The SRG/eROSITA All-Sky Survey X-ray selection function models for the eRASS1 galaxy cluster cosmology

• Published in: Astronomy and astrophysics (Berlin) Vol. 687
• Main Authors: Clerc, N., Comparat, J., Seppi, R., Artis, E., Bahar, Y. E., Balzer, F., Bulbul, E., Dauser, T., Garrel, C., Ghirardini, V., Grandis, S., Kirsch, C., Kluge, M., Liu, A., Pacaud, F., Ramos-Ceja, M. E., Reiprich, T. H., Sanders, J., Wilms, J., Zhang, X.
• Format: Journal Article
• Language: English
• Published: EDP Sciences 01.07.2024
• Subjects: Astrophysics; Physics; methods: statistical; catalogs; surveys; X-rays: galaxies: clusters
• ISSN: 0004-6361; 1432-0756
• DOI: 10.1051/0004-6361/202449447

Aims. Characterising galaxy cluster populations from a catalogue of sources selected in astronomical surveys requires knowledge of sample incompleteness, known as the selection function. The first All-Sky Survey (eRASS1) by eROSITA on board Spectrum Roentgen Gamma (SRG) has enabled the collection of large samples of galaxy clusters detected in the soft X-ray band over the western Galactic hemisphere. The driving goal consists in constraining cosmological parameters, which puts stringent requirements on the accuracy and flexibility of explainable selection function models.Methods. We used a large set of mock observations of the eRASS1 survey and we processed simulated data identically to the real eRASS1 events. We matched detected sources to simulated clusters and we associated detections to intrinsic cluster properties. We trained a series of models to build selection functions depending only on observable surface brightness data. We developed a second series of models relying on global cluster characteristics such as X-ray luminosity, flux, and the expected instrumental count rate as well as on morphological properties. We validated our models using our simulations and we ranked them according to selected performance metrics. We validated the models with datasets of clusters detected in X-rays and via the Sunyaev–Zeldovich effect. We present the complete Bayesian population modelling framework developed for this purpose.Results. Our results reveal the surface brightness characteristics most relevant to cluster selection in the eRASS1 sample, in particular the ambiguous role of central surface brightness at the scale of the instrument resolution. We have produced a series of user-friendly selection function models and demonstrated their validity and their limitations. Our selection function for bright sources reproduces the catalogue matches with external datasets well. We discuss potential inconsistencies in the selection models at a low signal-to-noise revealed by comparison with a deep X-ray sample acquired by eROSITA during its performance verification phase.Conclusions. Detailed modelling of the eRASS1 galaxy cluster selection function is made possible by reformulating selection into a classification problem. Our models are used in the first eRASS1 cosmological analysis and in sample studies of eRASS1 cluster and groups. These models are crucial for science with eROSITA cluster samples and our new methods pave the way for further investigation of faint cluster selection effects.