Cosmic web filaments in the CFHTLS

In the hierarchical model struture formation in the Universe, matter aggregates in larger and larger structures over cosmic time. In particular, galaxy clusters, the most dense and massive of these large-scale structures happen to be located at the intersection of large cosmic filaments. Matter fall along these filaments into the gravitotional potential well of the cluster, either as isolated galaxies, or as galaxies already in smaller galaxy groups.

Yet, living in groups or cosmic filaments affect galaxy properties. In particular, their star-formation process can be stopped (quenching). Hence, it is beleived that the part of the large fraction of quenched galaxies observed in clusters in the present day Universe could have quenched before they enter these clusters. This phenomenon is referred to as pre-processing. In 2018, when this work started, the relative importance of this pre-processing was yet unclear.

As part of my PhD work, in (Sarron et al., 2019) we focused on this aspect. We did so using a large photometric survey (CFHTLS), with the idea that this could give us interesting insights thanks to the cosmic volume covered by the survey, and despite the inherent noisiness of galaxy distance measures in such data. This choice was made building upon the great pioneer work of Laigle et al., 2018 that showed the effect of cosmic filaments could be recovered in COSMOS photometric data.

We adapted the method to detect cosmic filaments around AMASCFI clusters in the CFHTLS survey, for which the photometric redshift are less precise than in COSMOS. The method consists in looking for filaments in the two-dimensional projected galaxy distribution in photometric redshift slices. The filament detection is done with DisPerSE (Sousbie, 2011), a ridge finder algorithm.
We showed that cosmic filaments effects could be statistically recovered for regime of multiband photometric surveys where the uncertainty of the photometric redshift is \(\sigma_{\rm NMAD} \leq 0.03 \times (1 + z)\).

We then studied the fraction of quenched galaxies in the detected candidate cosmic filaments in the vicinity of galaxy clusters. We showed that in our CFHTLS data:

the fraction of quenched galaxies around clusters is higher in filaments than in random regions up to \(d \sim 5 {\rm cMpc}\),
this fraction decreases as one gets further away for the clusters, both in filaments and random regions.

This work lead us to start a project inside the Euclid consortium together with Clotilde Laigle and Ulrike Kuchner in 2020 on the role of the cosmic web role in driving galaxy evolution, and what can be inferred about it with Euclid data. A project on which later joined Pascale Jablonka, Nicola Malavasi, Michael Balogh, Katarina Kraljic among others.
This team work has lead to many interesting methodological developments, some of them will be featured in publications of the Euclid consortium in 2024, and we now have a better view of what will be reachable with Euclid data in the years to come.

References

2019

A&A
Pre-processing of galaxies in cosmic filaments around AMASCFI clusters in the CFHTLS

Florian Sarron, C. Adami , F. Durret , and 1 more author

Astronomy and Astrophysics, Dec 2019

Abs arXiv Bib HTML PDF

Context. Galaxy clusters and groups are thought to accrete material along the preferred direction of cosmic filaments. These structures have proven difficult to detect because their contrast is low, however, and only a few studies have focused on cluster infall regions. \Aims: We detect cosmic filaments around galaxy clusters using photometric redshifts in the range 0.15 < z < 0.7. We characterise galaxy populations in these structures to study the influence of pre-processing by cosmic filaments and galaxy groups on star formation quenching. \Methods: We detected cosmic filaments in the Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) T0007 data, focusing on regions around clusters of the AMASCFI CFHTLS cluster sample. The filaments were reconstructed with the discrete persistent structure extractor (DISPERSE) algorithm in photometric redshift slices. We show that this reconstruction is reliable for a CFHTLS-like survey at 0.15 < z < 0.7 using a mock galaxy catalogue. We split our galaxy catalogue into two populations (passive and star forming) using the LePhare spectral energy density fitting algorithm and worked with two redshift bins (0.15 < z \ensuremath≤ 0.4 and 0.4 < z < 0.7). \Results: We showed that the AMASCFI cluster connectivity (i.e. the number of filaments that is connected to a cluster) increases with cluster mass M_200. Filament galaxies outside R_200 are found to be closer to clusters at low redshift, regardless of the galaxy type. Passive galaxies in filaments are closer to clusters than star-forming galaxies in the low redshift bin alone. The passive fraction of galaxies decreases with increasing clustercentric distance up to d \ensuremath∼ 5 cMpc. Galaxy groups and clusters that are not located at nodes of our reconstruction are mainly found inside cosmic filaments. \Conclusions: These results give clues for pre-processing in cosmic filaments that could be due to smaller galaxy groups. This trend could be further explored by applying this method to larger photometric surveys such as the Hyper Suprime-Cam Subaru Strategic Program (HSC-SPP) or Euclid.
@article{Sarron2019, author = {{Sarron}, Florian and {Adami}, C. and {Durret}, F. and {Laigle}, C.}, title = {{Pre-processing of galaxies in cosmic filaments around AMASCFI clusters in the CFHTLS}}, journal = {Astronomy and Astrophysics}, keywords = {galaxies: clusters: general, large-scale structure of Universe, galaxies: evolution, galaxies: statistics, methods: data analysis, Astrophysics - Astrophysics of Galaxies, Astrophysics - Cosmology and Nongalactic Astrophysics}, year = {2019}, month = dec, volume = {632}, eid = {A49}, pages = {A49}, doi = {10.1051/0004-6361/201935394}, archiveprefix = {arXiv}, primaryclass = {astro-ph.GA}, adsurl = {https://ui.adsabs.harvard.edu/abs/2019A&A...632A..49S}, adsnote = {Provided by the SAO/NASA Astrophysics Data System}, }