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New large-scale CIB maps from Planck data

3ef87aeb8d713b39b9119be13b92aa3b?s=47 Daniel
July 20, 2018

New large-scale CIB maps from Planck data

Presentation at the Planck legacy data release meeting at COSPAR2018 in Pasadena, CA.

3ef87aeb8d713b39b9119be13b92aa3b?s=128

Daniel

July 20, 2018
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  1. New large-scale CIB maps from Planck data in collaboration with

    O. Doré, G. Lagache, B. Hensley Daniel Lenz COSPAR 2018, Pasadena July 20th © 2018 California Institute of Technology. Government sponsorship acknowledged.
  2. Daniel Lenz, JPL/Caltech Large-scale CIB maps from Planck data Conclusions

    ❖ New CIB maps for ~30% of the sky, 217-857 GHz ❖ Fewer systematics, larger sky fraction than previous work ❖ Powerful for cross- correlations and de- lensing !2
  3. Daniel Lenz, JPL/Caltech Large-scale CIB maps from Planck data Conclusions

    ❖ New CIB maps for ~30% of the sky, 217-857 GHz ❖ Fewer systematics, larger sky fraction than previous work ❖ Powerful for cross- correlations and de- lensing !2 CIB x CMB lensing
  4. Daniel Lenz, JPL/Caltech Large-scale CIB maps from Planck data What

    is the CIB? ❖ Made up from dust in galaxies at z=1-3 ❖ First detected in FIRAS data (Puget+ 1996) Extragalactic background light !3
  5. Daniel Lenz, JPL/Caltech Large-scale CIB maps from Planck data What

    is the CIB? ❖ Made up from dust in galaxies at z=1-3 ❖ First detected in FIRAS data (Puget+ 1996) Extragalactic background light !3
  6. Daniel Lenz, JPL/Caltech Large-scale CIB maps from Planck data Schmidt+

    (2015) ❖ Strong constraints on star formation history ❖ Probe dust temperature across cosmic times ❖ Understand star formation in DM halos !4 Why study the CIB? Star-formation!
  7. Daniel Lenz, JPL/Caltech Large-scale CIB maps from Planck data Manzotti

    (2017) Why study the CIB? Grav. lensing! ❖ CIB kernel and the CMB lensing kernel are well matched ❖ Internal de-lensing and CIB is very complimentary for BB reconstruction !5
  8. Daniel Lenz, JPL/Caltech Large-scale CIB maps from Planck data Planck

    collaboration (2013, XVII) !6 Why study the CIB? Grav. lensing! ❖ Cross-correlation of CIB and CMB lensing strongly detected in Planck data ❖ Lots of room for improvement: Sky fraction, CIB data, new CMB lensing map
  9. Daniel Lenz, JPL/Caltech Large-scale CIB maps from Planck data How

    to obtain CIB maps? ❖ Galactic thermal dust and CIB dust dominate on large scales at ~200 to 1000 GHz ❖ How to disentangle them? !7
  10. Daniel Lenz, JPL/Caltech Large-scale CIB maps from Planck data How

    to obtain CIB maps? A. Fit different frequency channels with modified blackbody spectra B. Use the different angular power spectra of these components (GNILC) C. Use template maps of Galactic dust (e.g. HI-based) ❖ Galactic thermal dust and CIB dust dominate on large scales at ~200 to 1000 GHz ❖ How to disentangle them? !7
  11. Daniel Lenz, JPL/Caltech Large-scale CIB maps from Planck data Correlation

    of dust and gas HI Dust ❖ Linear relation to first order (Boulanger+ 1996) ❖ But better model required to get to CIB levels !8
  12. Daniel Lenz, JPL/Caltech Large-scale CIB maps from Planck data HI4PI

