ComPACT: ACT+Planck galaxy cluster catalogue

TL;DR

This paper introduces ComPACT, a new galaxy cluster catalogue derived from a deep learning-based SZ signal detection method applied to ACT and Planck data, significantly expanding known clusters and improving purity.

Contribution

The paper presents a novel deep learning model for SZ signal segmentation and a pipeline for microwave cluster detection, enabling identification of previously unknown galaxy clusters below existing detection thresholds.

Findings

Created a catalogue with 2,962 SZ objects, 977+ new clusters.

Achieved cluster purity of approximately 74-84%.

Improved detection sensitivity over previous catalogs.

Abstract

Galaxy clusters are the most massive gravitationally bound systems consisting of dark matter, hot baryonic gas and stars. They play an important role in observational cosmology and galaxy evolution studies. We develop a deep learning model for segmentation of Sunyaev-Zeldovich (SZ) signal on ACT+Planck intensity maps and construct a pipeline for microwave cluster detection in the ACT footprint. The proposed model allows us to identify previously unknown galaxy clusters, i.e. it is capable of detecting SZ sources below the detection threshold adopted in the published galaxy clusters catalogues (such as ACT DR5 and PSZ2). In this paper, we use the derived SZ signal map to considerably improve a cluster purity in the extended catalogue of Sunyaev-Zeldovich objects from Planck data (SZcat) in the ACT footprint. From SZcat, we create a new microwave galaxy cluster catalogue (ComPACT), which includes 2,962 SZ objects with cluster purity conservatively estimated as $\gtrsim74-84$\%. We categorise objects in the catalogue into 3 categories, based on their cluster reliability. Within the ComPACT catalogue, there are $\gtrsim{}977$ new clusters with respect to the ACT DR5 and PSZ2 catalogues.