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Planck 2018 results. III. High Frequency Instrument data processing and frequency maps

Planck Collaboration, N. Aghanim, Y. Akrami, M. Ashdown, J. Aumont, C. Baccigalupi, M. Ballardini, A. J. Banday, R. B. Barreiro, N. Bartolo, S. Basak, K. Benabed, J. -P. Bernard, M. Bersanelli, P. Bielewicz, J. R. Bond, J. Borrill, F. R. Bouchet, F. Boulanger, M. Bucher, C. Burigana, E. Calabrese, J. -F. Cardoso, J. Carron, A. Challinor, H. C. Chiang, L. P. L. Colombo, C. Combet, F. Couchot, B. P. Crill, F. Cuttaia, P. de Bernardis, A. de Rosa, G. de Zotti, J. Delabrouille, J. -M. Delouis, E. Di Valentino, J. M. Diego, O. Doré, M. Douspis, A. Ducout, X. Dupac, G. Efstathiou, F. Elsner, T. A. Enßlin, H. K. Eriksen, E. Falgarone, Y. Fantaye, F. Finelli, M. Frailis, A. A. Fraisse, E. Franceschi, A. Frolov, S. Galeotta, S. Galli, K. Ganga, R. T. Génova-Santos, M. Gerbino, T. Ghosh, J. González-Nuevo, K. M. Górski, S. Gratton, A. Gruppuso, J. E. Gudmundsson, W. Handley, F. K. Hansen, S. Henrot-Versillé, D. Herranz, E. Hivon, Z. Huang, A. H. Jaffe, W. C. Jones, A. Karakci, E. Keihänen, R. Keskitalo, K. Kiiveri, J. Kim, T. S. Kisner, N. Krachmalnicoff, M. Kunz, H. Kurki-Suonio, G. Lagache, J. -M. Lamarre, A. Lasenby, M. Lattanzi, C. R. Lawrence, F. Levrier, M. Liguori, P. B. Lilje, V. Lindholm, M. López-Caniego, Y. -Z. Ma, J. F. Macías-Pérez, G. Maggio, D. Maino, N. Mandolesi, A. Mangilli, P. G. Martin, E. Martínez-González, S. Matarrese, N. Mauri, J. D. McEwen, A. Melchiorri, A. Mennella, M. Migliaccio, M. -A. Miville-Deschênes, D. Molinari, A. Moneti, L. Montier, G. Morgante, A. Moss, S. Mottet, P. Natoli, L. Pagano, D. Paoletti, B. Partridge, G. Patanchon, L. Patrizii, O. Perdereau, F. Perrotta, V. Pettorino, F. Piacentini, J. -L. Puget, J. P. Rachen, M. Reinecke, M. Remazeilles, A. Renzi, G. Rocha, G. Roudier, L. Salvati, M. Sandri, M. Savelainen, D. Scott, C. Sirignano, G. Sirri, L. D. Spencer, R. Sunyaev, A. -S. Suur-Uski, J. A. Tauber, D. Tavagnacco, M. Tenti, L. Toffolatti, M. Tomasi, M. Tristram, T. Trombetti, J. Valiviita, F. Vansyngel, B. Van Tent, L. Vibert, P. Vielva, F. Villa, N. Vittorio, B. D. Wandelt, I. K. Wehus, A. Zonca

TL;DR

The paper tackles the challenge of extracting precise CMB polarization and reionization information from Planck HFI data by advancing mapmaking through SRoll, an extended destriper that minimizes intensity-to-polarization leakage from calibration and bandpass mismatches. It leverages extensive end-to-end simulations (FFP10) to quantify residual systematics and uses high-fidelity calibrations based on the orbital and Solar dipoles, achieving sub-per-mil to per-mil-level inter-detector and inter-band consistency in the CMB bands. The combination of sky-based parameter recovery, improved foreground templates, and detailed simulation-driven likelihoods enables a robust first detection of the reionization optical depth $\tau$ with HFI data and substantially reduced systematics across the polarization maps. These advances yield high-fidelity, cross-validated frequency maps and set a strong legacy for future CMB experiments in calibration, bandpass handling, and end-to-end validation of complex instrumental systematics.

