<i>Planck</i>2015 results

Ade, Peter A. R.; Aghanim, N.; Ashdown, M.; Aumont, J.; Baccigalupi, C.; Banday, A. J.; Barreiro, R. B.; Bartolo, N.; Battaner, E.; Benabed, K.; Benoı̂t, A.; Benoit-Lévy, A.; Bernard, J.-P.; Bersanelli, M.; Bielewicz, P.; Bonaldi, A.; Bonavera, L.; Bond, J. R.; Borrill, J.; Bouchet, F. R.; Bucher, M.; Burigana, C.; Butler, R. C.; Calabrese, E.; Cardoso, J.-F.; Catalano, A.; Chamballu, A.; Chary, R.-R.; Christensen, P. R.; Colombi, S.; Colombo, L. P. L.; Crill, B. P.; Curto, A.; Cuttaia, F.; Danese, L.; Davies, R. D.; Davis, R. J.; Bernardis, P. de; Rosa, A. de; Zotti, G. de; Delabrouille, J.; Dickinson, C.; Diego, J. M.; Dole, H.; Donzelli, S.; Doré, O.; Douspis, M.; Ducout, A.; Dupac, X.; Efstathiou, G.; Elsner, F.; Enßlin, T. A.; Eriksen, H. K.; Fergusson, J.; Finelli⋆, F.; Forni, O.; Frailis, M.; Franceschi, E.; Frejsel, A.; Galeotta, S.; Galli, S.; Ganga, K.; Giard, M.; Giraud‐Héraud, Y.; Gjerløw, E.; González‐Nuevo, J.; Górski, K. M.; Gratton, Serge; Gregorio, A.; Gruppuso, A.; Hansen, F. K.; Hanson, D.; Harrison, D. L.; Henrot-Versillé, S.; Herranz, D.; Hildebrandt, S. R.; Hivon, E.; Hobson, M.; Holmes, W. A.; Hornstrup, A.; Hovest, W.; Huffenberger, K. M.; Hurier, G.; Jaffe, A. H.; Jaffe, T. R.; Juvela, M.; Keihänen, E.; Keskitalo, R.; Kiiveri, K.; Kisner, T. S.; Knoche, J.; Kunz, M.; Kurki-Suonio, H.; Lähteenmäki, A.; Lamarre, J.-M.; Lasenby, A.; Lattanzi, M.; Lawrence, C. R.; Leahy, J. P.; Leonardi, R.; Lesgourgues, Julien; Levrier, F.; Liguori, M.; Lilje, P. B.; Linden-Vørnle, M.; Lindholm, V.; López‐Caniego, M.; Lubin, P. M.; Macías-Pérez, J. F.; Maggio, G.; Maino, D.; Mandolesi, N.; Mangilli, A.; Martín, P. G.; Martínez-González, E.; Masi, S.; Matarrese, S.; Mazzotta, P.; McGehee, P.; Meinhold, P. R.; Melchiorri, A.; Mendes, L.; Mennella, A.; Migliaccio, M.; Mitra, S.; Montier, L.; Morgante, G.; Mortlock, D.; Moss, A.; Munshi, D.; Murphy, John A.; Naselsky, P.; Nati, F.; Natoli, P.; Netterfield, C. B.; Nørgaard-Nielsen, H. U.; Novikov, D.; Новиков, И. Д.; Paci, F.; Pagano, L.; Paoletti, D.; Partridge, B.; Pasian, F.; Patanchon, G.; Pearson, T. J.; Perdereau, O.; Perotto, L.; Perrotta, F.; Pettorino, V.; Pierpaoli, E.; Pietrobon, D.; Pointecouteau, É.; Polenta, G.; Pratt, G. W.; Prézeau, G.; Prunet, S.; Puget, J.-L.; Rachen, J. P.; Rebolo, R.; Reinecke, M.; Remazeilles, M.; Renzi, A.; Rocha, G.; Rosset, C.; Rossetti, M.; Roudier, G.; Rubiño-Martín, J. A.; Rusholme, B.; Sandri, M.; Santos, D.; Савелайнен, М.; Scott, Douglas; Seiffert, M. D.; Shellard, E. P. S.; Spencer, L. D.; Stolyarov, V.; Stompor, R.; Sutton, D.; Suur-Uski, A.-S.; Sygnet, J.-F.; Tauber, J. A.; Terenzi, L.; Toffolatti, L.; Tomasi, M.; Tristram, M.; Tucci, M.; Tuovinen, J.; Valenziano, L.; Väliviita, J.; Tent, B. Van; Vassallo, T.; Vielva, P.; Villa, F.; Wade, L. A.; Wandelt, B. D.; Watson, R. A.; Wehus, I. K.; Yvon, D.; Zacchei, A.; Zonca, A.

doi:10.1051/0004-6361/201525813

Cited by 64 publications

(6 citation statements)

References 44 publications

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“…We use the publicly available 2015 Planck high frequency instrument (HFI) and low frequency instrument (LFI) maps in this analysis [37,38] and construct Compton-y maps using the Needlet Internal Linear Combination (NILC) algorithm that is described in Delabrouille et al [39] and Guilloux et al [40]. For comparison, we will also make use of the publicly available Planck estimates of y described in Aghanim et al [9] which uses the same set of temperature maps.…”

