Planck 2013 results. II. Low Frequency Instrument data processing

TL;DR

This paper details the data processing pipeline for the Planck Low Frequency Instrument, including calibration, map-making, noise estimation, and beam characterization, based on 15.5 months of observational data at multiple frequencies.

Contribution

It introduces a comprehensive data processing pipeline for LFI data, including calibration, noise modeling, and beam analysis, enhancing the accuracy of full-sky microwave maps.

Findings

Produced calibrated, cleaned sky maps at 30, 44, and 70 GHz.

Estimated noise properties and covariance matrices for statistical analysis.

Characterized main beams using Jupiter transits for improved calibration.

Abstract

We describe the data processing pipeline of the Planck Low Frequency Instrument (LFI) data processing centre (DPC) to create and characterize full-sky maps based on the first 15.5 months of operations at 30, 44 and 70 GHz. In particular, we discuss the various steps involved in reducing the data, starting from telemetry packets through to the production of cleaned, calibrated timelines and calibrated frequency maps. Data are continuously calibrated using the modulation induced on the mean temperature of the cosmic microwave background radiation by the proper motion of the spacecraft. Sky signals other than the dipole are removed by an iterative procedure based on simultaneous fitting of calibration parameters and sky maps. Noise properties are estimated from time-ordered data after the sky signal has been removed, using a generalized least square map-making algorithm. A destriping code (Madam) is employed to combine radiometric data and pointing information into sky maps, minimizing the variance of correlated noise. Noise covariance matrices, required to compute statistical uncertainties on LFI and Planck products, are also produced. Main beams are estimated down to the -20 dB level using Jupiter transits, which are also used for the geometrical calibration of the focal plane.