the three hundredproject: a machine learning method to infer clusters of galaxy mass radial profiles from mock Sunyaev–Zel’dovich maps

Abstract

We develop a machine learning algorithm to infer the 3D cumulative radial profiles of total and gas mass in galaxy clusters from thermal Sunyaev-Zel’dovich effect maps. We generate around 73,000 mock images along various lines of sight using 2,522 simulated clusters from the The Three Hundred project at redshift z < 0.12 and train a model that combines an autoencoder and a random forest. Without making any prior assumptions about the hydrostatic equilibrium of the clusters, the model is capable of reconstructing the total mass profile as well as the gas mass profile, which is responsible for the SZ effect. We show that the recovered profiles are unbiased with a scatter of about $10\%$, slightly increasing towards the core and the outskirts of the cluster. We selected clusters in the mass range of 1013.5 ≤ M200/( h−1M⊙) ≤ 1015.5, spanning different dynamical states, from relaxed to disturbed halos. We verify that both the accuracy and precision of this method show a slight dependence on the dynamical state, but not on the cluster mass. To further verify the consistency of our model, we fit the inferred total mass profiles with an NFW model and contrast the concentration values with those of the true profiles. We note that the inferred profiles are unbiased for higher concentration values, reproducing a trustworthy mass-concentration relation. The comparison with a widely used mass estimation technique, such as hydrostatic equilibrium, demonstrates that our method recovers the total mass that is not biased by non-thermal motions of the gas.

Intrinsic Mass–Richness Relation of Clusters from THE THREE HUNDRED Hydrodynamic Simulations

“…Recently, there has been a lot of work using machine learning to estimate the mass of individual clusters (e.g., Ntampaka et al 2015Ntampaka et al , 2019Yan et al 2020;Ferragamo et al 2023;de Andres et al 2024). This data-driven approach circumvents the need for dynamical or hydrostatic assumptions, effectively reducing the bias.…”

“…The resimulation process initializes the parent dark matter particles into dark matter, m DM = 1.27 × 10 9 h −1 M e , and gas components, m gas = 2.36 × 10 8 h −1 M e , then conducts three different baryonic codes: GADGET-MUSIC (Sembolini et al 2013), GADGET-X (Rasia et al 2015), and GIZMO-SIMBA (Davé et al 2019;Cui et al 2022). Thanks to THE300ʼs unique setups, for example, the large surrounding area of 15 h −1 Mpc around the central cluster, the filamentary structures connecting to the cluster are studied (Kuchner et al 2020(Kuchner et al , 2021Rost et al 2021Rost et al , 2024; the large sample of clusters permits statistical studies of cluster profiles (Mostoghiu et al 2019;Li et al 2020;Baxter et al 2021), backsplash galaxies (Arthur et al 2019;Haggar et al 2020;Knebe et al 2020), cluster dynamical state (Capalbo et al 2021;De Luca et al 2021;Li et al 2022;Zhang et al 2022), lensing studies (Vega-Ferrero et al 2021;Herbonnet et al 2022;Euclid Collaboration et al 2024) and cluster mass (Li et al 2021;Gianfagna et al 2023); it is further used for machine-learning studies (de Andres et al 2022Andres et al , 2024Ferragamo et al 2023).…”