New large-scale CIB maps from Planck data

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

Transcript

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