A novel radio imaging method for physical spectral index modelling Article Swipe
E Ceccotti
,
A. R. Offringa
,
L. V. E. Koopmans
,
R. Timmerman
,
S. A. Brackenhoff
,
B. K. Gehlot
,
Florent Mertens
,
S. Munshi
,
V. N. Pandey
,
R. J. van Weeren
,
S. Yatawatta
,
Saleem Zaroubi
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1093/mnras/stad2465
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1093/mnras/stad2465
We present a new method, called ‘forced-spectrum fitting’, for physically based spectral modelling of radio sources during deconvolution. This improves upon current common deconvolution fitting methods, which often produce inaccurate spectra. Our method uses any pre-existing spectral index map to assign spectral indices to each model component cleaned during the multifrequency deconvolution of wsclean, where the pre-determined spectrum is fitted. The component magnitude is evaluated by performing a modified weighted linear least-squares fit. We test this method on a simulated LOFAR high-band antenna (HBA) observation of the 3C 196 QSO and a real LOFAR HBA observation of the 4C+55.16 FRI galaxy. We compare the results from the forced-spectrum fitting with traditional joined-channel deconvolution using polynomial fitting. Because no prior spectral information was available for 4C+55.16, we demonstrate a method for extracting spectral indices in the observed frequency band using ‘clustering’. The models generated by the forced-spectrum fitting are used to improve the calibration of the data sets. The final residuals are comparable to existing multifrequency deconvolution methods, but the output model agrees with the provided spectral index map, embedding correct spectral information. While forced-spectrum fitting does not solve the determination of the spectral information itself, it enables the construction of accurate multifrequency models that can be used for wide-band calibration and subtraction.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/mnras/stad2465
- https://academic.oup.com/mnras/article-pdf/525/3/3946/51911135/stad2465.pdf
- OA Status
- hybrid
- Cited By
- 3
- References
- 97
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386008256
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4386008256Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/mnras/stad2465Digital Object Identifier
- Title
-
A novel radio imaging method for physical spectral index modellingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-08-16Full publication date if available
- Authors
-
E Ceccotti, A. R. Offringa, L. V. E. Koopmans, R. Timmerman, S. A. Brackenhoff, B. K. Gehlot, Florent Mertens, S. Munshi, V. N. Pandey, R. J. van Weeren, S. Yatawatta, Saleem ZaroubiList of authors in order
- Landing page
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https://doi.org/10.1093/mnras/stad2465Publisher landing page
- PDF URL
-
https://academic.oup.com/mnras/article-pdf/525/3/3946/51911135/stad2465.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://academic.oup.com/mnras/article-pdf/525/3/3946/51911135/stad2465.pdfDirect OA link when available
- Concepts
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Deconvolution, Physics, Spectral index, LOFAR, Calibration, Spectral line, Algorithm, Remote sensing, Optics, Astrophysics, Computer science, Radio telescope, Geology, Astronomy, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 1Per-year citation counts (last 5 years)
- References (count)
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97Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.often | 28 |
| abstract_inverted_index.prior | 119 |
| abstract_inverted_index.radio | 15 |
| abstract_inverted_index.sets. | 157 |
| abstract_inverted_index.solve | 188 |
| abstract_inverted_index.using | 114, 139 |
| abstract_inverted_index.where | 55 |
| abstract_inverted_index.which | 27 |
| abstract_inverted_index.agrees | 172 |
| abstract_inverted_index.assign | 41 |
| abstract_inverted_index.called | 6 |
| abstract_inverted_index.common | 23 |
| abstract_inverted_index.during | 17, 49 |
| abstract_inverted_index.linear | 71 |
| abstract_inverted_index.method | 33, 77, 129 |
| abstract_inverted_index.models | 142, 203 |
| abstract_inverted_index.output | 170 |
| abstract_inverted_index.Because | 117 |
| abstract_inverted_index.antenna | 83 |
| abstract_inverted_index.cleaned | 48 |
| abstract_inverted_index.compare | 103 |
| abstract_inverted_index.correct | 180 |
| abstract_inverted_index.current | 22 |
| abstract_inverted_index.enables | 197 |
| abstract_inverted_index.fitted. | 60 |
| abstract_inverted_index.fitting | 25, 109, 147, 185 |
| abstract_inverted_index.galaxy. | 101 |
| abstract_inverted_index.improve | 151 |
| abstract_inverted_index.indices | 43, 133 |
| abstract_inverted_index.itself, | 195 |
| abstract_inverted_index.method, | 5 |
| abstract_inverted_index.present | 2 |
| abstract_inverted_index.produce | 29 |
| abstract_inverted_index.results | 105 |
| abstract_inverted_index.sources | 16 |
| abstract_inverted_index.4C+55.16 | 99 |
| abstract_inverted_index.ABSTRACT | 0 |
| abstract_inverted_index.accurate | 201 |
| abstract_inverted_index.existing | 164 |
| abstract_inverted_index.fitting. | 116 |
| abstract_inverted_index.improves | 20 |
| abstract_inverted_index.methods, | 26, 167 |
| abstract_inverted_index.modified | 69 |
| abstract_inverted_index.observed | 136 |
| abstract_inverted_index.provided | 175 |
| abstract_inverted_index.spectra. | 31 |
| abstract_inverted_index.spectral | 12, 37, 42, 120, 132, 176, 181, 193 |
| abstract_inverted_index.spectrum | 58 |
| abstract_inverted_index.weighted | 70 |
| abstract_inverted_index.wsclean, | 54 |
| abstract_inverted_index.4C+55.16, | 125 |
| abstract_inverted_index.available | 123 |
| abstract_inverted_index.component | 47, 62 |
| abstract_inverted_index.embedding | 179 |
| abstract_inverted_index.evaluated | 65 |
| abstract_inverted_index.frequency | 137 |
| abstract_inverted_index.generated | 143 |
| abstract_inverted_index.high-band | 82 |
| abstract_inverted_index.magnitude | 63 |
| abstract_inverted_index.modelling | 13 |
| abstract_inverted_index.residuals | 160 |
| abstract_inverted_index.simulated | 80 |
| abstract_inverted_index.wide-band | 209 |
| abstract_inverted_index.comparable | 162 |
| abstract_inverted_index.extracting | 131 |
| abstract_inverted_index.inaccurate | 30 |
| abstract_inverted_index.performing | 67 |
| abstract_inverted_index.physically | 10 |
| abstract_inverted_index.polynomial | 115 |
| abstract_inverted_index.calibration | 153, 210 |
| abstract_inverted_index.demonstrate | 127 |
| abstract_inverted_index.fitting’, | 8 |
| abstract_inverted_index.information | 121, 194 |
| abstract_inverted_index.observation | 85, 96 |
| abstract_inverted_index.traditional | 111 |
| abstract_inverted_index.construction | 199 |
| abstract_inverted_index.information. | 182 |
| abstract_inverted_index.pre-existing | 36 |
| abstract_inverted_index.subtraction. | 212 |
| abstract_inverted_index.deconvolution | 24, 52, 113, 166 |
| abstract_inverted_index.determination | 190 |
| abstract_inverted_index.least-squares | 72 |
| abstract_inverted_index.deconvolution. | 18 |
| abstract_inverted_index.joined-channel | 112 |
| abstract_inverted_index.multifrequency | 51, 165, 202 |
| abstract_inverted_index.pre-determined | 57 |
| abstract_inverted_index.forced-spectrum | 108, 146, 184 |
| abstract_inverted_index.‘clustering’. | 140 |
| abstract_inverted_index.‘forced-spectrum | 7 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 12 |
| citation_normalized_percentile.value | 0.73268434 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |