collaborators

30 papers

astro-ph.GA20264 cited

Quantifying the Impact of Incompleteness on Identifying and Interpreting Galaxy Protocluster Populations with the TNG-Cluster Simulation

Devontae C. Baxter, Alison L. Coil, Ethan O. Nadler +7

We use the TNG-Cluster simulation to investigate how stellar mass and star formation rate (SFR) incompleteness affect the identification of density peaks within galaxy protocluster…

astro-ph.GA2026

The host halo masses of AGNs and quasars at with TNG-Cluster, FLAMINGO and other cosmological galaxy simulations

Akanksha Kapahtia, Annalisa Pillepich, Joey Braspenning +5

Most observations and clustering analyses suggest that quasars inhabit a narrow range of dark-matter halo masses ( M) across cosmic time (). Recen…

astro-ph.GA2026

Bulk vs. turbulent motions at the centres of galaxy clusters: AGN-driven turbulence according to TNG-Cluster

Bipradeep Saha, Annalisa Pillepich, Joey Braspenning +3

The highly dynamic intracluster medium (ICM) influences cluster thermodynamic evolution and probes key physical processes. Quantifying the non-thermal motions is therefore essentia…

astro-ph.GA2026

Numerical effects on the stripping of dark matter and stars in IllustrisTNG galaxy groups and clusters

Mark R. Lovell, Annalisa Pillepich, Christoph Engler +4

The stellar haloes and intra-cluster light around galaxies are crucial test beds for dark matter (DM) physics and galaxy formation models. We consider the role that the numerical r…

astro-ph.GA2026

The temperature and metallicity distributions of the ICM: insights with TNG-Cluster for XRISM-like observations

Dimitris Chatzigiannakis, Annalisa Pillepich, Aurora Simionescu +2

The new era of high-resolution X-ray spectroscopy will significantly improve our understanding of the intra-cluster medium (ICM) by providing precise constraints on its underlying…

astro-ph.GA2026

ERGO-ML: The assembly histories of HSC galaxy images via invertible neural networks, contrastive learning, and cosmological simulations

Lukas Eisert, Connor Bottrell, Annalisa Pillepich +4

In this paper of ERGO-ML (Extracting Reality from Galaxy Observables with Machine Learning), we develop a model that infers the merger/assembly histories of galaxies directly from…