<i>Planck</i>2013 results. VIII. HFI photometric calibration and mapmaking
Bibliographic record
Abstract
This paper describes the methods used to produce photometrically calibrated maps from the Planck High Frequency Instrument (HFI) cleaned, time-ordered information. HFI observes the sky over a broad range of frequencies, from 100 to 857 GHz. To obtain the best calibration accuracy over such a large range, two different photometric calibration schemes have to be used. The 545 and 857 GHz data are calibrated by comparing flux-density measurements of Uranus and Neptune with models of their atmospheric emission. The lower frequencies (below 353 GHz) are calibrated using the solar dipole. A component of this anisotropy is time-variable, owing to the orbital motion of the satellite in the solar system. Photometric calibration is thus tightly linked to mapmaking, which also addresses low-frequency noise removal. By comparing observations taken more than one year apart in the same configuration, we have identified apparent gain variations with time. These variations are induced by non-linearities in the read-out electronics chain. We have developed an effective correction to limit their effect on calibration. We present several methods to estimate the precision of the photometric calibration. We distinguish relative uncertainties (between detectors, or between frequencies) and absolute uncertainties. Absolute uncertainties lie in the range from 0.54% to 10% from 100 to 857 GHz. We describe the pipeline used to produce the maps from the HFI timelines, based on the photometric calibration parameters, and the scheme used to set the zero level of the maps a posteriori. We also discuss the cross-calibration between HFI and the SPIRE instrument on board Herschel. Finally we summarize the basic characteristics of the set of HFI maps included in the 2013 Planck data release.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 0.039 |
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".