Overview of LFI maps generation and their characteristics Davide Maino University of Milano, Dept. of Physics UC Davis, 20 May 2013 Davide Maino Overview of LFI maps generation and their characteristics 1/22
Outline 1 Data Processing Overview 2 Map-making 3 Systematic Effects 4 Internal Validation 5 Maps Characteristics 6 Planck Maps 7 Next Steps Davide Maino Overview of LFI maps generation and their characteristics 2/22
Operations Planck was launched on 14 May 2009 and the current data release includes temperature maps based on the first 15.5 months of observations Planck is still alive and LFI is currently taking data HFI was switched off on January 2012, beyond the nominal period and now is supporting LFI providing essential H/K telemetry (e.g. 4K cooler) LFI is expected to continue at least till September 2013 Data are of incredible quality! Davide Maino Overview of LFI maps generation and their characteristics 3/22
Data Processing Overview DPC approaches data reduction with specific tasks aiming to estimate and correct instrumental systematic effects There are three main logical levels: Level 1: H/K and Science telemetry from the satellite are transformed into raw timelines and stored into dedicated databases with the associated time information Level 2: instrument information is gathered and ingested into the Instrument Model, removal of systematic effects, flag data of suspected quality, photometric calibration and creation of maps and ancillary products Level 3: more science here with component separation, power spectra estimation and extraction of cosmological parameters Each step is internally validated (with dedicated sims) and most of the DPC work is spent cross-checking internally and between the two instruments Davide Maino Overview of LFI maps generation and their characteristics 4/22
LFI pipeline Raw data from L1 AD/C & 1Hz spikes L1 raw TOI diode Focal plane geometry Planck velocity Attitude history file AD/C Non linearity correction & 1 Hz Spikes Removal Differentiated TOI Detector pointing Computation Check, Fill Gaps & flag Subsampled data Sky/load mean & GMF evaluation GMF values Pointing pipeline Detector pointing diode weights Differentiation & diode combination Differentiated TOI Detector pointing Planck velocity Galaxy mask Phase binned data Dipole Fit Dipolefit Raw gain table Iterative calibration Raw gain table Differentiated TOI Gain Application Final Gain table OSG Smoothing Calibrated TOI Detector pointing Galaxy mask Noise characteristics MapMaker (MADAM) LEVEL 2 pipeline Planck Symposium - 28 Jan 2004 Davide Maino Overview of LFI maps generation and their characteristics 5/22
Flagged data 30 GHz 44 GHz 70 GHz Missing [%] 0.00014 0.00023 0.00032 Anomalies [%] 0.82220 0.99763 0.82925 Manoeuvres [%] 8.07022 8.07022 8.07022 Usable [%] 91.10744 90.93191 91.10021 During L1 and L2 pipeline all data are checked sample-by-sample to discard, flag all unusable samples and data acquired during manoeuvres and special operations (e.g. Sorption cooler switch-over) About 1% of the data are discarded due to instrument anomaly Davide Maino Overview of LFI maps generation and their characteristics 6/22
Beams reconstruction Beams contours (-20dB) from planet transit. From S/C attitude and this geometrical calibration, pointing for each horn is reconstructed Davide Maino Overview of LFI maps generation and their characteristics 7/22
Detector Pointing LFI M-Beams boresight direction in Planck FOV 0.1 error boxes and positions magnified by a factor 100 0.05 V coordinate (in-scan) 0 Jupiter 1 Jupiter 2 Jupiter 3 Jupiter 4 Stacked J1J2 Stacked J3J4 Stacked J1J2J3J4-0.05-0.1-0.1-0.05 0 0.05 0.1 U coordinate (cross-scan) Pointing reconstruction: error smaller that 0.5. Several corrections applied: stellar aberration and wobble angle (thermoelastic deformation changing the relative orientation between start-tracker and reference body frame). Davide Maino Overview of LFI maps generation and their characteristics 8/22
Effective Beams Band FWHM [ ] e Ω [arcmin 2 ] 30 32.239±0.013 1.320±0.031 1189.51±0.84 44 27.01±0.55 1.034±0.031 833±32 70 13.252±0.033 1.223±0.026 200.7±1.0 Scanning beams are used to compute effective beams which gives pixel-by pixel the beam shape projected in the sky when scanning strategy is accounted for. Fundamental for point source extractions and computation of beam window function Davide Maino Overview of LFI maps generation and their characteristics 9/22
10-2 10-1 Frequency [Hz] 100 101 10-3 10-2 10-1 Frequency [Hz] 100 101 109 107 108 OD-146 OD-186 OD-207 OD-226 OD-280 OD-326 OD-386 OD-446 OD-486 106 Power Spectrum Density [µk2 /Hz] 109 108 107 106 Power Spectrum Density [µk2 /Hz] OD-146 OD-186 OD-207 OD-226 OD-280 OD-326 OD-386 OD-446 OD-486 105 10-3 105 107 108 OD-146 OD-186 OD-207 OD-226 OD-280 OD-326 OD-386 OD-446 OD-486 106 105 Power Spectrum Density [µk2 /Hz] 109 LFI TOI Noise Estimation 10-3 10-2 10-1 Frequency [Hz] 100 101 Noise estimation is used as a diagnostic tool and provide information for Monte Carlo Simulations. Determines also the correct horn weights used during the map-making process. Noise pipeline is checked against noise derived from HR maps and it is compatible at 1% level Davide Maino Overview of LFI maps generation and their characteristics 10/22
