Work

Estimating Cluster Masses: A Comparative Study between Machine Learning and Maximum Likelihood

Year: 2025

Type: article

Abstract: We compare an autoencoder convolutional neural network (AE-CNN) with conventional maximum-likelihood estimation (MLE) for inferring cluster virial masses, M v , directly from the galaxy distribution around clusters, without identifying members or interlopers. The AE-CNN is trained on mock galaxy catalogues, whereas the MLE assumes that clusters of similar mass share the same phase-space galaxy profile. Conceptually, the MLE returns an unbiased estimate of log M v at fixed true mass, whereas the AE-CNN approximates the posterior mean, so the true log M v is unbiased at fixed estimate. Using MultiDark Planck 2 mock clusters with redshift space number density as input, the AE-CNN attains an rms scatter of 0.10 dex between predicted and true log M v , compared with 0.16 dex for the MLE. With inputs based on mean peculiar velocities, binned in redshift space or observed distance, the AE-CNN achieves scatters of 0.12 and 0.16 dex, respectively, despite strong inhomogeneous Malmquist bias.

Source: The Astrophysical Journal

DOI: 10.3847/1538-4357/ae17c0

Authors: Raeed Mundow, Adi Nusser

Institutions: Technion – Israel Institute of Technology, Asher Space Research Institute

Language: English

Topic: Galaxies: Formation, Evolution, Phenomena

Subfield: Astronomy and Astrophysics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $4,499

Funder: Israel Science Foundation