Tensor-to-scalar ratio forecasts for extended LiteBIRD frequency configurations
Creators: name: Fuskeland, U.
name: Aumont, J.
name: Aurlien, R.
name: Baccigalupi, C.
name: Banday, A. J.
name: Eriksen, H. K.
name: Errard, J.
name: Génova-Santos, R. T.
name: Hasebe, T.
name: Hubmayr, J.
name: Imada, H.
name: Krachmalnicoff, N.
name: Lamagna, L.
name: Pisano, G.
name: Poletti, D.
name: Remazeilles, M.
name: Thompson, K. L.
name: Vacher, L.
name: Wehus, I. K.
name: Azzoni, S.
name: Ballardini, M.
name: Barreiro, R. B.
name: Bartolo, N.
name: Basyrov, A.
name: Beck, D.
name: Bersanelli, M.
name: Bortolami, M.
name: Brilenkov, M.
name: Calabrese, E.
name: Carones, A.
name: Casas, F. J.
name: Cheung, K.
name: Chluba, J.
name: Clark, S. E.
name: Clermont, L.
name: Columbro, F.
name: Coppolecchia, A.
name: D'Alessandro, G.
name: de Bernardis, P.
name: de Haan, T.
name: de la Hoz, E.
name: De Petris, M.
name: Della Torre, S.
name: Diego-Palazuelos, P.
name: Finelli, F.
name: Franceschet, C.
name: Galloni, G.
name: Galloway, M.
name: Gerbino, M.
name: Gervasi, M.
name: Ghigna, T.
name: Giardiello, S.
name: Gjerløw, E.
name: Gruppuso, A.
name: Hargrave, P.
name: Hattori, M.
name: Hazumi, M.
name: Hergt, L. T.
name: Herman, D.
name: Herranz, D.
name: Hivon, E.
name: Hoang, T. D.
name: Kohri, K.
name: Lattanzi, M.
name: Lee, A. T.
name: Leloup, C.
name: Levrier, F.
name: Lonappan, A. I.
name: Luzzi, G.
name: Maffei, B.
name: Martínez-González, E.
name: Masi, S.
name: Matarrese, S.
name: Matsumura, T.
name: Migliaccio, M.
name: Montier, L.
name: Morgante, G.
name: Mot, B.
name: Mousset, L.
name: Nagata, R.
name: Namikawa, T.
name: Nati, F.
name: Natoli, P.
name: Nerval, S.
name: Novelli, A.
name: Pagano, L.
name: Paiella, A.
name: Paoletti, D.
name: Pascual-Cisneros, G.
name: Patanchon, G.
name: Pelgrims, V.
name: Piacentini, F.
name: Piccirilli, G.
name: Polenta, G.
name: Puglisi, G.
name: Raffuzzi, N.
name: Ritacco, A.
name: Rubino-Martin, J. A.
name: Savini, G.
name: Scott, D.
name: Sekimoto, Y.
name: Shiraishi, M.
name: Signorelli, G.
name: Stever, S. L.
name: Stutzer, N.
name: Sullivan, R. M.
name: Takakura, H.
name: Terenzi, L.
name: Thommesen, H.
name: Tristram, M.
name: Tsuji, M.
name: Vielva, P.
name: Weller, J.
name: Westbrook, B.
name: Weymann-Despres, G.
name: Wollack, E. J.
name: Zannoni, M.
Publisher: name: arXiv
Year: 2023
Subjects: subject: Cosmology and Nongalactic Astrophysics (astro-ph.CO)
subject: FOS: Physical sciences
subject: FOS: Physical sciences

description: LiteBIRD is a planned JAXA-led CMB B-mode satellite experiment aiming for launch in the late 2020s, with a primary goal of detecting the imprint of primordial inflationary gravitational waves. Its current baseline focal-plane configuration includes 15 frequency bands between 40 and 402 GHz, fulfilling the mission requirements to detect the amplitude of gravitational waves with the total uncertainty on the tensor-to-scalar ratio, $δr$, down to $δr&lt;0.001$. A key aspect of this performance is accurate astrophysical component separation, and the ability to remove polarized thermal dust emission is particularly important. In this paper we note that the CMB frequency spectrum falls off nearly exponentially above 300 GHz relative to the thermal dust SED, and a relatively minor high frequency extension can therefore result in even lower uncertainties and better model reconstructions. Specifically, we compare the baseline design with five extended configurations, while varying the underlying dust modeling, in each of which the HFT (High-Frequency Telescope) frequency range is shifted logarithmically towards higher frequencies, with an upper cutoff ranging between 400 and 600 GHz. In each case, we measure the tensor-to-scalar ratio $r$ uncertainty and bias using both parametric and minimum-variance component-separation algorithms. When the thermal dust sky model includes a spatially varying spectral index and temperature, we find that the statistical uncertainty on $r$ after foreground cleaning may be reduced by as much as 30--50 % by extending the upper limit of the frequency range from 400 to 600 GHz, with most of the improvement already gained at 500 GHz. We also note that a broader frequency range leads to better ability to discriminate between models through higher $χ^2$ sensitivity. (abridged)
description: 18 pages, 13 figures. Published in A&amp;A