<i>Planck</i>2015 results

Aghanim, N.; Arnaud, M.; Ashdown, M.; Aumont, J.; Baccigalupi, C.; Banday, A. J.; Barreiro, R. B.; Bartlett, J. G.; Bartolo, N.; Battaner, E.; Battye, Richard A.; Benabed, K.; Benoı̂t, A.; Benoit-Lévy, A.; Bernard, J.-P.; Bersanelli, M.; Bielewicz, P.; Bock, J. J.; Bonaldi, A.; Bonavera, L.; Bond, J. R.; Borrill, J.; Bouchet, F. R.; Burigana, C.; Butler, R. C.; Calabrese, E.; Cardoso, J.-F.; Catalano, A.; Challinor, A.; Chiang, H. C.; Christensen, P. R.; Churazov, E.; Clements, D. L.; Colombo, L. P. L.; Combet, C.; Comis, B.; Coulais, A.; 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.; Désert, F.‐X.; Dickinson, C.; Diego, J. M.; Dolag, K.; 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.; Fraisse, A. A.; Franceschi, E.; Frejsel, A.; Galeotta, S.; Galli, S.; Ganga, K.; Génova-Santos, R. T.; Giard, M.; González‐Nuevo, J.; Górski, K. M.; Gregorio, A.; Gruppuso, A.; Gudmundsson, J. E.; Hansen, F. K.; Harrison, D. L.; Henrot-Versillé, S.; Hernández-Monteagudo, C.; Herranz, D.; Hildebrandt, S. R.; Hivon, E.; Holmes, W. A.; Hornstrup, A.; Huffenberger, K. M.; Hurier, G.; Jaffe, A. H.; Jones, W. C.; Juvela, M.; Keihänen, E.; Keskitalo, R.; Kneißl, R.; Knoche, J.; Kunz, M.; Kurki-Suonio, H.; Lacasa, F.; Lagache, G.; Lähteenmäki, A.; Lamarre, J.-M.; Lasenby, A.; Lattanzi, M.; Leonardi, R.; Lesgourgues, Julien; Levrier, F.; Liguori, M.; Lilje, P. B.; Linden-Vørnle, M.; López‐Caniego, M.; Macías-Pérez, J. F.; Maffei, B.; Maggio, G.; Maino, D.; Mandolesi, N.; Mangilli, A.; Maris, M.; Martín, P. G.; Martínez-González, E.; Masi, S.; Matarrese, S.; Melchiorri, A.; Melin, J.-B.; Migliaccio, M.; Miville-Deschênes, M.-A.; Moneti, A.; Montier, L.; Morgante, G.; Mortlock, D.; Munshi, D.; Murphy, John A.; Naselsky, P.; Nati, F.; Natoli, P.; Noviello, F.; Novikov, D.; Новиков, И. Д.; Paci, F.; Pagano, L.; Pajot, F.; Paoletti, D.; Pasian, F.; Patanchon, G.; Perdereau, O.; Perotto, L.; Pettorino, V.; Piacentini, F.; Piat, M.; Pierpaoli, E.; Pietrobon, D.; Plaszczynski, S.; Pointecouteau, É.; Polenta, G.; Ponthieu, N.; Pratt, G. W.; Prunet, S.; Puget, J.-L.; Rachen, J. P.; Reinecke, M.; Remazeilles, M.; Renault, C.; Renzi, A.; Ristorcelli, I.; Rocha, G.; Rossetti, M.; Roudier, G.; Rubiño-Martín, J. A.; Rusholme, B.; Sandri, M.; Santos, D.; Sauvé, A.; Савелайнен, М.; Savini, G.; Scott, Douglas; Spencer, L. D.; Stolyarov, V.; Stompor, R.; Sunyaev, R.; Sutton, D.; Suur-Uski, A.-S.; Sygnet, J.-F.; Tauber, J. A.; Terenzi, L.; Toffolatti, L.; Tomasi, M.; Tramonte, D.; Tristram, M.; Tucci, M.; Tuovinen, J.; Valenziano, L.; Väliviita, J.; Tent, B. Van; Vielva, P.; Villa, F.; Wade, L. A.; Wandelt, B. D.; Wehus, I. K.; Yvon, D.; Zacchei, A.; Zonca, A.

doi:10.1051/0004-6361/201525826

Cited by 296 publications

(88 citation statements)

References 103 publications

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“…In order to estimate the tSZ signal associated with voids we use the Compton-y parameter maps made available by the Planck Collaboration [64]. The available maps were derived by applying internal linear combination (ILC) techniques to the Planck intensity maps from 30 to 857 GHz.…”

Section: B Tsz and Cmb Mapsmentioning

confidence: 99%

Measurement of the thermal Sunyaev-Zel’dovich effect around cosmic voids

et al. 2018

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We stack maps of the thermal Sunyaev-Zel'dovich effect produced by the Planck Collaboration around the centers of cosmic voids defined by the distribution of galaxies in the CMASS sample of the Baryon Oscillation Spectroscopic Survey, scaled by the void effective radii. We report a first detection of the associated cross-correlation at the 3.4σ level: voids are under-pressured relative to the cosmic mean. We compare the measured Compton-y profile around voids with a model based solely on the spatial modulation of halo abundance with environmental density. The amplitude of the detected signal is marginally lower than predicted by an overall amplitude αv = 0.67 ± 0.2. We discuss the possible interpretations of this measurement in terms of modelling uncertainties, excess pressure in low-mass halos, or non-local heating mechanisms.

