New efforts to extract CIB maps from Planck in collaboration with O. Doré, G. Lagache, P. Serra Daniel Lenz Advances in Theoretical Cosmology in Light of Data July 13 © 2017 California Institute of Technology. Government sponsorship acknowledged. 

Daniel Lenz, Caltech/JPL New CIB maps Before we start… ❖ Talk to me about: ❖ Galactic HI ❖ Observational multiphase ISM ❖ CIB/CMB component separation ❖ Dust/Reddening ❖ HI intensity mapping (simulations) ❖ Machine learning, Bayesian models, python, Mac OS 2 

Daniel Lenz, Caltech/JPL New CIB maps The Cosmic Infrared Background What is the CIB? 3 

Daniel Lenz, Caltech/JPL New CIB maps The Cosmic Infrared Background ❖ Made up from ❖ Dust in galaxies at z=1-3 ❖ (Re-emitted AGN emission) Lagache+ (2002) 4 

Daniel Lenz, Caltech/JPL New CIB maps The CIB as cosmological probe … of star formation history Planck collaboration (2013 XXX) ❖ Strong constraints on SFH up to z=2.5 ❖ Probe dust temperature across cosmic times ❖ Understand star formation in DM halos 5 

Daniel Lenz, Caltech/JPL New CIB maps The CIB as cosmological probe … of large scale structure to de-lens CMB maps Manzotti+ (2017) 6 

Daniel Lenz, Caltech/JPL New CIB maps ❖ Herschel 500 micron as CIB template ❖ "No lensing" excluded at 7 sigma … of large scale structure to de-lens CMB maps Manzotti+ (2017) The CIB as cosmological probe 7 

Daniel Lenz, Caltech/JPL New CIB maps ❖ CMB lensing and CIB match great in z and MHalo ❖ Ideal probe of relation between dark and luminous matter … of large scale structure to cross-correlate with lensing Planck collaboration (2014 XVIII) The CIB as cosmological probe 8 

Daniel Lenz, Caltech/JPL New CIB maps How to obtain CIB maps? A. Fit different frequency channels with modified blackbody spectra B. Utilize the different angular power spectra of these components 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? 9 

Daniel Lenz, Caltech/JPL New CIB maps Correlation of dust and gas HI Dust ❖ Linear relation to first order, but better model required to get to CIB levels 10 

Daniel Lenz, Caltech/JPL New CIB maps Current CIB maps: Planck (2013 XXX) ❖ Limited sky coverage, hard to access large scales 11 

Daniel Lenz, Caltech/JPL New CIB maps Current CIB maps: GNILC ❖ Planck collaboration (2016 XLVIII), focus on removing CIB from Galactic dust maps ❖ Using the angular power spectra of the two components ❖ Does not agree that well on a pixel-to-pixel basis 12 

Daniel Lenz, Caltech/JPL New CIB maps Current CIB maps: GNILC ❖ Cross correlation with Planck CMB lensing ❖ Missing CIB power, especially on the largest scales 13 

Daniel Lenz, Caltech/JPL New CIB maps 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
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Daniel Lenz, Caltech/JPL New CIB maps Modeling dust foregrounds • • Velocity separation difficult for complex structures and large scales 15 Radial Velocity HVC IVC LVC I = ✏HVC NHVC + ✏IVC NIVC + ✏LVC NLVC 

Daniel Lenz, Caltech/JPL New CIB maps Modeling dust foregrounds • Generalised linear model (GLM) • Radial Velocity I = X i ✏iTi B 16 

Daniel Lenz, Caltech/JPL New CIB maps Modeling dust foregrounds • Generalised linear model (GLM) • • Regularised: • • Accounts for all features along line of sight I = X i ✏iTi B Radial Velocity | Datai Modeli |2 + ↵ · |✏i | 17 

Daniel Lenz, Caltech/JPL New CIB maps Modeling dust foregrounds Model Residual Standard GLM GLM Standard Lenz+ (2016) 18 

Daniel Lenz, Caltech/JPL New CIB maps CIB: Access large scales ❖ Sliding window is moved across the sky ❖ Model is evaluated for each position, yields map of parameters and CIB values 19 

Daniel Lenz, Caltech/JPL New CIB maps CIB: Galactic poles Total FIR intensity CIB 20 

Daniel Lenz, Caltech/JPL New CIB maps Thank you! ❖ Talk to me about: ❖ Galactic HI ❖ Observational multiphase ISM ❖ CIB/CMB component separation ❖ Dust/Reddening ❖ HI intensity mapping (simulations!) ❖ Machine learning, Bayesian models, python, Mac OS 21