期刊
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
卷 484, 期 1, 页码 L29-L34出版社
OXFORD UNIV PRESS
DOI: 10.1093/mnrasl/sly242
关键词
methods: analytical; methods: data analysis; methods: statistical; cosmology: cosmological parameters; cosmology: large-scale structure of Universe
资金
- Perren studentship
- IMPACT studentship
- Labex ILP part of the Idex SUPER [ANR-10-LABX-63]
- Agence Nationale de la Recherche, as part of the programme Investissements d'avenir [ANR-11-IDEX-0004-02.e]
- STFC [RG84196, RG70655 LEAG/506]
- European Union's Horizon 2020 research and innovation programme under Marie Sklodowska-Curie grant [6655919]
- European Research Council Advanced Grant [FP7/291329]
- STFC [ST/M001334/1] Funding Source: UKRI
We present a novel method to compress galaxy clustering three-point statistics and apply it to redshift space galaxy bispectrum monopole measurements from BOSS DR12 CMASS data considering a k-space range of 0.03 - 0.12 h/Mpc. The method consists in binning together bispectra evaluated at sets of wavenumbers forming closed triangles with similar geometrical properties: the area, the cosine of the largest angle, and the ratio between the cosines of the remaining two angles. This enables us to increase the number of bispectrum measurements, for example by a factor of 23 over the standard binning (from 116 to 2734 triangles used), which is otherwise limited by the number of mock catalogues available to estimate the covariance matrix needed to derive parameter constraints. The 68 per cent credible intervals for the inferred parameters (b(1), b(2), f, sigma(8)) are thus reduced by (-39 per cent, -49 per cent, -29 per cent, -22 per cent), respectively. We find very good agreement with the posteriors recently obtained by alternative maximal compression methods. This new method does not require the a-priori computation of the data vector covariance matrix and has the potential to be directly applicable to other three-point statistics (e.g. galaxy clustering, weak gravitational lensing, 21-cm emission line) measured from future surveys such as DESI, Euclid, PFS, and SKA.
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