Starobinsky in stereo: SKA-CMB synergy in SBI
Modern machine learning techniques can unlock the vast cosmological information encoded in forthcoming Square Kilometre Array (SKA) observations. We show that tomographic 21 cm data from the reionisation era can yield stringent tests of inflationary models — here illustrated with Starobinsky R + R 2...
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| Hauptverfasser: | , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
27 February 2026
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Journal of cosmology and astroparticle physics
Year: 2026, Jahrgang: 2026, Heft: 02, Pages: 1-28 |
| ISSN: | 1475-7516 |
| DOI: | 10.1088/1475-7516/2026/02/086 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1088/1475-7516/2026/02/086
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| Verfasserangaben: | Benedikt Schosser, Caroline Heneka and Björn Malte Schäfer |
| Zusammenfassung: | Modern machine learning techniques can unlock the vast cosmological information encoded in forthcoming Square Kilometre Array (SKA) observations. We show that tomographic 21 cm data from the reionisation era can yield stringent tests of inflationary models — here illustrated with Starobinsky R + R 2 inflation. Using a simulation-based inference (SBI) framework, we compare neural summaries (convolutional network and vision transformer) with a traditional power spectrum summary and perform a fully joint SBI analysis combining 21 cm data with data of the cosmic microwave background (CMB). Forecasts based on realistic mock observations indicate that SKA alone will achieve constraints competitive with Planck, and that the combined SKA + CMB dataset will tighten bounds on both inflationary and ΛCDM parameters considerably while improving precision on key astrophysical quantities. |
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| Beschreibung: | Gesehen am 30.04.2026 |
| Beschreibung: | Online Resource |
| ISSN: | 1475-7516 |
| DOI: | 10.1088/1475-7516/2026/02/086 |