1. PACS page 1 NHSC SPIRE Data Processing Webinars 7 th March 2012 PACS page 1 SPIRE Photometer Map Making C. Kevin Xu (NHSC/IPAC)

2. PACS page 2 NHSC SPIRE Data Processing Webinars 7 th March 2012 Contents SPIRE mapmakers: Naïve Mapper (pipeline default) MADMapper (more complicated but not better, in HIPE) Baseline Removers: Median baseline remover (pipeline default) Polynomial baseline remover (available in HIPE) Destriper (minimizing stripes iteratively, available in HIPE)

3. PACS page 3 NHSC SPIRE Data Processing Webinars 7 th March 2012 Scan Map Pipeline Flow Chart Baseline Remover + Mapper A “reversed sequence” relative to the chain of data acquisition Default: naïve mapper Alternative: madmapper Options: Default: scan median baseline remover Advanced : polynomial baseline remover Most advanced: an iterative destriper

4. PACS page 4 NHSC SPIRE Data Processing Webinars 7 th March 2012 SPIRE Naïve Mapper signal samplings Sky pixel Flux of a sky pixel is the simple average of all signal samplings (by all detectors) in the pixel, with no weighting for either the measurement error or the position of a sampling: f3f3 f1f1 f2f2 f4f4 f5f5 New version (being developed): with the option of inverse-noise weighted mean.

5. PACS page 5 NHSC SPIRE Data Processing Webinars 7 th March 2012 MADMapper A mapper originally developed for the CMB measurements (MAD: Microwave Anisotropy Dataset). The algorithm is FFT based. Strength: Minimize stripes due to uncorrelated detector drifts (“1/f noise”). Example (taken from an earlier talk of PACS team): Naïve map of a PACS observation MADmap of the same observation Weakness: ringing effect (in the time domain) at any places the timelines have sharp changes (bright sources, edges of scans, etc.)

6. PACS page 6 NHSC SPIRE Data Processing Webinars 7 th March 2012 SPIRE Photometer : Comparison between MADMapper & NaïveMapper 6 mapPlw=madScanMapper(level1, array="PLW") MADMapper: FFT based, minimize uncorrelated 1/f noise Diff. map: MADMap does not improve over the simple naïve map: weak uncorr. 1/f in SPIRE!! MADmap has shadows around the central bright source, due to ringing!! mapPlw=naiveScanMapper(level1, array="PLW") NaïveMapper: simple average

7. PACS page 7 NHSC SPIRE Data Processing Webinars 7 th March 2012 Baseline Remover: Why Is It Needed? example: PSW map without baseline removal you see nothing but stripes stripes: offsets of individual detector channels on the order of ~ 0.1Jy/beam. (mainly caused by errors of Temperature Drift Correction). 7

8. PACS page 8 NHSC SPIRE Data Processing Webinars 7 th March 2012 Baseline Removers in HIPE 8 3 Baseline Removers: Median baseline remover (pipeline default). Polynomial baseline remover. Destriper.

9. PACS page 9 NHSC SPIRE Data Processing Webinars 7 th March 2012 Median Baseline Remover Med subtr, scan-by-scan Module name: baselineRemovalMedian 1. Subtract the median scan-by-scan and detector-by-detector (pipeline default). 2. Subtract the median of each timeline detector-by-detector (not very useful). Time (second) Signal (Jy/beam) Level 1 timeline (uncorrected) of a detector 9 Strength: simple (you know what you are doing). Weakness: residual drifts (as shown in the illustration); over subtraction when there is extended emission.

10. PACS page 10 NHSC SPIRE Data Processing Webinars 7 th March 2012 Polynomial Baseline Remover Med subtr, scan-by-scan Module name: baselineRemovalPolynomial 1.Subtract a polynomial baseline scan-by-scan and detector-by-detector. 2.Subtract a polynomial baseline of each timeline detector-by-detector. Time (second) Signal (Jy/beam) Level 1 timeline (uncorrected) of a detector 10 Strength: more flexible than median removal (often means less residual drifts). Weakness (compare to median removal): more severe over-subtraction (in particular when the polynomial order is high). Poly-order =1 (linear) baseline subtr., whole obs

11. PACS page 11 NHSC SPIRE Data Processing Webinars 7 th March 2012 ~4.5 deg scan length 20”/sec scan speed (parallel mode) Polynomial Baseline Remover: Example Remove “burp” stripes with scan-by-scan polynomial baseline removal: 9 th order polynomial baseline removal Median baseline removal 11 5 th order polynomial baseline removal OBSID = 1342189003 Caveat: this won’t work if the background is structured.

