https://insu.hal.science/insu-03747403Lavaux, GuilhemGuilhemLavauxIAP - Institut d'Astrophysique de Paris - INSU - CNRS - Institut national des sciences de l'Univers - SU - Sorbonne UniversiteĢ - CNRS - Centre National de la Recherche ScientifiqueJasche, JensJensJascheIAP - Institut d'Astrophysique de Paris - INSU - CNRS - Institut national des sciences de l'Univers - SU - Sorbonne UniversiteĢ - CNRS - Centre National de la Recherche ScientifiqueUnmasking the masked Universe: the 2M++ catalogue through Bayesian eyesHAL CCSD2016methods: data analysismethods: statisticalgalaxies: statisticslarge-scale structure of UniverseAstrophysics - Cosmology and Nongalactic Astrophysics[SDU] Sciences of the Universe [physics][SDU.ASTR] Sciences of the Universe [physics]/Astrophysics [astro-ph]POTHIER, Nathalie2022-08-08 11:28:342023-05-01 04:01:142022-08-08 11:28:35enJournal articleshttps://insu.hal.science/insu-03747403/document10.1093/mnras/stv2499application/pdf1This work describes a full Bayesian analysis of the Nearby Universe as traced by galaxies of the 2M++ survey. The analysis is run in two sequential steps. The first step self-consistently derives the luminosity-dependent galaxy biases, the power spectrum of matter fluctuations and matter density fields within a Gaussian statistic approximation. The second step makes a detailed analysis of the three-dimensional large-scale structures, assuming a fixed bias model and a fixed cosmology. This second step allows for the reconstruction of both the final density field and the initial conditions at z = 1000 assuming a fixed bias model. From these, we derive fields that self-consistently extrapolate the observed large-scale structures. We give two examples of these extrapolation and their utility for the detection of structures: the visibility of the Sloan Great Wall, and the detection and characterization of the Local Void using DIVA, a Lagrangian based technique to classify structures.