Lensing reconstruction from intensity maps

Transcription

1 Lensing reconstruction from intensity maps Simon Foreman Canadian Institute for Theoretical Astrophysics with Alex van Engelen, Daan Meerburg, Joel Meyers Aspen Center for Physics February 6, 2018

2 line :) Lensing reconstruction from intensity maps Simon Foreman Canadian Institute for Theoretical Astrophysics with Alex van Engelen, Daan Meerburg, Joel Meyers Aspen Center for Physics February 6, 2018

3 A cartoon of gravitational lensing Image: NASA/ESA Directly traces low-redshift structure Neutrino masses, structure growth, cross-correlations

4 CMB lensing vs. LIM lensing Single 2d map CMB lensing: Full 3d map reduce noise tomography LIM lensing: Intrinsic statistics: linear, Gaussian Intrinsic statistics: nonlinear, non-gaussian Techniques/instruments: well-established Techniques/instruments: in progress Images: ESA; Romeo et al. 2017

5 Lensing estimator, in 2d Lensing correlates different Fourier modes of the observed temperature: D T ( ~`)T ( L ~ E ~`) = f( ~`, ~ L) ( ~ L) Construct lensing estimator based on these correlations: Z ˆ(~ L)= g( ~`, L ~ ~`)T ( ~`)T ( L ~ ~`) ~` (ˆn) = Take power spectrum of ˆ map (or cross-correlate): D L)E ˆ(~ L) ˆ ( ~ = C L + N (G) L Z 0 s d ( ) ( ˆn; z[ ]) Hu 2001 signal! bias / noise

6 Lensing estimator, in 3d 3d temperature field, observed within comoving thickness L : T (~x k k = 2?,x k ) T ( ~`,k k ), j, j =0, 1, 2,... L Lensing estimator takes similar form: Z ˆ(~ L; kk )= g( )T ( ~`,k k )T ( L ~ ~`, k k ) Extra contribution to D ˆ(~ L; kk1 ) ˆ ( ~ L; k k2 )E ~` h ˆˆ i from gravitational nonlinearity: = C L + N (G) L + Z ~` Z ~`0 ( ) httt T i c Zahn & Zaldarriaga 2006 partially removable Foreman et al. in prep

7 Our forecasts Main goal: quantify impact of gravitational contributions on lensing estimator Assumptions (21cm): T b matter (with Kaiser RSD) tree-level perturbation theory for grav. 4-pt. function instrumental noise = thermal noise, set by Tsys, nbase, foregrounds kill first few line-of-sight modes can cross-correlate with LSST

8 Forecasts for 21cm surveys PRELIMINARY S/N on lensing power spectra for 21cm surveys z width of f sky happleapplei happleg LSST i happle LSST i each band [MHz] SKA1-Low 6 <z<14 27 deg CHIME 1.1 <z< HIRAX 1.3 <z< lensing auto spectrum lensing x ~LSST galaxy clustering lensing x ~LSST galaxy lensing Conclusion: cross-correlations might be worth a try! Key factor: angular resolution

9 Forecasts for single-dish IM survey (~CCAT-p) (fsky/f sky) fid 1/2 S/N m dish 0m dish 1 0 z = 4 k â k k â g LSST k â g LSST 6m dish m dish z 1 = 6 z = 6 1 j max j max parameterizes noise level ( `max fixed by D dish, obs) Auto/cross detectability 1 1 z = 8 z = 8 depends on S/N of IM signal, f sky fsky fid and ( = 16 deg 2 ) j max j max

10 Conclusions Techniques from CMB lensing can also be applied to line intensity maps see also: Zahn & Zaldarriaga 2006; Lu & Pen 2008; Pourtsidou & Metcalf 2014; Romeo et al Gravitational nonlinearity adds bias + noise to the lensing estimator, but this can be partially mitigated Foreman et al. in prep First detections of cross-correlations may be possible in near-future surveys, contingent on control of systematics