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 Planck 2015 release, many of which were used and described already in an intermediate paper dedicated to the Planck polarized data at low multipoles. These improvements enabled the first significant measurement of the reionization optical depth parameter using Planck-HFI data. This paper presents an extensive analysis of systematic effects, including the use of end-to-end 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, especially the leakage from intensity to polarization. Calibration, based on the cosmic microwave background (CMB) dipole, is now extremely accurate and in the frequency range 100–353 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 0.35 μK, an accuracy of order 10−4. This is a major legacy from the Planck HFI for future CMB experiments. The removal of bandpass leakage has been improved for the main high-frequency foregrounds 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. End-to-end simulations have been shown to 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 (analogue-to-digital convertor non-linearity residuals). Using these simulations, we have been able to 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.

Highlights

  • This paper, one of a series accompanying the final full release of Planck1 data products, summarizes the calibration, cleaning and other processing steps used to convert High Frequency Instrument (HFI) time-ordered information (TOI) into singlefrequency maps

  • Through the use of the E2E simulations, we have demonstrated that the calibration dispersion inside a frequency band is due to the ADC non-linearity (ADCNL) and we have evaluated the induced calibration bias

  • The improvement obtained through the new mapmaking procedure adopted for this release leads to a much improved dipole calibration stability for polarized bolometers within a frequency band from a few times 10−3 in 2013 and 15 to better than 2×10−5 for the cosmic microwave background (CMB) channels and 2 × 10−4 for 353 GHz

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Summary

High Frequency Instrument data processing and frequency maps

Planck Collaboration: N. Aghanim47, Y. Akrami49,51, M. Ashdown58,5, J. Aumont86, C. Baccigalupi71, M. Ballardini19,35, A. J. Banday86,8, R. B. Barreiro53, N. Bartolo24,54, S. Basak78, K. Benabed48,85, J.-P. Bernard86,8, M. Bersanelli27,39, P. Bielewicz70,8,71, J. R. Bond7, J. Borrill12,83, F. R. Bouchet48,80, F. Boulanger61,47,48, M. Bucher2,6, C. Burigana38,25,41, E. Calabrese75, J.-F. Cardoso48, J. Carron20, A. Challinor50,58,11, H. C. Chiang22,6, L. P. L. Colombo27, C. Combet63, F. Couchot59, B. P. Crill55,10, F. Cuttaia35, P. de Bernardis26, A. de Rosa35, G. de Zotti36,71, J. Delabrouille2, J.-M. Delouis48,85, , E. Di Valentino56, J. M. Diego53, O. Doré55,10, M. Douspis47, A. Ducout48,46, X. Dupac30, G. Efstathiou58,50, F. Elsner67, T. A. Enßlin67, H. K. Eriksen51, E. Falgarone60, Y. Fantaye3,17, F. Finelli35,41, M. Frailis37, A. A. Fraisse22, E. Franceschi35, A. Frolov79, S. Galeotta37, S. Galli57, K. Ganga2, R. T. Génova-Santos52,14, M. Gerbino84, T. Ghosh74,9, J. González-Nuevo15, K. M. Górski55,87, S. Gratton58,50, A. Gruppuso35,41, J. E. Gudmundsson84,22, W. Handley58,5, F. K. Hansen51, S. Henrot-Versillé59, D. Herranz53, E. Hivon48,85, Z. Huang76, A. H. Jaffe46, W. C. Jones22, A. Karakci51, E. Keihänen21, R. Keskitalo12, K. Kiiveri21,34, J. Kim67, T. S. Kisner65, N. Krachmalnicoff71, M. Kunz13,47,3, H. Kurki-Suonio21,34, G. Lagache4, J.-M. Lamarre60, A. Lasenby5,58, M. Lattanzi25,42, C. R. Lawrence55, F. Levrier60, M. Liguori24,54, P. B. Lilje51, V. Lindholm21,34, M. López-Caniego30, Y.-Z. Ma56,73,69, J. F. Macías-Pérez63, G. Maggio37, D. Maino27,39,43, N. Mandolesi35,25, A. Mangilli8, P. G. Martin7, E. Martínez-González53, S. Matarrese24,54,32, N. Mauri41, J. D. McEwen68, A. Melchiorri26,44, A. Mennella27,39, M. Migliaccio82,45, M.-A. Miville-Deschênes62, D. Molinari25,35,42, A. Moneti48, L. Montier86,8, G. Morgante35, A. Moss77, S. Mottet48,80, P. Natoli25,82,42, L. Pagano47,60, D. Paoletti35,41, B. Partridge33, G. Patanchon2, L. Patrizii41, O. Perdereau59, F. Perrotta71, V. Pettorino1, F. Piacentini26, J.-L. Puget47,48, , J. P. Rachen16, M. Reinecke67, M. Remazeilles56, A. Renzi54, G. Rocha55,10, G. Roudier2,60,55, L. Salvati47, M. Sandri35, M. Savelainen21,34,66, D. Scott18, C. Sirignano24,54, G. Sirri41, L. D. Spencer75, R. Sunyaev67,81, A.-S. Suur-Uski21,34, J. A. Tauber31, D. Tavagnacco37,28, M. Tenti40, L. Toffolatti15,35, M. Tomasi27,39, M. Tristram59, T. Trombetti38,42, J. Valiviita21,34, F. Vansyngel47, B. Van Tent64, L. Vibert47,48, P. Vielva53, F. Villa35, N. Vittorio29, B. D. Wandelt48,85,23, I. K. Wehus55,51, and A. Zonca72

