GEOMAX: beyond linear compression for three-point galaxy clustering statistics
| العنوان: | GEOMAX: beyond linear compression for three-point galaxy clustering statistics |
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| المؤلفون: | Benjamin Joachimi, Davide Gualdi, Ofer Lahav, Héctor Gil-Marín, Marc Manera |
| المساهمون: | European Commission, La Caixa |
| المصدر: | Digital.CSIC. Repositorio Institucional del CSIC instname |
| بيانات النشر: | Oxford University Press (OUP), 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | Physics, Large-scale structure of Universe, 010308 nuclear & particles physics, Cosmological parameters, Astronomy and Astrophysics, Astrophysics::Cosmology and Extragalactic Astrophysics, 01 natural sciences, Galaxy, Space and Planetary Science, analytical [Methods], 0103 physical sciences, media_common.cataloged_instance, Methods: analytical, European union, Cluster analysis, 010303 astronomy & astrophysics, Mathematical economics, media_common |
| الوصف: | We present the GEOMAX algorithm and its python implementation for a two-step compression of bispectrum measurements. The first step groups bispectra by the geometric properties of their arguments; the second step then maximizes the Fisher information with respect to a chosen set of model parameters in each group. The algorithm only requires the derivatives of the data vector with respect to the parameters and a small number of mock data, producing an effective, non-linear compression. By applying GEOMAX to bispectrum monopole measurements from BOSS DR12 CMASS redshift-space galaxy clustering data, we reduce the 68 per cent credible intervals for the inferred parameters (b1, b2, f, σ8) by 50.4, 56.1, 33.2, and 38.3 per cent with respect to standard MCMC on the full data vector. We run the analysis and comparison between compression methods over 100 galaxy mocks to test the statistical significance of the improvements. On average, GEOMAX performs ∼15 per cent better than geometrical or maximal linear compression alone and is consistent with being lossless. Given its flexibility, the GEOMAX approach has the potential to optimally exploit three-point statistics of various cosmological probes like weak lensing or line-intensity maps from current and future cosmological data sets such as DESI, Euclid, PFS, and SKA. DG acknowledges support from European Union’s Horizon 2020 research and innovation programme ERC (BePreSySe, grant agreement 725327). HGM acknowledges the support from la Caixa Foundation (ID 100010434) with code LCF/BQ/PI18/11630024. MM acknowledges support from the European Union’s Horizon 2020 research and innovation program under Marie Sklodowska-Curie grant agreement No. 6655919y. |
| تدمد: | 1365-2966 0035-8711 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fafb8798443226d430ecc788dca5845dTest https://doi.org/10.1093/mnras/staa1941Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....fafb8798443226d430ecc788dca5845d |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 13652966 00358711 |
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