Periodic Reporting for period 4 - ByoPiC (The Baryon Picture of the Cosmos)
Reporting period: 2021-07-01 to 2023-06-30
The cosmological paradigm of structure formation is both extremely successful and plagued by many enigmas. Not only the nature of the main matter component, dark matter, shaping the structure skeleton in the form of a cosmic web (CW), is mysterious; but also half of the ordinary matter (i.e. baryons) at late times of the cosmic history, remains hidden. ByoPiC focuses on this key issue in astrophysics and cosmology: Where and how are half of the baryons hidden at late times?
ByoPiC answers this central question by detecting, mapping, and assessing the physical properties of hot ionised baryons at large cosmic scales and at late times. This gives a completely new picture of the CW. To this end, ByoPiC performs the first statistically consistent, joint analysis of complementary multiwavelength data: Planck observations tracing hot, ionised baryons via the Sunyaev-Zeldovich (SZ) effect and X-rays, optimally combined with optical and near infrared galaxy surveys. This joint analysis relies on innovative statistical tools to recover all the (cross)information contained in these data in order to detect most of the hidden baryons in CW elements such as (super)clusters and filaments. Thanks to that, ByoPiC performs the most complete and detailed assessment of the hot ionised baryons to the total baryon budget, and identifies the main physical processes driving their evolution in the CW.
ByoPiC answers this central question by detecting, mapping, and assessing the physical properties of hot ionised baryons at large cosmic scales and at late times. This gives a completely new picture of the CW. To this end, ByoPiC performs the first statistically consistent, joint analysis of complementary multiwavelength data: Planck observations tracing hot, ionised baryons via the Sunyaev-Zeldovich (SZ) effect and X-rays, optimally combined with optical and near infrared galaxy surveys. This joint analysis relies on innovative statistical tools to recover all the (cross)information contained in these data in order to detect most of the hidden baryons in CW elements such as (super)clusters and filaments. Thanks to that, ByoPiC performs the most complete and detailed assessment of the hot ionised baryons to the total baryon budget, and identifies the main physical processes driving their evolution in the CW.
Analysing multi-wavelength data, collected/produced in ByoPiC, with novel statistical tools, we detected and characterised the ionised hot baryons in the comic web (CW) from the densest environments (galaxy clusters/groups) to the least dense ones (filaments).
The detection/characterisation of the ionised hot/warm gas content of the CW using the Planck SZ map and X-ray surveys was the heart of ByoPiC. Ever since the 90s, simulations showed that the missing baryons, constituting about half the ordinary matter, should be mainly in the filaments of the CW. ByoPiC hence focused on these environments to find evidence for the missing baryons. After the first significant detection of SZ signal in bridges between cluster-pairs, we focused on large (>30Mpc) filaments detected in the SDSS galaxy survey. We performed the very first detection of their stacked SZ signal, and hence their ionised gas content and we characterised the associated overdensity thanks to the analysis of the lensing maps from Planck. In the same filaments, we also measured for the very first time the X-ray emission from hot/warm ionised gas using ROSAT X-ray survey data, providing the first unambiguous evidence of missing hot/warm baryons in cosmic filaments. We confirmed this discovery using the early release data from the new generation X-ray survey eROSITA..
Based on publicly available hydrodynamic simulations, we have conducted detailed and comprehensive study of the matter distribution around filaments of the cosmic web. We showed for the first time the presence of two extreme populations of filaments: Short filaments short tracing dense environments, containing hot gas and connecting massive haloes and long filaments tracing lower-density environments, filled with warmer and connecting low-mass haloes. We derived the thermodynamic properties of the different gas phases in the short and long filaments and showed that the expected SZ signal in these cosmic web elements agrees with the one we measured in the actual SZ data from Planck.
We performed a systematic investigation of the interplay between filaments and galaxy clusters/groups (i.e. nodes of the CW). It necessitated the development of novel statistical estimators of the anisotropic matter distribution and of innovative filament detection methods. We have shown that the low density environment around clusters where they connect to the CW exhibits a significant anisotropic distribution of matter due to the filamentary pattern. With a harmonic decomposition, we studied the azimuthal distribution of galaxies and identified angular symmetries around clusters/groups, with our new filament detection method we estimated the connectivity to the CW. We found that cluster outskirts are dominated by a quadrupole configuration. We also found that highly connected clusters are more elliptical and grow faster than low-connectivity clusters. We also found that the warm/hot gas in filaments around clusters could explain the soft X-ray excess observed in the ROSAT data.
We unveiled the cosmological information in CW elements. The matter distribution in the universe is the result of the growth of initial density perturbations and their organisation in the cosmic web. The cosmological model governs this evolution and hence cosmological parameters can be inferred from the matter distribution. Using a suite of 44,000 publicly available numerical simulations spanning hundreds of cosmological models, we partitioned the cosmic web into all its elements (nodes, filaments, voids, walls) and performed the very first comprehensive analysis of their cosmological information content. We showed that determining the cosmological parameters from the combination of the CW environments improves the constraints by up to a factor ten.
The detection/characterisation of the ionised hot/warm gas content of the CW using the Planck SZ map and X-ray surveys was the heart of ByoPiC. Ever since the 90s, simulations showed that the missing baryons, constituting about half the ordinary matter, should be mainly in the filaments of the CW. ByoPiC hence focused on these environments to find evidence for the missing baryons. After the first significant detection of SZ signal in bridges between cluster-pairs, we focused on large (>30Mpc) filaments detected in the SDSS galaxy survey. We performed the very first detection of their stacked SZ signal, and hence their ionised gas content and we characterised the associated overdensity thanks to the analysis of the lensing maps from Planck. In the same filaments, we also measured for the very first time the X-ray emission from hot/warm ionised gas using ROSAT X-ray survey data, providing the first unambiguous evidence of missing hot/warm baryons in cosmic filaments. We confirmed this discovery using the early release data from the new generation X-ray survey eROSITA..
Based on publicly available hydrodynamic simulations, we have conducted detailed and comprehensive study of the matter distribution around filaments of the cosmic web. We showed for the first time the presence of two extreme populations of filaments: Short filaments short tracing dense environments, containing hot gas and connecting massive haloes and long filaments tracing lower-density environments, filled with warmer and connecting low-mass haloes. We derived the thermodynamic properties of the different gas phases in the short and long filaments and showed that the expected SZ signal in these cosmic web elements agrees with the one we measured in the actual SZ data from Planck.
We performed a systematic investigation of the interplay between filaments and galaxy clusters/groups (i.e. nodes of the CW). It necessitated the development of novel statistical estimators of the anisotropic matter distribution and of innovative filament detection methods. We have shown that the low density environment around clusters where they connect to the CW exhibits a significant anisotropic distribution of matter due to the filamentary pattern. With a harmonic decomposition, we studied the azimuthal distribution of galaxies and identified angular symmetries around clusters/groups, with our new filament detection method we estimated the connectivity to the CW. We found that cluster outskirts are dominated by a quadrupole configuration. We also found that highly connected clusters are more elliptical and grow faster than low-connectivity clusters. We also found that the warm/hot gas in filaments around clusters could explain the soft X-ray excess observed in the ROSAT data.
We unveiled the cosmological information in CW elements. The matter distribution in the universe is the result of the growth of initial density perturbations and their organisation in the cosmic web. The cosmological model governs this evolution and hence cosmological parameters can be inferred from the matter distribution. Using a suite of 44,000 publicly available numerical simulations spanning hundreds of cosmological models, we partitioned the cosmic web into all its elements (nodes, filaments, voids, walls) and performed the very first comprehensive analysis of their cosmological information content. We showed that determining the cosmological parameters from the combination of the CW environments improves the constraints by up to a factor ten.
* SZ maps are usually constructed from single-instrument data. We developed the first algorithm that undertakes the combination of multi-instrument data to construct a high resolution SZ map and derived a new pressure profile of the gas in galaxy clusters.
* Standard methods to construct SZ maps from multi-frequency data are based on inversion techniques. We followed a novel approach based on deep learning to distinguish the SZ signal buried in the astrophysical components and uncovers signal from fainter sources.
* We devised a novel approach of cluster detection using Artificial Neural Networks and the decomposition of the Planck signal into its different astrophysical sources to increase the detection low-mass clusters/groups of galaxies.
* We developed an approach fully based on supervised machine learning in order to derive physical properties of galaxies (star-formation rate, stellar masses and galaxy types).
* We developed a new method to automatically retrieve the CW filamentary pattern, using graphs embedded in an unsupervised Machine Learning framework, which is natively optimised for actual data.
* We developed a novel approach based on harmonic decomposition to analyse and quantify the anisotropic matter distribution around nodes of the CW and go beyond the usual assumption of cluster/group sphericity.
* We emulated numerical simulations of the CW using generative deep learning methods which permit to recover the mildly non-linear evolution of structure formation.
* We devised a new statistical estimator based on the three-point mean relative velocity of halos to measure cosmological parameters.
* We achieved the first unambiguous detection of the hot ionised gas, constituting the missing baryons, in the largest elements of the CW: superclusters and filaments.
* We exhibited the presence of two extreme populations of filaments each tracing different environments.
* We developed a harmonic decomposition-based technique to study the cluster outskirts the impact of the filaments connected to them on the properties of their gas and galaxies.
* We showed that the analysis of the matter distribution, when partitioned into nodes, filaments, wall and voids, improves by an order of magnitude the determination of cosmological parameters.
* Standard methods to construct SZ maps from multi-frequency data are based on inversion techniques. We followed a novel approach based on deep learning to distinguish the SZ signal buried in the astrophysical components and uncovers signal from fainter sources.
* We devised a novel approach of cluster detection using Artificial Neural Networks and the decomposition of the Planck signal into its different astrophysical sources to increase the detection low-mass clusters/groups of galaxies.
* We developed an approach fully based on supervised machine learning in order to derive physical properties of galaxies (star-formation rate, stellar masses and galaxy types).
* We developed a new method to automatically retrieve the CW filamentary pattern, using graphs embedded in an unsupervised Machine Learning framework, which is natively optimised for actual data.
* We developed a novel approach based on harmonic decomposition to analyse and quantify the anisotropic matter distribution around nodes of the CW and go beyond the usual assumption of cluster/group sphericity.
* We emulated numerical simulations of the CW using generative deep learning methods which permit to recover the mildly non-linear evolution of structure formation.
* We devised a new statistical estimator based on the three-point mean relative velocity of halos to measure cosmological parameters.
* We achieved the first unambiguous detection of the hot ionised gas, constituting the missing baryons, in the largest elements of the CW: superclusters and filaments.
* We exhibited the presence of two extreme populations of filaments each tracing different environments.
* We developed a harmonic decomposition-based technique to study the cluster outskirts the impact of the filaments connected to them on the properties of their gas and galaxies.
* We showed that the analysis of the matter distribution, when partitioned into nodes, filaments, wall and voids, improves by an order of magnitude the determination of cosmological parameters.