(full page version here)

Research

Here is a link to all my publications registered in ADS : ADS publications

Below I detail some of the projects I have been spending time on during my PhD project and since then...

1. Cosmic web filaments in the CFHTLS

The importance of groups of galaxies and cosmic filaments in pre-processing the galaxy population before they enter galaxy clusters is not yet clear. The work soon to be submitted in Sarron et al. (in prep), presented and reviewed by the jury in my PhD thesis manuscript focused on this aspect. To detect cosmic filaments around AMASCFI clusters in the CFHTLS, we adapted the method proposed by Laigle et al. (2018). The method consists in looking for filaments in the two-dimensional projected galaxy distribution in redshift slices. The filament detection is done with DisPerSE (Sousbie 2011), a ridge finder algorithm.

Stay tuned for the results in the upcoming paper!

Reconstruction of the connected cosmic web filaments around a massive cluster the 100 deg2 EUCLID lightcone from Merson et al (2013). The reconstruction in a 2D slice using photometric redshift is shown in green, while the true 3D skeleton projected in the slice is shown in black. The filaments are detected using DisPerSE (Sousbie 2011). The red circle has a 1.5 cMpc radius.

2. A cluster finding algorithm : AMASCFI

I designed an algorithm called the Adami, MAzure and Sarron Cluster FInder (AMASCFI) to detect clusters of galaxies using large photometric surveys.
The main idea behind AMASCFI is to look for overdensities in overlapping redshift slices of the galaxy catalogue and then merge them into cluster candidates using a friend-of-friend algorithm. A mass estimate of the cluster candidates is derived a posterori using a scaling relation with the cluster richness.
The algorithm was built to handle large data sets such as Euclid or the Large Synoptic Survey Telescope (LSST), particularly using parallel programming on computer clusters.

I successfully applied AMASCFI to the full 154 deg2 of the CFHTLS in Sarron et al. (2018). We detected 7100 cluster candidates up to z = 1.1. The cluster candidate catalogue is publicly available on the VizieR portal.

Details on the AMASCFI algorithm, its selection function and the CFHTLS cluster candidate catalogue can be found in Sarron, F., Martinet, N., Durret, F., et al. 2018, A&A, 613, A67

I will do my best to publicly release AMASCFI (set of fortran routines) through github in the future...

Galaxy cluster detected by AMASCFI at a redshift z = 0.52

3. Cluster galaxy luminosity function in the CFHTLS

To study environmental galaxy quenching, we built the galaxy luminosity function (GLF) of AMASCFI cluster candidates (Sarron et al. 2018) for two galaxy populations: passive and star-forming. The segregation between the two populations was done using the photo-z code LePhare (Ilbert et al. 2006).

The size of our sample allowed us to explore jointly the redshift evolution and cluster mass dependence, thus breaking the degeneracy that exists between the two parameters for the first time.

Redshift evolution of the Schechter fit of early-type / passive (orange, red, and brown) and late-type / star-forming(green, light blue, and deep blue) galaxy luminosity function (GLF). The shaded areas are the 68% confidence interval on the fit. Adapated from Sarron et al. (2018)

The main result of our study is that the number of faint passive galaxies in clusters increases from redshift z = 0.7 to z = 0.1, the effect being slightly stronger in higher mass clusters. These results show that the red sequence is already in place at z > 0.7, and it keeps being enriched between these epochs through efficient environmental quenching (i.e. suppression of star-formation)

All the results can be found in Sarron, F., Martinet, N., Durret, F., et al. 2018, A&A, 613, A67

Contact

Adress:
22 rue des boulets
75011 Paris
FRANCE

Email:

florian.sarron(at)iap(.)fr

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