# Deep-CEE I: Fishing for Galaxy Clusters with Deep Neural Nets

## TL;DR

Deep-CEE is a novel deep learning approach that directly analyzes wide-field colour images to identify galaxy clusters, bypassing traditional catalogues and enabling exploration of the Universe's structure.

## Contribution

This paper introduces a deep learning method for galaxy cluster detection directly from imaging data, avoiding reliance on simulated data and traditional catalogues.

## Key findings

- Successfully localized and classified galaxy clusters in SDSS images.
- Demonstrated potential for application to future surveys like LSST and Euclid.
- Provides a realistic training approach using existing labelled data.

## Abstract

We introduce Deep-CEE (Deep Learning for Galaxy Cluster Extraction and Evaluation), a proof of concept for a novel deep learning technique, applied directly to wide-field colour imaging to search for galaxy clusters, without the need for photometric catalogues. This technique is complementary to traditional methods and could also be used in combination with them to confirm galaxy cluster candidates. We use a state-of-the-art probabilistic algorithm, adapted to localize and classify galaxy clusters from other astronomical objects in SDSS imaging. As there is an abundance of labelled data for galaxy clusters from previous classifications in publicly available catalogues, we do not need to rely on simulated data. This means we keep our training data as realistic as possible, which is advantageous when training a deep learning algorithm. Ultimately, we will apply our model to surveys such as LSST and Euclid to probe wider and deeper into unexplored regions of the Universe. This will produce large samples of both high redshift and low mass clusters, which can be utilized to constrain both environment-driven galaxy evolution and cosmology.