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Section: B Tsz and Cmb Mapsmentioning

confidence: 99%

Measurement of the thermal Sunyaev-Zel’dovich effect around cosmic voids

et al. 2018

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“…In the past decade cosmological constraints from galaxy cluster observations have been derived mainly from cluster abundance measurements (see e.g. Vikhlinin et al 2009b; Planck Collaboration: Ade et al 2016b;Schellenberger & Reiprich 2017b), while it has only recently been possible to derive cosmological parameter bounds from measurements of the spatial clustering of galaxy clusters thanks to the analysis of the Planck comptonization maps of the thermal SZ effect (Planck Collaboration: Ade et al 2014bAde et al , 2016a. In contrast, the use of galaxy cluster mass profile as cosmic probe remains a challenging task (see e.g.…”

Section: Introductionmentioning

confidence: 99%

Average dark matter halo sparsity relations as consistency check of mass estimates in galaxy cluster samples

Corasaniti

2019

Monthly Notices of the Royal Astronomical Society

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The dark matter halo sparsity provides a direct observational proxy of the halo mass profile, characterizing halos in terms of the ratio of masses within radii which enclose two different overdensities. Previous numerical simulation analyses have shown that at a given redshift the halo sparsity carries cosmological information encoded in the halo mass profile. Moreover, its ensemble averaged value can be inferred from prior knowledge of the halo mass function at the overdensities of interest. Here, we present a detailed study of the ensemble average properties of the halo sparsity. In particular, using halo catalogs from high-resolution N-body simulations, we show that its ensemble average value can be estimated from the ratio of the averages of the inverse halo masses as well as the ratio of the averages of the halo masses at the overdensity of interests. This can be relevant for galaxy clusters data analyses. As an example, we have estimated the average sparsity properties of galaxy clusters from the LoCuSS and HIFLUGCS datasets respectively. The results suggest that the expected consistency of the different average sparsity estimates can provide a test of the robustness of mass measurements in galaxy cluster samples.

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“…Upcoming proposed CMB missions like PIXIE [42] would have an instrumental noise of nearly 3 − 4 orders of magnitude better than FIRAS and hence would be able to measure the CMB spectral distortions with an unprecedented accuracy [43]. Measurement of the spectral distortions signal will also depend upon the successful cleaning of the foreground contaminations [41,[44][45][46][47]. Other CMB missions like CORE [48] and LiteBIRD [49] would be able to measure the spatially fluctuating part of the spectral distortions [50] at a much better precision compared to Planck.…”

Section: Introductionmentioning

confidence: 99%

Polarized anisotropic spectral distortions of the CMB: galactic and extragalactic constraints on photon-axion conversion

Mukherjee

Khatri

Wandelt

2018

J. Cosmol. Astropart. Phys.

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We revisit the cosmological constraints on resonant and non-resonant conversion of photons to axions in the cosmological magnetic fields. We find that the constraints on photonaxion coupling and primordial magnetic fields are much weaker than previously claimed for low mass axion like particles with masses m a 5 × 10 −13 eV. In particular we find that the axion mass range 10 −14 eV ≤ m a ≤ 5×10 −13 eV is not excluded by the CMB data contrary to the previous claims. We also examine the photon-axion conversion in the Galactic magnetic fields. Resonant conversion in the large scale coherent Galactic magnetic field results in 100% polarized anisotropic spectral distortions of the CMB for the mass range 10 −13 eV m a 10 −11 eV. The polarization pattern traces the transverse to line of sight component of the Galactic magnetic field while both the anisotropy in the Galactic magnetic field and electron distribution imprint a characteristic anisotropy pattern in the spectral distortion. Our results apply to scalar as well as pseudoscalar particles. For conversion to scalar particles, the polarization is rotated by 90 • allowing us to distinguish them from the pseudoscalars. For m a 10 −14 eV we have non-resonant conversion in the small scale turbulent magnetic field of the Galaxy resulting in anisotropic but unpolarized spectral distortion in the CMB. These unique signatures are potential discriminants against the isotropic and non-polarized signals such as primary CMB, and µ and y distortions with the anisotropic nature making it accessible to experiments with only relative calibration like Planck, LiteBIRD, and CORE. We forecast for PIXIE as well as for these experiments using Fisher matrix formalism.

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Planck2015 results

Cited by 296 publications

References 103 publications

Measurement of the thermal Sunyaev-Zel’dovich effect around cosmic voids

Measurement of the thermal Sunyaev-Zel’dovich effect around cosmic voids

Average dark matter halo sparsity relations as consistency check of mass estimates in galaxy cluster samples

Polarized anisotropic spectral distortions of the CMB: galactic and extragalactic constraints on photon-axion conversion

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