Mass and selection biases of galaxy clusters: a multi-probe approach
PRIN 2022: PROGETTI DI RICERCA DI RILEVANTE INTERESSE NAZIONALE – Prot. 20228B938N
Research Unit 1: S. Andreon (INAF-Milan) & Reasearch Unit 2: M. De Petris (Sapienza-Rome)
Embedded Files
Galaxy clusters are fascinating astrophysical objects with a great interest for cosmological applications.
Baryons, detectable at different wavelengths, represent only small amount in the mass budget (up to 10% in the diffuse IntraCluster Medium, ICM, and up to 10% in galaxies), while Dark Matter, is the dominant ingredient (85%) but unfortunately only traceable through its gravitational impact.
Up to now, clusters have been mostly selected by their minority component: directly through the emission from galaxies or the hot ICM, or through the scattering of the Cosmic Microwave Background (CMB) on the ICM (Sunayev-Zeldovich, SZ, effect). These selection criteria are convenient, have provided us with large samples at various redshifts and have helped us establish many of the cluster properties. However, the samples are biased and the amplitude of the bias is yet to be determined.
Furthermore, knowledge of the cluster mass is needed for both cosmological and astrophysical studies, but mass cannot be directly observed and needs to be derived from the collected photons.
Our understanding of galaxy cluster physics, and our ability to use clusters to constrain cosmological parameters, are limited by mass and selection biases of the studied samples.
Given the critical role played by the mass and selection biases, in this project we (Unit 1) exploit the only sample available so far that contains a sizable fraction of low surface brightness clusters missed in X-ray- and SZ-selected surveys. We aim at better determining the role of the selection bias (against low surface brightness clusters) in scaling relations of cosmological and astrophysical interest with the assistance of our data and hydrodynamic simulations; and we (Unit 2) investigate the amount of mass bias by using multi-probe approaches based on high-resolution and high-sensitivity observations with the support of state-of-the-art hydrodynamical simulations.
With this project, we can address, uniquely and for the first time, the mass bias of a sample non-ICM biased and the impact of the ICM selection on the mass bias by comparing ICM-biased and ICM-unbiased samples. We hope to overcome the limitation of literature studies that address mass bias and selection effects independently.
Sample selection biases: how much the way galaxy clusters are selected impact the measured cluster properties? We are completing the analysis of a ICM-unbiased cluster sample.
The environment of low X-ray surface brightness galaxy clusters. Puddu et al. near submission
Where do low Xray surface brightness clusters sit in relation to filaments? Zanardini et al. near submission
Investigating thermodynamical properties of low ICM content clusters of galaxies. Observations in progress.
Stars and ICM are minority components. Gravitational lensing (alias the deformation of the shape of background galaxies due to the cluster potential well) allows us to detect clusters without relying on minority tracers (stars and ICM). In addition, gravitational lensing returns masses free of assumptions about the cluster dynamical status. A number of works based on samples selected by gravitational lensing are on-going:
The uncommon intracluster medium features of the first massive clusters selected independently of their baryon content. Andreon et al. submitted
Gravity-selected cluster samples: the Compton Y mass scaling. Andreon et al. in preparation
Gravity-selected cluster samples: the X-ray luminosity mass scaling. Li et al. in preparation
Toward Euclid: characterization of a richness-complete sample of galaxy clusters undetected by Planck. SZ (NIKA2) and X-ray observations in progress.
Pathway to Euclid: characterization of a baryon-unbiased sample. Analysis underway.
Euclid: characterization of a complete gravity-selected cluster sample undetected in X-ray and SZ. In planning stage, in collaboration with the Euclid consortium.
Finally, in the current epoch of advanced data science methods and techniques, we need to analyze large observational samples of clusters encapsulating the global properties of the overall population allowing differences between cluster's specific, individual objects. To investigate heterogeneity of the pressure profiles across different galaxy clusters, a fully hierarchical model has been built paying particular attention to make the computation feasible on state of the art computing facilities, and applied to the current largest sample of clusters with resolved pressure profiles (58 clusters) Castagna et al. in preparation.
