameters
ORCID:
AuthorORCID
Puglisi, Giuseppe0000-0002-0689-4290
Panopoulou, Georgia V.0000-0001-7482-5759
Additional Information:© 2022 The Author(s). Published by Oxford University Press on behalf of 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 2022 January 4. Received 2021 December 31; in original form 2021 October 4. Published: 12 January 2022. This research used resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility located at Lawrence Berkeley National Laboratory, operated under Contract No. DE-AC02-05CH11231. Giuseppe Puglisi acknowledges support by the COSMOS & LiteBIRD networks of the Italian Space Agency. The authors thank Clement Leloup, Carlo Baccigalupi, for having read the paper thoroughly. Giuseppe Puglisi would like to thank: Jonathan Aumont, Mathieu Remazailles, Jens Chluba, Aditya Rotti, Susanna Azzoni, Leo Vacher, Hans Christian Eriksen, Nicoletta Krachmalnicoff for useful comments and discussions. Georgia Panopoulou acknowledges support for this work by NASA through the NASA Hubble Fellowship grant #HST-HF2-51444.001-A awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Incorporated, under NASA contract NAS5-26555. Data Availability: The whole clustering analysis presented here has been collected into a python package, ForeGround Clusters (FGCLUSTERShttps://github.com/giuspugl/fgcluster13). The inputs used throughout this paper together with the outputs obtained with the Clustering technique have been made publicly available online14 and in the Harvard Dataverse: Nc map : https://doi.org/10.7910/DVN/XAMJ4X; PySM spectral parameters: https://doi.org/10.7910/DVN/WJUEFA; GNILC dust parameters: https://doi.org/10.7910/DVN/02SCHB.
Funders:
Funding AgencyGrant Number
Department of Energy (DOE)DE-AC02-05CH11231
Agenzia Spaziale Italiana (ASI)UNSPECIFIED
NASA Hubble FellowshipHST-HF2-51444.001-A
NASANAS5-26555
Subject Keywords:cosmic background radiation –cosmological parameters –diffuse radiation –inflation –ISM –ISM: clouds
Issue or Number:2
DOI:10.1093/mnras/stac069
Record Number:CaltechAUTHORS:20220419-307548700
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20220419-307548700
Official Citation:Giuseppe Puglisi, Gueorgui Mihaylov, Georgia V Panopoulou, Davide Poletti, Josquin Errard, Paola A Puglisi, Giacomo Vianello, Improved galactic foreground removal for B-mode detection with clustering methods, Monthly Notices of the Royal Astronomical Society, Volume 511, Issue 2, April 2022, Pages 2052–2074, https://doi.org/10.1093/mnras/stac069
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:114372
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:19 Apr 2022 18:07
Last Modified:19 Apr 2022 18:07

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