    Survey ❖ Merges data from Effelsberg and Parkes ❖ Replaces LAB as state-of-the-art full-sky HI survey ❖ Higher sensitivity & resolution, fewer systematics, full sampling 20 21 22 log(NHI [cm 2]) 180 135 90 45 0 315 270 225 180 60 30 0 30 60 HI4PI collaboration
 (2017) !9
  13. Daniel Lenz, JPL/Caltech Large-scale CIB maps from Planck data Two

    challenges !10 ❖ Spectrally ❖ O(1000) velocity channels in HI ❖ Need to control overfitting
  14. Daniel Lenz, JPL/Caltech Large-scale CIB maps from Planck data Two

    challenges ❖ Spatially ❖ Dust-to-gas ratios vary over the sky ❖ Need to preserve large-scale CIB power !10 ❖ Spectrally ❖ O(1000) velocity channels in HI ❖ Need to control overfitting
  15. Daniel Lenz, JPL/Caltech Large-scale CIB maps from Planck data HI-based

    dust models • • Velocity separation difficult for complex structures and large scales Radial Velocity HVC IVC LVC I = ✏HVC NHVC + ✏IVC NIVC + ✏LVC NLVC !11
  16. Daniel Lenz, JPL/Caltech Large-scale CIB maps from Planck data •

    Generalised linear model (GLM) • Radial Velocity I = X i ✏iTi B !12 HI-based dust models
  17. Daniel Lenz, JPL/Caltech Large-scale CIB maps from Planck data •

    Generalised linear model (GLM) • • Regularised: • • Accounts for all features along line of sight I = X i ✏iTi B Radial Velocity |Datai Modeli |2 + ↵ · |✏i | !13 HI-based dust models
  18. Daniel Lenz, JPL/Caltech Large-scale CIB maps from Planck data Preliminary

    Results !14 (give us two weeks)
  19. Daniel Lenz, JPL/Caltech Large-scale CIB maps from Planck data Maps:

    Smaller regions !15
  20. Daniel Lenz, JPL/Caltech Large-scale CIB maps from Planck data Maps:

    Large-scale map !16 ~30% of the sky, 5 frequencies, 10 arcmin
  21. Daniel Lenz, JPL/Caltech Large-scale CIB maps from Planck data Gaussianity

    !17 ❖ Patch-by-patch analysis ❖ Full sky PDF very Gaussian ❖ Molecular gas adds skewness
  22. Daniel Lenz, JPL/Caltech Large-scale CIB maps from Planck data Comparison

    to earlier work !18 Maps
  23. Daniel Lenz, JPL/Caltech Large-scale CIB maps from Planck data Based

    on spatial information: GNILC ❖ Power-spectrum based ❖ Designed to remove CIB from Galactic dust maps ❖ Over-subtraction of CIB Planck (2016 XLVIII) !19
  24. Daniel Lenz, JPL/Caltech Large-scale CIB maps from Planck data HI-based:

    Planck (2014 XXX) ❖ ~10 individual fields, HI data from the GBT ❖ Two larger fields from EBHIS and GASS ❖ One field cleaned at a time ❖ Manual fine-tuning !20
  25. Daniel Lenz, JPL/Caltech Large-scale CIB maps from Planck data Comparison

    to earlier work: Small fields !21 ❖ Different data sets, resolutions, sky regions ❖ Apples-to-apples comparison yields great agreement
  26. Daniel Lenz, JPL/Caltech Large-scale CIB maps from Planck data Comparison

    to earlier work: Small fields !21 ❖ Different data sets, resolutions, sky regions ❖ Apples-to-apples comparison yields great agreement
  27. Daniel Lenz, JPL/Caltech Large-scale CIB maps from Planck data Comparison

    to earlier work !22 Power spectra
  28. Daniel Lenz, JPL/Caltech Large-scale CIB maps from Planck data CIB

    auto power spectra !23 unconstrained ❖ Great agreement with Planck (2014 XXX) ❖ Extends to larger scales ❖ Maps will be public
  29. Daniel Lenz, JPL/Caltech Large-scale CIB maps from Planck data CIB