12. PACS page 12 NHSC SPIRE Data Processing Webinars 7 th March 2012 ROI (available for both median & polynomial baseline removers): Excluding bright regions from baseline estimate to minimize the over-subtraction. 12 ROI: Region Of Interest An example for median remover diff. map Data inside this ROI are masked & excluded from baseline removal. Median baseline removal without ROI mask.

13. PACS page 13 NHSC SPIRE Data Processing Webinars 7 th March 2012 Destriper: How It Works Module name: destriper Minimize map stripes by adjusting the baseline subtraction (median or polynomial) iteratively using the naïve mapper: 13 Detector 2 Signal (Jy/beam) Detector 1 time (second) Baselines (poly-order=1) New baseline adjustments (δB, additive). Residual = (signal-baseline) – f pix (this is to be minimized through the iterations.) Sky pixels 12 Pix 1 Pix 2 Signal of j-th detector at i-th sampling Baseline of j-th detector at i-th sampling Naive Mapper:

14. PACS page 14 NHSC SPIRE Data Processing Webinars 7 th March 2012 Destriper: How It Works Module name: destriper Minimize map stripes by adjusting the baseline subtraction (median or polynomial) iteratively using the naïve mapper: 14 Detector 2 Signal (Jy/beam) Detector 1 time (second) Baselines (poly-order=1) Sky pixels 12 Pix 1 Pix 2 Signal of j-th detector at i-th sampling Baseline of j-th detector at i-th sampling Naive Mapper: And a new iteration starts (the iteration stops when δB’s converge to zero or, occasionally, the residuals converge to 0). New baseline: B new =B old +δB Residual = (signal-baseline) – f pix (this is to be minimized through the iterations.) New baseline adjustments (δB, additive).

15. PACS page 15 NHSC SPIRE Data Processing Webinars 7 th March 2012 Destriper: Continued 15 Strength: make better maps in general (no significant over-subtraction); the only SPIRE baseline remover in HIPE for structured emission regions; can do the 2 nd order deglitching (HIPE 9.1). Weakness: longer computation time, higher demand on RAM. Median Baseline Remover: Destriper:

16. PACS page 16 NHSC SPIRE Data Processing Webinars 7 th March 2012 Destriper + Extended Gain Correction 16 Why? The timelines are calibrated using the point source calibration, while a map measures the extended emission. Errors due to the difference between point source calibration and extended source calibration affect the timelines of different detectors differently. The extended gain correction removes these (systematic) errors. Naïve Mapper + Destriper (amplifying low level signals). Naïve Mapper + Destriper + Extended Gain Correction Applying Destriper + Extended Gain Correction can further improve the mapmaking! Detector 1 Detector 2

17. PACS page 17 NHSC SPIRE Data Processing Webinars 7 th March 2012 Other Two HIPE Scripts Related to SPIRE Mapmaking 17 Map Merging: Make maps using timelines of more than 1 observations (particularly useful for parallel mode observations with cross-scans). Solar System Object Motion Correction: Correct the proper motion of solar system objects (causing blurred images) before the mapmaking.

18. PACS page 18 NHSC SPIRE Data Processing Webinars 7 th March 2012 Summary 18 We do not recommend to use MADMapper for SPIRE photometer maps. SPIRE maps, produced by standard pipeline (with median baseline remover and the naïve mapper), can be improved using more advanced polynomial baseline remover or destriper. In general, high quality SPIRE maps can be produced using the destriper together with the naïve mapper. However, this takes longer than the standard processing, and requires more RAM (> 8 GB recommended). In many cases, the polynomial baseline remover (with or without ROI masking) can do a significantly better job than the default median baseline remover. However, be aware of removing real signals.