Abstract

This paper presents the High Frequency Instrument (HFI) data processing procedures for the Planck 2018 release. Major improvements in mapmaking have been achieved since the previous 2015 release. They enabled the first significant measurement of the reionization optical depth parameter using HFI data. This paper presents an extensive analysis of systematic effects, including the use of simulations to facilitate their removal and characterize the residuals. The polarized data, which presented a number of known problems in the 2015 Planck release, are very significantly improved. Calibration, based on the CMB dipole, is now extremely accurate and in the frequency range 100 to 353 GHz reduces intensity-to-polarization leakage caused by calibration mismatch. The Solar dipole direction has been determined in the three lowest HFI frequency channels to within one arc minute, and its amplitude has an absolute uncertainty smaller than $0.35μ$K, an accuracy of order $10^{-4}$. This is a major legacy from the HFI for future CMB experiments. The removal of bandpass leakage has been improved by extracting the bandpass-mismatch coefficients for each detector as part of the mapmaking process; these values in turn improve the intensity maps. This is a major change in the philosophy of "frequency maps", which are now computed from single detector data, all adjusted to the same average bandpass response for the main foregrounds. Simulations reproduce very well the relative gain calibration of detectors, as well as drifts within a frequency induced by the residuals of the main systematic effect. Using these simulations, we measure and correct the small frequency calibration bias induced by this systematic effect at the $10^{-4}$ level. There is no detectable sign of a residual calibration bias between the first and second acoustic peaks in the CMB channels, at the $10^{-3}$ level.

Planck 2018 results. III. High Frequency Instrument data processing and frequency maps

TL;DR

The paper tackles the challenge of extracting precise CMB polarization and reionization information from Planck HFI data by advancing mapmaking through SRoll, an extended destriper that minimizes intensity-to-polarization leakage from calibration and bandpass mismatches. It leverages extensive end-to-end simulations (FFP10) to quantify residual systematics and uses high-fidelity calibrations based on the orbital and Solar dipoles, achieving sub-per-mil to per-mil-level inter-detector and inter-band consistency in the CMB bands. The combination of sky-based parameter recovery, improved foreground templates, and detailed simulation-driven likelihoods enables a robust first detection of the reionization optical depth with HFI data and substantially reduced systematics across the polarization maps. These advances yield high-fidelity, cross-validated frequency maps and set a strong legacy for future CMB experiments in calibration, bandpass handling, and end-to-end validation of complex instrumental systematics.

Abstract

This paper presents the High Frequency Instrument (HFI) data processing procedures for the Planck 2018 release. Major improvements in mapmaking have been achieved since the previous 2015 release. They enabled the first significant measurement of the reionization optical depth parameter using HFI data. This paper presents an extensive analysis of systematic effects, including the use of simulations to facilitate their removal and characterize the residuals. The polarized data, which presented a number of known problems in the 2015 Planck release, are very significantly improved. Calibration, based on the CMB dipole, is now extremely accurate and in the frequency range 100 to 353 GHz reduces intensity-to-polarization leakage caused by calibration mismatch. The Solar dipole direction has been determined in the three lowest HFI frequency channels to within one arc minute, and its amplitude has an absolute uncertainty smaller than K, an accuracy of order . This is a major legacy from the HFI for future CMB experiments. The removal of bandpass leakage has been improved by extracting the bandpass-mismatch coefficients for each detector as part of the mapmaking process; these values in turn improve the intensity maps. This is a major change in the philosophy of "frequency maps", which are now computed from single detector data, all adjusted to the same average bandpass response for the main foregrounds. Simulations reproduce very well the relative gain calibration of detectors, as well as drifts within a frequency induced by the residuals of the main systematic effect. Using these simulations, we measure and correct the small frequency calibration bias induced by this systematic effect at the level. There is no detectable sign of a residual calibration bias between the first and second acoustic peaks in the CMB channels, at the level.

Paper Structure

This paper contains 12 sections, 10 equations, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Data processing
  3. TOI processing and outputs
  4. On-board signal processing
  5. TOI processing outputs to SRoll
  6. Change in data selection
  7. Noise characterization
  8. TOI processing outputs to simulations and likelihood codes
  9. SRoll-mapmaking solution
  10. The integrated scheme
  11. SRoll implementation
  12. Approximations in the pipeline

Figures (3)

  • Figure 1: Schematic of the HFI pipeline, referencing sections of previous papers (and this work) at each step.
  • Figure 2: Signal level for all HFI detectors near the end of the HFI cryogenic phase of the mission.
  • Figure 3: Histogram the $\chi ^2$ distribution for the maps in which a set of 1000 consecutive pointing periods has been removed (see text). The red line shows the $\chi ^2$ for the last block of pointing periods removed.