Cluster mass inference could be impacted by cluster dynamical state. Studying the most efficient morphological proxies, or a combination of them, to infer the dynamical state with survey-quality data and with hydrodynamical simulations is mandatory.
A new and promising approach based on Zernike Polynomials (ZP), already validated on SZ maps generated by synthetic clusters of The300, is extended to different resolutions and wavelengths images and real data to infer the dynamical state.
Planck nearby clusters (109 clusters at z<0.1), Capalbo V. et al. under submission;
NIKA2 Large Program Sunyaev Zeldovich dataset (35 clusters at 0.5<z<0.9 observed at 150 & 260 GHz), Pappalardo E. et al. in preparation;
CHEX-MATE sample (118 clusters observed by XMM-Newton), Benincasa A. et al. in preparation;
optical images (galaxy number density maps), Ferragamo A. et al. on-going analysis.
Other different metrics can be used to classify cluster dynamical state: a possibility has been the recent use of two different statistical approaches –Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP), see Haggar R. et al. 2024.
Inference of a minimally biased total mass by applying Machine Learning techniques. This work is based on a strong/old collaboration with the Universidad Autonoma de Madrid (G. Yepes, W. Cui, D. de Andres and collabs) and EURA NOVA, a company working on big data. We started infering the total mass of Planck clusters and reconstructing unbiased 3D total (and gas) mass radial profiles starting from mock Compton-y maps. Now we are about to take few steps forward with the following projects:
Generating observations of galaxy clusters from Dark-Matter-only simulations by Deep Learning, Caro A. et al. 2024 submitted to RASti;
Reconstruction of total mass radial profiles of NIKA2 Twin Samples by ML model, Ferragamo A. et al. on-going analysis;
How much unbiased Planck clusters mass impact cosmological parameters? Wicker R. et al. on-going analysis.
Impact of filaments on cluster properties, such as mass and bias. How much the filamentary structures around clusters are correlated with their properties?
Cluster connectivity, estimated by gas density field, is recostructured in The300 regions, Santoni S. et al. 2024 submitted to A&A.
Mapping gas filaments by SZ and X-ray signals in clusters outskirts, Santoni S. et al. on-going analysis.
Estimate of cluster mass by NIKA2 observations. Among the several projects on-going within the NIKA2 LPSZ team, we are investigating:
the IntraCluster Medium and dynamical state of ACT-CL J0240.0+0116 using a multi-wavelength approach, Paliwal A. et al. 2024 under submission;
the gas pressure profiles in the NIKA2 redshift range (0.5-0.9) reconstructed from NIKA2 Twin Sample mock images at 150 GHz comparing them with catalog profiles and available models in literature, Paliwal A. et al. 2024 on-going analysis.
The Sparsity in clusters, the ratio of spherical halo masses estimated at radii enclosing different overdensities, is useful to infer constraints on cosmological model parameters but it's important to take care of the impact of possible mass bias, such as the hydrostatic one, see Corasaniti S. et al. 2024 under submission.
To investigate the correlation between Sparsity, mass bias and cluster dynamical state., TBD in the planning stage.
For very distant objects, where X-ray spectrometers are starved of photons, it is difficult to obtain an accurate map of the temperature of the gas in the clusters. The combination of SZ and X-ray intensity observations will allow to reconstruct such kind of maps.
We are validating a model to recover mass-weighted temperature maps using simulated SZ and X-ray images of The300 clusters affected by observational impacts of NIKA2 and XMM-Newton instruments. Wicker R. et al. on-going analysis.
The application on NIKA2 LPSZ with XMM-Newton maps is just around the corner ...
Nearly all the projects benefit from the on-going active collaborations, such as The300, NIKA2 and CHEX-MATE and with EURA NOVA.
Page updated
Report abuse