arXiv subjects

Pierre Galois

Publications and source records attributed to Pierre Galois.

2 recordsLinked to original sources

arXiv2026

Galaxy clusters in the VIDEO fields: detection and characterisation in the context of MOONRISE

We analyse the cluster content of the $\sim 4.5 \text{ deg}^{2}$ XMM-LSS and CDFS VIDEO fields which are expected to be partially covered by the upcoming MOONRISE survey. Using AMICO and WaZP photometric redshift-based cluster finders, we construct a sample of $519$ cluster candidates detected by both finders in the redshift range $z = 0.1-3$, including $74$ detections at $z > 1.5$. For all detections, we identify the Brightest Central Galaxy (BCG) and compute a list of probabilistic cluster memberships. Our photometric redshift measurements of the clusters agree well with spectroscopic redshifts from the literature, when available. From ancillary spectroscopic data, we assign $z_\text{spec}$ measurements to $116$ cluster candidates based on their spectroscopic members and to $204$ based on their likely BCGs. We also show that candidates containing Radio-Loud members are efficiently recovered using the prior-based cluster finder PPM. We perform a preliminary analysis of the galaxy content of these candidates, focusing on the Red-Sequence components of their apparent Colour-Magnitude Diagram. By comparing with models of galaxy evolution, we show that this population is consistent with a model of passive evolution with a formation at high redshift, and is already in place at $z = 1.5-2.0$. Finally, our cluster sample is used to evaluate how these clusters would be detected and characterised, according to various MOONRISE strategies. We show that cluster spectroscopic confirmation and characterisation could be efficiently achieved up to $z\sim1.7$ even with the shallowest survey strategy. This open unprecedented insight into the physical properties of high-redshift galaxy clusters and into galaxy formation in dense environments.

astro-ph.GA↗

arXiv2023

Reionisation time fields reconstruction from 21 cm signal maps

During the Epoch of reionisation, the intergalactic medium is reionised by the UV radiation from the first generation of stars and galaxies. One tracer of the process is the 21 cm line of hydrogen that will be observed by the Square Kilometre Array (SKA) at low frequencies, thus imaging the distribution of ionised and neutral regions and their evolution. To prepare for these upcoming observations, we investigate a deep learning method to predict from 21 cm maps the reionisation time field (treion(r)), i.e. the time at which each location has been reionised. treion(r) encodes the propagation of ionisation fronts in a single field, gives access to times of local reionisation or to the extent of the radiative reach of early sources. Moreover it gives access to the time evolution of ionisation on the plane of sky, when such evolution is usually probed along the line-of-sight direction. We trained a convolutional neural network (CNN) using simulated 21 cm maps and reionisation times fields produced by the simulation code 21cmFAST . We also investigate the performance of the CNN when adding instrumental effects. Globally, we find that without instrumental effects the 21 cm maps can be used to reconstruct the associated reionisation times field in a satisfying manner: the quality of the reconstruction is dependent on the redshift at which the 21 cm observation is being made and in general it is found that small scale (<10cMpc/h) features are smoothed in the reconstructed field, while larger scale features are well recovered. When instrumental effects are included, the scale dependance of reconstruction is even further pronounced, with significant smoothing on small and intermediate scales.

astro-ph.CO↗