Investigating the contribution of extended radio sources to the Epoch of Reionization power spectrum
Bibliographic record
Abstract
ABSTRACT We investigate the contribution of extended radio sources such as Centaurus A, and Galactic supernova remnants (SNRs) to our ability to detect the statistical 21-cm signal from the Epoch of Reionisation (EoR) with the Murchison Widefield Array (MWA). These sources are typically ignored because they are in highly attenuated parts of the MWA primary beam, however, in aggregate, these sources have apparent flux densities of $10\, \rm {Jy}$ on angular scales we expect to detect the 21-cm signal. We create bespoke multicomponent 2D Gaussian models for Galactic SNRs and for Centaurus A, and simulate the visibilities for two MWA snapshot observations. We grid those visibilities and then Fourier transform them with respect to frequency, averaging them both spherically and cylindrically to produce the 1D and 2D power spectra. We compare the simulated 1D power spectra to the expected 21-$\rm {cm}$ power spectrum. We find that although these extended sources are in highly attenuated parts of the MWA primary beam pattern, collectively they have enough power (∼104−105 $\rm {mK^2}\, {\it h^{-3}} \, \rm {Mpc^{3}}$) on EoR significant modes $(|{\boldsymbol k}| \lesssim 0.1\, h\, \rm {Mpc^{-1}})$ to prohibit detection of the 21-$\rm {cm}$ signal (∼104 $\rm {mK^2}\, {\it h^{-3}} \, \rm {Mpc^{3}}$). We find that $50{-}90{{\ \rm per\ cent}}$ of sources must be removed in order to reduce leakage to a level of $\sim 10{-}20{{\ \rm per\ cent}}$ of the 21-$\rm {cm}$ power spectrum on EoR significant modes. The effects of wide-field extended sources will have implications on the detectability of the 21-$\rm {cm}$ signal for the MWA and with the future Square Kilometre Array (SKA).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".