# AMICO: optimised detection of galaxy clusters in photometric surveys

## TL;DR

AMICO is a new algorithm that enhances galaxy cluster detection in photometric surveys by using optimal filtering and an iterative approach to improve deblending and reduce false positives.

## Contribution

It introduces an iterative method with membership probability to effectively deblend close structures and improve detection accuracy in photometric galaxy surveys.

## Key findings

- Successfully deblends over 50% of close or aligned structures.
- Maintains a consistent mass-amplitude relation across redshifts 0.3 to 1.
- False detection rate drops sharply above S/N of 5.

## Abstract

We present AMICO (Adaptive Matched Identifier of Clustered Objects), a new algorithm for the detection of galaxy clusters in photometric surveys. AMICO is based on the Optimal Filtering technique, which allows to maximise the signal-to-noise ratio of the clusters. In this work we focus on the new iterative approach to the extraction of cluster candidates from the map produced by the filter. In particular, we provide a definition of membership probability for the galaxies close to any cluster candidate, which allows us to remove its imprint from the map, allowing the detection of smaller structures. As demonstrated in our tests, this method allows the deblending of close-by and aligned structures in more than $50\%$ of the cases for objects at radial distance equal to $0.5 \times R_{200}$ or redshift distance equal to $2 \times \sigma_z$, being $\sigma_z$ the typical uncertainty of photometric redshifts. Running AMICO on mocks derived from N-body simulations and semi-analytical modelling of the galaxy evolution, we obtain a consistent mass-amplitude relation through the redshift range $0.3 < z < 1$, with a logarithmic slope $\sim 0.55$ and a logarithmic scatter $\sim 0.14$. The fraction of false detections is steeply decreasing with S/N, and negligible at S/N > 5.