Estimating infall times of galaxies around clusters: Working to achieve accurate results based on observational data

School of Physics and Astronomy, Beijing Normal University, Beijing 100875, PR China

^⋆ Corresponding author: yuheng@bnu.edu.cn

Received: 21 April 2025
Accepted: 22 May 2025

Abstract

Context. The environment plays a crucial role in galaxy evolution, particularly for galaxies infalling into clusters. Accurately estimating the infall times of galaxies from observations can significantly enhance our understanding of the environmental effects on galaxy evolution.

Aims. This paper is aimed at evaluating existing methods for estimating infall times via the R − V diagram. Here, we explore plausible strategies for improving the accuracy in estimating infall times and we discuss the fundamental limitations.

Methods. We utilised a TNG300-1 simulation and constructed the R − V diagram that is directly comparable to the observations. Using the same dataset, we systematically compared four commonly used methods, including the projected radii, caustic profiles, and two discrete methods. A simple linear partition was also considered as a reference.

Results. Each method exhibits distinct characteristics. While the linear partition slightly outperforms other methods, all methods suffer from limited accuracy (≳2.6 Gyr), constrained by the intrinsic dispersion (2.53 Gyr) of infall times in the R − V diagram. Given this limit, we explored two potential approaches that could improve the accuracy: (1) the infall time dispersion is smaller in more dynamically relaxed clusters and (2) employing two estimates of infall times instead of one reduces the dispersion to ≲1.5 Gyr. We further demonstrate that the intrinsic dispersion primarily arises from orbital overlap: galaxies in different orbital phases overlap with each other in the R − V diagram and thus appear indistinguishable.

Conclusions. Orbital overlap fundamentally limits the accuracy of infall time estimation. The linear partition approach could be a simple and robust estimation.