BICEP / Keck XVII: Line of Sight Distortion Analysis: Estimates of Gravitational Lensing, Anisotropic Cosmic Birefringence, Patchy Reionization, and Systematic Errors

Ade, P. A. R.; Ahmed, Z.; Amiri, M.; Barkats, D.; Thakur, R. Basu; Beck, D.; Bischoff, C. A.; Bock, J. J.; Boenish, H.; Bullock, E.; Buza, V.; Cheshire, J. R.; Connors, J.; Cornelison, J.; Crumrine, M.; Cukierman, A.; Denison, E. V.; Dierickx, M.; Duband, L.; Eiben, M.; Fatigoni, S.; Filippini, J. P.; Fliescher, S.; Giannakopoulos, C.; Goeckner-Wald, N.; Goldfinger, D. C.; Grayson, J.; Grimes, P.; Halal, G.; Hall, G.; Halpern, M.; Hand, E.; Harrison, S.; Henderson, S.; Hildebrandt, S. R.; Hubmayr, J.; Hui, H.; Irwin, K. D.; Kang, J.; Karkare, K. S.; Karpel, E.; Kefeli, S.; Kernasovskiy, S. A.; Kovac, J. M.; Kuo, C. L.; Lau, K.; Leitch, E. M.; Lennox, A.; Megerian, K. G.; Minutolo, L.; Moncelsi, L.; Nakato, Y.; Namikawa, T.; Nguyen, H. T.; O'Brient, R.; Ogburn, R. W.; Palladino, S.; Petroff, M. A.; Prouve, T.; Pryke, C.; Racine, B.; Reintsema, C. D.; Richter, S.; Schillaci, A.; Schmitt, B. L.; Schwarz, R.; Sheehy, C. D.; Singari, B.; Soliman, A.; Germaine, T. St; Steinbach, B.; Sudiwala, R. V.; Teply, G. P.; Thompson, K. L.; Tolan, J. E.; Tucker, C.; Turner, A. D.; Umilta, C.; Verges, C.; Vieregg, A. G.; Wandui, A.; Weber, A. C.; Wiebe, D. V.; Willmert, J.; Wong, C. L.; Wu, W. L. K.; Yang, H.; Yoon, K. W.; Young, E.; Yu, C.; Zeng, L.; Zhang, C.; Zhang, S.

doi:10.48550/arxiv.2210.08038

Cited by 1 publication

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“…( 19)), we can explicitly compute all unique 18 responses, which are given in Table I. Similar responses to s Q and s U were recently derived in Appendix E of [27].…”

Section: B Foreground Linear Responsementioning

confidence: 78%

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Foreground-immune CMB lensing reconstruction with polarization

Sailer¹,

Ferraro²,

Schaan³

2022

Preprint

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Extragalactic foregrounds are known to generate significant biases in temperature-based CMB lensing reconstruction. Several techniques, which include "source hardening" and "shear-only estimators" have been proposed to mitigate contamination and have been shown to be very effective at reducing foreground-induced biases. Here we extend both techniques to polarization, which will be an essential component of CMB lensing reconstruction for future experiments, and investigate the "large-lens" limit analytically to gain insight on the origin and scaling of foreground biases, as well as the sensitivity to their profiles. Using simulations of polarized point sources, we estimate the expected bias to both Simons Observatory and CMB-S4 like (polarization-based) lensing reconstruction, finding that biases to the former are minuscule while those to the latter are potentially non-negligible at small scales (L ∼ 1000 − 2000). In particular, we show that for a CMB-S4 like experiment, an optimal linear combination of point-source hardened estimators can reduce the (point-source induced) bias to the CMB lensing power spectrum by up to two orders of magnitude, at a ∼ 4% noise cost relative to the global minimum variance estimator.

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“…( 19)), we can explicitly compute all unique 18 responses, which are given in Table I. Similar responses to s Q and s U were recently derived in Appendix E of [27].…”

Section: B Foreground Linear Responsementioning

confidence: 78%

“…From Eq. ( 29), we see that setting A (0) = O((L/ ) 2 ), A (1) = O(L/ ) forces the response to foregrounds (27) to be suppressed by O((L/ ) 2 ). From Eq.…”

Section: Large-lens Suppressed Estimatorsmentioning

confidence: 90%