    - CMB lensing cross power !24 unconstrained ❖ Great agreement with Planck (2013 XVIII) ❖ Extends to larger scales ❖ GNILC x Phi shows weaker correlation
  30. Daniel Lenz, JPL/Caltech Large-scale CIB maps from Planck data CIB

    - CMB lensing cross correlation coefficient !25 ❖ > 60% correlation for l >= 100 ❖ ~10-15% higher than with GNILC CIB ❖ Powerful in combination with Planck lensing map for BB de-lensing
  31. Daniel Lenz, JPL/Caltech Large-scale CIB maps from Planck data Conclusions

    ❖ Large-scale Planck CIB maps for 5 frequencies ❖ Significant improvement in component separation ❖ Better understanding of systematics ❖ Large scales are challenging! !26
  32. Daniel Lenz, JPL/Caltech Large-scale CIB maps from Planck data Conclusions

    ❖ Large-scale Planck CIB maps for 5 frequencies ❖ Significant improvement in component separation ❖ Better understanding of systematics ❖ Large scales are challenging! ❖ CIB is powerful probe of large-scale structure ❖ Study cosmic star-formation ❖ De-lensing for current and future CMB experiments !26
  33. Daniel Lenz, JPL/Caltech Large-scale CIB maps from Planck data Thank

    you! !27
  34. Daniel Lenz, JPL/Caltech Large-scale CIB maps from Planck data Backup

    !28
  35. Daniel Lenz, JPL/Caltech Large-scale CIB maps from Planck data Large-scale

    bias !29 Input CIB
  36. Daniel Lenz, JPL/Caltech Large-scale CIB maps from Planck data Large-scale

    bias !29 Mean per field Input CIB
  37. Daniel Lenz, JPL/Caltech Large-scale CIB maps from Planck data Large-scale

    bias !29 Mean per field After mean subtraction Input CIB
  38. Daniel Lenz, JPL/Caltech Large-scale CIB maps from Planck data Large-scale

    bias !30 Large scales Small scales
  39. Daniel Lenz, JPL/Caltech Large-scale CIB maps from Planck data Large-scale

    bias !30 ❖ Separating one region at a time removes large-scale power ❖ Essential for CIB reconstruction at low l Large scales Small scales
  40. Daniel Lenz, JPL/Caltech Large-scale CIB maps from Planck data Spatial

    selection ❖ Build dust models that preserve large-scale power ❖ Use consistency checks and cross correlations ❖ Difficult trade-off! !31 Offsets in the HI/ dust correlation
  41. Daniel Lenz, JPL/Caltech Large-scale CIB maps from Planck data Spatial

    selection !32 Offsets in the HI/ dust correlation (smoothed) ❖ Build dust models that preserve large-scale power ❖ Use consistency checks and cross correlations ❖ Difficult trade-off!
  42. Daniel Lenz, JPL/Caltech Large-scale CIB maps from Planck data Based

    on spatial information: GNILC Planck (2016 XLVIII !33
  43. Daniel Lenz, JPL/Caltech Large-scale CIB maps from Planck data Comparison

    to earlier work: Small fields ❖ Very similar morphologies despite totally different spatial selections ❖ Yet differences remain! MJy/sr !34
  44. Daniel Lenz, JPL/Caltech Large-scale CIB maps from Planck data Comparison

    to earlier work: Small fields ❖ Very similar morphologies despite totally different spatial selections ❖ Yet differences remain! MJy/sr !34
  45. Daniel Lenz, JPL/Caltech Large-scale CIB maps from Planck data Comparison

    to earlier work: Small fields ❖ Differences can be partially attributed to the underlying HI data ❖ Radial velocity cuts have strong effect !35
  46. Daniel Lenz, JPL/Caltech Large-scale CIB maps from Planck data Comparison

    to earlier work: Large field Planck (2014 XXX) This work !36
  47. Daniel Lenz, JPL/Caltech Large-scale CIB maps from Planck data Comparison

    to earlier work: Large field Planck (2014 XXX) This work Planck 2014 - This work !36