Periodic Reporting for period 1 - MEMORY (The cosmic web from eROSITA and galaxy surveys synergy)
Okres sprawozdawczy: 2023-09-01 do 2026-01-31
Galaxies like the Milky Way do not exist isolated in space, but occupy a Large Scale Structure (LSS) in the Universe. The LSS is organized in a network of structures (called the Cosmic Web, CW) whose main components are galaxy clusters (at the nodes), connected by filaments, which frame walls. Walls surround vast empty regions called voids. The structures of the CW are made of dark matter, galaxies, and gas. In galaxy clusters in particular, gas is hot and emits in the X-rays, which makes them relatively easy to detect as bright, diffuse X-ray sources. Matter (including gas and galaxies) is accreted by galaxy clusters through the filaments connected to them, which act as highways for matter flow onto structures. As such, studying galaxy clusters connections to the filaments is a very important topic to understand their properties and evolution.
In recent years, a few works have focused on the study of the so-called connectivity-mass relation, i.e. the study of the number of filaments connected to clusters as a function of their mass. However, these studies could only rely on limited samples of clusters within restricted redshift and mass ranges.
The objectives of my work were two: 1) to measure the connectivity-mass relation for an extensive sample of X-ray detected clusters, increasing the mass and redshift range where this relation is analyzed and 2) to study the very low mass regime of groups and the high-mass regime of super-clusters, placing these structures in the context of the CW.
In recent years, a few works have focused on the study of the so-called connectivity-mass relation, i.e. the study of the number of filaments connected to clusters as a function of their mass. However, these studies could only rely on limited samples of clusters within restricted redshift and mass ranges.
The objectives of my work were two: 1) to measure the connectivity-mass relation for an extensive sample of X-ray detected clusters, increasing the mass and redshift range where this relation is analyzed and 2) to study the very low mass regime of groups and the high-mass regime of super-clusters, placing these structures in the context of the CW.
Throughout the duration of this project I have worked to achieve two results: 1) the detection of the CW from galaxy surveys and 2) the measurement of the connectivity-mass relation for eROSITA clusters.
Using the DisPerSE algorithm, I have achieved a detection of the filaments of the CW in both the GAMA survey (using spectroscopic redshifts) and in the DESI Legacy Imaging Surveys (DESI LS). In the case of the DESI LS I have performed a Montecarlo sampling of galaxy photometric redshifts to take their higher uncertainty into account. The detection of the CW in both surveys is described in Malavasi et al. 2026 (under review at A&A).
I have also compared the skeleton detection in both surveys, performing a quantitative measurement of the similarity of filament sets derived with spectroscopic and photometric redshifts. This work, described in the same publication, makes a large use of several techniques developed in the context of the Euclid survey. Throughout this project I have worked to achieve a publication (Euclid Collaboration: Malavasi et al. 2025, under review at A&A) where I compare sets of filaments detected in the mock catalogues reproducing the Euclid Wide Survey. In this preparatory work, I have introduced the measurement of distances and angles between filaments as a way to estimate how similar two filament sets are. In the context of the Euclid survey this was done to measure how similar a skeleton extracted using photometric redshift was from a skeleton extracted using galaxy true redshifts.
I have used what learned in Euclid to compare filaments in two real datasets, namely the GAMA survey and the DESI LS. This is one of the first time that two filament sets extracted from different galaxy samples overlapping in the same volume of the Universe are thoroughly compared. We were able to conclude that the skeleton extracted in the DESI LS can be considered as a smoothed version of the skeleton extracted in GAMA.
With this newly introduced filament sample I was then able to provide a first measurement of the connectivity-mass relation for the sample of clusters extracted from the first year of data of eROSITA (eRASS1). I have fully characterized this relation and provided one of the most comprehensive measurements of its dependence on redshift. These results are contained in a publication to be submitted soon.