Photometric Calibration This is the most important and critical aspect of data reduction pipeline. The LFI one works as follows estimate dipole amplitude and alignment of dipole for any direction create the expected level of the dipole signal accounting for the full-beam shape fit real data with dipole with iterative approach (raw gains) filter raw gains or use 4K to calibrate data Calib. Accuracy estimated to be 0.82% (30), 0.55% (44) and 0.62% (70) Davide Maino Overview of LFI maps generation and their characteristics 11/22
Photometric Calibration Davide Maino Overview of LFI maps generation and their characteristics 12/22
Map-making Calibrated TOI for each radiometer are input of madam map-making code, together with pointing data The algorithm is a maximum-likelihood destriping and estimates in this fashion the amplitude of the 1/f -noise baseline, subtract from the timelines and then simply bins the resulting TOI into a map Maps produced at different levels: Frequency maps, HR maps and Survey maps Low resolution maps used for the computation of the noise covariance matrix Davide Maino Overview of LFI maps generation and their characteristics 13/22
Systematic effecs CMB - TT SD B HR A Total D C E F G H Two strategies for systematic effects analysis: Null tests: primary tool to see systematic effect residual w.r.t. white noise level Sims: Assess their impact on TOI using in-flight H/K data. This approach provides a powerful tool to check for systematics Davide Maino Overview of LFI maps generation and their characteristics 14/22
LFI Internal Validation - Null tests C l [µk 2 ] 10-2 10-1 10 0 TT SS TT jk EE SS EE jk C l [µk 2 ] 10-2 10-1 10 0 TT SS TT jk EE SS EE jk C l [µk 2 ] 10-2 10-1 10 0 TT SS TT jk EE SS EE jk 10 1 10 2 10 3 l 10 1 10 2 10 3 l 10 1 10 2 10 3 Quality of LFI maps is assessed and verified by a set of null-tests in an almost automatic way Several data combination (radiometer, horn-pairs, frequency) on different time-scales (1 hour, survey, full-mission): difference at horn level at even/odd surveys clearly reveals side lobe effect Null-test power spectra are used to check total level of systm. effects to be compared w.r.t. white noise level and systematic effects analysis l Davide Maino Overview of LFI maps generation and their characteristics 15/22
LFI Internal Validation - Check Spectra l(l +1)C l 44 /2π [ µk 2] 0 2000 4000 6000 70 vs 44GHz α =1.006 ± 0.017 l =[2, 400] 4000 5000 0 1000 2000 3000 6000 l(l +1)C 70 l /2π [ µk 2] l(l +1)C l 30 /2π [ µk 2] 0 2000 4000 6000 70 vs 30GHz α =0.994 ± 0.022 l = [50, 400] 4000 5000 0 1000 2000 3000 6000 l(l +1)C 70 l /2π [ µk 2] l(l +1)C l 30 /2π [ µk 2] 0 2000 4000 6000 44 vs 30GHz α =0.979 ± 0.027 l = [50, 400] 4000 5000 0 1000 2000 3000 6000 l(l +1)C 44 l /2π [ µk 2] Compute power spectra in multipole range around the first acoustic peak removing the unresolved point source contributions. Spectra are consistent within errors. 30 and 44/70 have different approaches to gain applied Hausman test assess consistency at 70 GHz showing no statistically significant problem Spectra from horn-pairs and from all 12 radiometers: χ 2 analysis shows compatibility with null-hypothesis Davide Maino Overview of LFI maps generation and their characteristics 16/22
Maps Characteristics Uncertainty Applies to Method Gain calib All sky WMAP dipole Zero level All sky Galactic Cosecant model Beam All sky GRASP models via FeBecop CC non-cmb ground/flight bandpass leakage Resid. Sys All sky Null-tests Property 30 44 70 Frequency [GHz] 28.4 44.1 70.4 Noise rms/pixel [µk CMB ] 9.2 12.5 23.2 Gain Uncert 0.82% 0.55% 0.62% Zero Level Uncert [µk CMB ] ±2.23 ±0.78 ±0.64 Davide Maino Overview of LFI maps generation and their characteristics 17/22
Planck-LFI 30 GHz Davide Maino Overview of LFI maps generation and their characteristics 18/22
Planck-LFI 44 GHz Davide Maino Overview of LFI maps generation and their characteristics 19/22
Planck-LFI 70 GHz Davide Maino Overview of LFI maps generation and their characteristics 20/22
Future Steps & Conclusions Maps and other products are of very high quality and consistency Next goal will be the analysis of polarisation data. This implies the following steps: 4π beam in calibration based on in-band response Removal of Galactic Straylight Assessment of expected systematics in polarisation Davide Maino Overview of LFI maps generation and their characteristics 21/22
Davide Maino Overview of LFI maps generation and their characteristics 22/22