Introduction
Data processing
On-board signal processing
TOI processing outputs to SRoll
Change in data selection
Noise characterization
The integrated scheme
TOI processing outputs to simulations and likelihood codes
SRoll implementation
Approximations in the pipeline
PSBa versus PSBb calibration differences
Intensity-to-polarization leakage
Map products
Specific maps for testing purposes
Caveats on the usage of the frequency maps
Calibrated HPRs
Comparison with previous HFI frequency maps
Survey null tests on the data
Power spectra null tests on the data
Statistical analysis of noise and systematics residuals
High multipoles
Summary
Absolute primary photometric calibration
Method
Results
A posteriori inter-calibration within a frequency using the Solar dipole
Simulations of dipole calibration accuracy
Intensity inter-frequency band calibration on CMB anisotropies
Polarization inter-frequency band calibration on CMB anisotropies
Point-source calibration
Conclusions on calibration
Noise and systematic residuals
Consistency of the zodiacal emission removal
Far sidelobes
Half-ring noise correlation
Compression-decompression
Beam mismatch leakage and sub-pixel effects
Undetected glitches
High-energy cosmic-ray showers
5.10.1. Cross-talk
5.10.2. Instrumental polarization parameters
5.10.3. Polarization angle and polarization efficiency
5.11.1. Need for an empirical transfer function
5.11.2. Implementation of the empirical TF
5.11.3. Effects of low-multipole TF residuals at 353 GHz
5.11.4. Summary of constraints on TF residuals
5.12. Intensity-to-polarization leakage from calibration and bandpass mismatch
5.12.1. Polarization leakage from calibration mismatch
5.12.2. Consistency of bandpass leakage coefficients
5.12.3. Effect of bandpass-mismatch leakage on power spectra
5.13. ADC non-linearities
5.14. Systematic effects summary
5.14.1. Systematic effects that do not project directly onto the sky maps
5.14.2. Cross-spectra
5.14.3. All systematic effects summary figure and table
Conclusions
857 Notes
Generating the CMB maps
Convolving the CMB maps with effective beams
Building the foreground maps
Diffuse Galactic components
Unresolved point sources and the cosmic infrared background
Galaxy clusters
Building the sky map TOIs
Mapmaking
Findings
Post-processing
Full Text
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