I have also used existing data sets to study superclusters: I have performed a match between the catalog of superclusters in eROSITA (detected in the X-rays), a catalog of superclusters detected in the SDSS by means of the galaxy distribution and filaments detected in the SDSS via DisPerSE. I have identified superclusters as dense nodes of the CW, often corresponding to several filaments and substructures. All in all, the match between these catalogs is surprisingly good, given the different detection methods.
Using the DisPerSE algorithm, I have achieved a detection of the filaments of the CW in both the GAMA survey (using spectroscopic redshifts) and in the DESI Legacy Imaging Surveys (DESI LS). In the case of the DESI LS I have performed a Montecarlo sampling of galaxy photometric redshifts to take their higher uncertainty into account. The detection of the CW in both surveys is described in Malavasi et al. 2026 (under review at A&A).
I have also compared the skeleton detection in both surveys, performing a quantitative measurement of the similarity of filament sets derived with spectroscopic and photometric redshifts. This work, described in the same publication, makes a large use of several techniques developed in the context of the Euclid survey. Throughout this project I have worked to achieve a publication (Euclid Collaboration: Malavasi et al. 2025, under review at A&A) where I compare sets of filaments detected in the mock catalogues reproducing the Euclid Wide Survey. In this preparatory work, I have introduced the measurement of distances and angles between filaments as a way to estimate how similar two filament sets are. In the context of the Euclid survey this was done to measure how similar a skeleton extracted using photometric redshift was from a skeleton extracted using galaxy true redshifts.
I have used what learned in Euclid to compare filaments in two real datasets, namely the GAMA survey and the DESI LS. This is one of the first time that two filament sets extracted from different galaxy samples overlapping in the same volume of the Universe are thoroughly compared. We were able to conclude that the skeleton extracted in the DESI LS can be considered as a smoothed version of the skeleton extracted in GAMA.
With this newly introduced filament sample I was then able to provide a first measurement of the connectivity-mass relation for the sample of clusters extracted from the first year of data of eROSITA (eRASS1). I have fully characterized this relation and provided one of the most comprehensive measurements of its dependence on redshift. These results are contained in a publication to be submitted soon.
I have also used existing data sets to study superclusters: I have performed a match between the catalog of superclusters in eROSITA (detected in the X-rays), a catalog of superclusters detected in the SDSS by means of the galaxy distribution and filaments detected in the SDSS via DisPerSE. I have identified superclusters as dense nodes of the CW, often corresponding to several filaments and substructures. All in all, the match between these catalogs is surprisingly good, given the different detection methods.
The results of this project advance the state of the art in that this is the first time that the connectivity-mass relation is measured for a sample of galaxy clusters, securely detected via their X-ray emission, of this size. We are able to measure the connectivity-mass relation over half of the sky. It is also the first time that filament extraction is performed on such a large area.
The connectivity-mass relation has been fully characterized, not only in terms of its redshift evolution but also in terms of the systematic effects that the filament sample, cluster sample, and redshift precision for the filament extraction have on the relation.
Future developments of this work include the application of what has been learned from eRASS1 directly to the new catalog of clusters produced by eRASS:4 data. The effort to produce this catalog is underway at the Max Planck Institute for Extraterrestrial Physics and I am directly involved in it via the creation of simulations to better understand the selection function of the survey. I also plan to extend the analysis to much lower mass systems, which will be very numerous in eRASS:4.
The connectivity-mass relation has been fully characterized, not only in terms of its redshift evolution but also in terms of the systematic effects that the filament sample, cluster sample, and redshift precision for the filament extraction have on the relation.
Future developments of this work include the application of what has been learned from eRASS1 directly to the new catalog of clusters produced by eRASS:4 data. The effort to produce this catalog is underway at the Max Planck Institute for Extraterrestrial Physics and I am directly involved in it via the creation of simulations to better understand the selection function of the survey. I also plan to extend the analysis to much lower mass systems, which will be very numerous in eRASS:4.