Iterative map-making with two-level preconditioning for polarized Cosmic Microwave Background data sets Article Swipe
Giuseppe Puglisi
,
C. Baccigalupi
,
R. Stompor
,
Giulio Fabbian
,
Luca Heltai
,
D. Poletti
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1801.08937
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1801.08937
An estimation of the sky signal from streams of Time Ordered Data (TOD) acquired by Cosmic Microwave Background (\cmb) experiments is one of the most important steps in the context of \cmb data analysis referred to as the map-making problem. The continuously growing \cmb data sets render the \cmb map-making problem more challenging in terms of computational cost and memory in particular in the context of ground based experiments. In this context, we study a novel class of the Preconditioned Conjugate Gradient (PCG) solvers which invoke two-level preconditioners. We compare them against PCG solvers commonly used in the map-making context considering their precision and time-to-solution. We compare these new methods on realistic, simulated data sets reflecting the characteristics of current and forthcoming \cmb ground-based experiment. We develop an embarrassingly parallel implementation of the approach where each processor performs a sequential map-making for a subset of the TOD. We find that considering the map level residuals the new class of solvers permits achieving tolerance of up to 3 orders of magnitude better than the standard approach, where the residual level often saturates before convergence is reached. This corresponds to an important improvement in the precision of recovered power spectra in particular on the largest angular scales. The new method also typically requires fewer iterations to reach a required precision and thus shorter runtimes for a single map-making solution. However, the construction of an appropriate two-level preconditioner can be as costly as a single standard map-making run. Nevertheless, if the same problem needs to be solved multiple times, e.g., as in Monte Carlo simulations, this cost has to be incurred only once, and the method should be competitive not only as far as its precision but also its performance is concerned.
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https://openalex.org/W2963976353Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.1801.08937Digital Object Identifier
- Title
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Iterative map-making with two-level preconditioning for polarized Cosmic Microwave Background data setsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2018Year of publication
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2018-01-26Full publication date if available
- Authors
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Giuseppe Puglisi, C. Baccigalupi, R. Stompor, Giulio Fabbian, Luca Heltai, D. PolettiList of authors in order
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://doi.org/10.48550/arxiv.1801.08937Direct OA link when available
- Concepts
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Physics, Cosmic microwave background, Astrophysics, Microwave, Astronomy, COSMIC cancer database, Optics, Anisotropy, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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19Other works algorithmically related by OpenAlex
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| abstract_inverted_index.commonly | 94 |
| abstract_inverted_index.context, | 71 |
| abstract_inverted_index.incurred | 267 |
| abstract_inverted_index.multiple | 254 |
| abstract_inverted_index.parallel | 129 |
| abstract_inverted_index.performs | 137 |
| abstract_inverted_index.problem. | 39 |
| abstract_inverted_index.reached. | 184 |
| abstract_inverted_index.referred | 34 |
| abstract_inverted_index.required | 216 |
| abstract_inverted_index.requires | 210 |
| abstract_inverted_index.residual | 177 |
| abstract_inverted_index.runtimes | 221 |
| abstract_inverted_index.standard | 173, 242 |
| abstract_inverted_index.Conjugate | 80 |
| abstract_inverted_index.Microwave | 16 |
| abstract_inverted_index.achieving | 161 |
| abstract_inverted_index.approach, | 174 |
| abstract_inverted_index.important | 25, 189 |
| abstract_inverted_index.magnitude | 169 |
| abstract_inverted_index.precision | 102, 193, 217, 282 |
| abstract_inverted_index.processor | 136 |
| abstract_inverted_index.recovered | 195 |
| abstract_inverted_index.residuals | 154 |
| abstract_inverted_index.saturates | 180 |
| abstract_inverted_index.simulated | 112 |
| abstract_inverted_index.solution. | 226 |
| abstract_inverted_index.tolerance | 162 |
| abstract_inverted_index.two-level | 86, 233 |
| abstract_inverted_index.typically | 209 |
| abstract_inverted_index.Background | 17 |
| abstract_inverted_index.concerned. | 288 |
| abstract_inverted_index.estimation | 1 |
| abstract_inverted_index.iterations | 212 |
| abstract_inverted_index.map-making | 38, 49, 98, 140, 225, 243 |
| abstract_inverted_index.particular | 61, 199 |
| abstract_inverted_index.realistic, | 111 |
| abstract_inverted_index.reflecting | 115 |
| abstract_inverted_index.sequential | 139 |
| abstract_inverted_index.appropriate | 232 |
| abstract_inverted_index.challenging | 52 |
| abstract_inverted_index.competitive | 275 |
| abstract_inverted_index.considering | 100, 150 |
| abstract_inverted_index.convergence | 182 |
| abstract_inverted_index.corresponds | 186 |
| abstract_inverted_index.experiment. | 124 |
| abstract_inverted_index.experiments | 19 |
| abstract_inverted_index.forthcoming | 121 |
| abstract_inverted_index.improvement | 190 |
| abstract_inverted_index.performance | 286 |
| abstract_inverted_index.construction | 229 |
| abstract_inverted_index.continuously | 41 |
| abstract_inverted_index.experiments. | 68 |
| abstract_inverted_index.ground-based | 123 |
| abstract_inverted_index.simulations, | 261 |
| abstract_inverted_index.Nevertheless, | 245 |
| abstract_inverted_index.computational | 56 |
| abstract_inverted_index.Preconditioned | 79 |
| abstract_inverted_index.embarrassingly | 128 |
| abstract_inverted_index.implementation | 130 |
| abstract_inverted_index.preconditioner | 234 |
| abstract_inverted_index.characteristics | 117 |
| abstract_inverted_index.preconditioners. | 87 |
| abstract_inverted_index.time-to-solution. | 104 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/13 |
| sustainable_development_goals[0].score | 0.800000011920929 |
| sustainable_development_goals[0].display_name | Climate action |
| citation_normalized_percentile.value | 0.18651151 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |