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The Baryon Picture of the Cosmos

Periodic Reporting for period 3 - ByoPiC (The Baryon Picture of the Cosmos)

Reporting period: 2020-01-01 to 2021-06-30

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, is mysterious; but also half of the ordinary matter (i.e. baryons) at late times of the
cosmic history, remains unobserved, or hidden! ByoPiC focuses on this key and currently unresolved issue in
astrophysics and cosmology: Where and how are half of the baryons hidden at late times? ByoPiC will
answer that central question by detecting, mapping, and assessing the physical properties of hot ionised
baryons at large cosmic scales and at late times. This will give a completely new picture of the cosmic web,
added to its standard tracers, i.e. galaxies made of cold and dense baryons. To this end, ByoPiC will perform
the first statistically consistent, joint analysis of complementary multiwavelength data: Planck observations
tracing hot, ionised baryons via the Sunyaev-Zeldovich effect, optimally combined with optical and near
infrared galaxy surveys as tracers of cold baryons. This joint analysis will rely on innovative statistical tools
to recover all the (cross)information contained in these data in order to detect most of the hidden baryons in
cosmic web elements such as (super)clusters and filaments. These newly detected elements will then be
assembled to reconstruct the cosmic web as traced by both hot ionised baryons and galaxies. Thanks to that,
ByoPiC will perform the most complete and detailed assessment of the census and contribution of hot
ionised baryons to the total baryon budget, and identify the main physical processes driving their evolution
in the cosmic web. Catalogues of new (super)clusters and filaments, and innovative tools, will be key
deliverable products, allowing for an optimal preparation of future surveys.
Large and complementary datasets are needed to attain ByoPiC goals. We have thus actively worked to collect or produce these data. They include data tracing hot gas in the cosmic web sucha Sunyaev-Zeldovich (SZ) signal and X-ray emission, but also catalogues of cosmic web elements (clusters, superclusters, cluster-pairs, and filaments) derived from galaxy surveys.

Galaxy clusters and groups are the best laboratories to study hot gas properties and galaxy evolution in dense environments and they represent the anchors of cosmic-web filaments. We have developed optimised cluster-finder techniques to construct the most complete and unbiased catalogues of clusters and groups. We find five times more candidate clusters to study and to reconstruct full distribution of the cosmic web. We have conducted studies to understand, model and probe the gas distribution in clusters and to use this information to test the cosmological model. Galaxy clusters observed in SZ are strong cosmological probes. We revisited the analysis of cluster counts and showed that the tension between CMB and cluster cosmology is significantly reduced.

We also focused on low density environments, i.e. filaments and superclusters. We searched for and characterised the gas content of superclusters and filaments using the Planck SZ map. We performed the very first statistically significant detection of SZ signal in filaments between cluster-pairs. This was extended to analyse of large-scale cosmic filaments where we did the first measure of their gas properties using Planck lensing and SZ maps. Furthermore, with existing catalogues of superclusters, we assessed their content in hot ionised gas and showed that they make up to 50% of the missing baryons. In parallel, we reinvestigated the exceptional cluster pair A401-A399 with a first comprehensive study combining SZ, X-ray and optical/nearIR data that showed the fossil nature of the filament.

Galaxy properties are tracers of the environment and of the main physical processes such as star formation. They have been widely explored in the dense environment of clusters but not so much in other cosmic web elements. We explored the properties of galaxies around cosmic filaments and measured an excess of passive galaxies near the filament spine, higher than that of active galaxies, with star formation rate and stellar mass gradients pointing towards the spine. We investigated statistical observables of the large-scale environment. We used multipole moments to study the azimuthal distribution of galaxies around clusters. We found that massive clusters are connected to a larger number of filaments, with an average of 2. We investigated the detailed position in the cosmic web of the Coma cluster. We detected 3 secure large-scale filaments connecting Coma to several other clusters and measured a median connectivity of 2.5 in agreement with clusters of similar mass and numerical simulations.

Finally, the cosmic web is a complex multi-scale structure. Identification of its individual large elements (e.g. superclusters, filaments) is challenging because they are not well-defined objects. We explore ways to detect and reconstruct it as a whole using deep learning, e.g. Generative Adversarial Networks (GAN), graphs, and community theory.
* We have developed the first algorithm that undertakes the combination of multi-instrument data to construct a high resolution SZ map from the ACT (ground based survey) and Planck (multi-frequency space survey). We have shown how significantly improved is the sensitivity of the resulting SZ map that covers 1000 square degrees.
* We have developed the first approach fully based on machine learning in order to derive physical properties of galaxies: star-formation rate and stellar masses.
* We have also followed a novel approach of component separation based on deep learning that enables to distinguish the SZ signal buried in the many astrophysical components of the submillimeter sky. This approach uncovers SZ signal from much fainter sources than what can be achieved with standard methods.
* We have developed a new approach to automatically retrieve the underlying cosmic-web’s filamentary pattern from a galaxy distribution using graph theory embedded in an unsupervised Machine Learning framework.
* We performed the first analysis of the superclusters signature in SZ maps and have been able to detect up to half of the missing baryons. Moreover, we have uncovered first characteristics of the hot baryons (profile, density, temperature, etc.).
* Missing baryons in the form of hot gas is expected in the cosmic filaments. For the first time, we have studied the gas content in cosmic filaments of sizes tens of megaparsec, neither resulting from cluster interactions nor linking cluster-pairs. In order to investigate their content in the hot gas, we have constructed a brand new catalogue of cosmic filaments and measured the profile of the gas pressure.
* Low density environments such as cluster outskirts are widely used to understand the interplay between cosmic web elements and its effect on gas and galaxy properties. For the first time and by contrast with standard approaches based on the analysis of symmetrised profiles, we used multipole moments to study the distribution of galaxies and identify angular symmetries around galaxy clusters both (in simulations and in actual surveys).
Cosmic filaments detected in the SDSS galaxy survey
New method to detect cosmic filaments using graph theory
Hot gas observed via Sunyaev-Zeldovich effect with Planck satellite
Exploring the distribution of galaxies from cluster centers to filamemts with multipole decompositio