Section: B Planck Mapsmentioning

confidence: 99%

Constraints on the redshift evolution of astrophysical feedback with Sunyaev-Zel’dovich effect cross-correlations

Pandey¹,

Baxter²,

Xu³

et al. 2019

Phys. Rev. D

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Section: B Planck Mapsmentioning

confidence: 99%

Constraints on the redshift evolution of astrophysical feedback with Sunyaev-Zel’dovich effect cross-correlations

Pandey¹,

Baxter²,

Xu³

et al. 2019

Phys. Rev. D

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“…We use the publicly available 2015 Planck High Frequency Instrument (HFI) and Low Frequency Instrument (LFI) maps in this analysis [48,49] and construct Compton-y maps using the Needlet Internal Linear Combination (NILC) algorithm that is described in Delabrouille et al [18] and Guilloux et al [28]. For comparison, we will also make use of the publicly available Planck estimates of y described in Aghanim et al [3] which uses the same set of temperature maps.…”

Section: B Planck Mapsmentioning

confidence: 99%

Constraints on the redshift evolution of astrophysical feedback with Sunyaev-Zeldovich effect cross-correlations

Pandey,

Baxter,

et al. 2019

Preprint

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An understanding of astrophysical feedback is important for constraining models of galaxy formation and for extracting cosmological information from current and future weak lensing surveys. The thermal Sunyaev-Zel'dovich effect, quantified via the Compton-y parameter, is a powerful tool for studying feedback, because it directly probes the pressure of the hot, ionized gas residing in dark matter halos. Cross-correlations between galaxies and maps of Compton-y obtained from cosmic microwave background surveys are sensitive to the redshift evolution of the gas pressure, and its dependence on halo mass. In this work, we use galaxies identified in year one data from the Dark Energy Survey and Compton-y maps constructed from Planck observations. We find highly significant (roughly 12σ) detections of galaxy-y cross-correlation in multiple redshift bins. By jointly fitting these measurements as well as measurements of galaxy clustering, we constrain the halo biasweighted, gas pressure of the Universe as a function of redshift between 0.15 z 0.75. We compare these measurements to predictions from hydrodynamical simulations, allowing us to constrain the amount of thermal energy in the halo gas relative to that resulting from gravitational collapse.

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“…FIRAS is still a unique dataset to constrain the µ distortion monopole. Being effectively insensitive to the absolute signal level, modern imagers like Planck often rely on the knowledge of the CMB monopole measured by FIRAS and on the annual modulation of the CMB dipole anisotropy (Solar dipole) induced by the motion of the spacecraft (orbital dipole), which is known very well, to achieve an accurate intercalibration of the frequency channels [4,7]. At present, the precision of this technique approaches 10 −4 [15], not far from the precision of the T 0 measurement [39].…”

Section: Comparison With Previous Resultsmentioning

confidence: 99%

CMB spectral distortions revisited: a new take on $μ$ distortions and primordial non-Gaussianities from FIRAS data

Bianchini,

Fabbian

2022

Preprint

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Deviations from the blackbody spectral energy distribution of the Cosmic Microwave Background (CMB) are a precise probe of physical processes active both in the early universe (such as those connected to particle decays and inflation) and at later times (e.g. reionization and astrophysical emissions). Limited progress has been made in the characterization of these spectral distortions after the pioneering measurements of the FIRAS instrument on the COBE satellite in the early 1990s, which mainly targeted the measurement of their average amplitude across the sky. Since at present no follow-up mission is scheduled to update the FIRAS measurement, in this work we re-analyze the FIRAS data and produce a map of µ-type spectral distortion across the sky. We provide an updated constraint on the µ distortion monopole | µ | < 47 × 10 −6 at 95% confidence level that sharpens the previous FIRAS estimate by a factor of ∼ 2. We also constrain primordial non-Gaussianities of curvature perturbations on scales 10 k 5 × 10 4 through the cross-correlation of µ distortion anisotropies with CMB temperature and, for the first time, the full set of polarization anisotropies from the Planck satellite. We obtain upper limits on fNL 3.6 × 10 6 and on its running nNL 1.4 that are limited by the FIRAS sensitivity but robust against galactic and extragalactic foreground contaminations. We revisit previous similar analyses based on data of the Planck satellite and show that, despite their significantly lower noise, they yield similar or worse results to ours once all the instrumental and astrophysical uncertainties are properly accounted for. Our work is the first to self-consistently analyze data from a spectrometer and demonstrate the power of such instrument to carry out this kind of science case with reduced systematic uncertainties.

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Planck2015 results

Cited by 64 publications

References 44 publications

Constraints on the redshift evolution of astrophysical feedback with Sunyaev-Zel’dovich effect cross-correlations

Constraints on the redshift evolution of astrophysical feedback with Sunyaev-Zel’dovich effect cross-correlations

Constraints on the redshift evolution of astrophysical feedback with Sunyaev-Zeldovich effect cross-correlations

CMB spectral distortions revisited: a new take on $μ$ distortions and primordial non-Gaussianities from FIRAS data

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