Reconstructing Cosmic History with Machine Learning: A Study Using CART, MLPR, and SVR
Agripino Sousa-Neto, Maria Aldinez Dantas
arXiv:2505.17205v1 Announce Type: new
Abstract: In this work, we reconstruct cosmic history via supervised learning through three methods: Classification and Regression Trees (CART), Multi-layer Perceptron Regressor (MLPR), and Support Vector Regression (SVR). For this purpose, we use ages of simulated galaxies based on 32 massive, early-time, passively evolving galaxies in the range $0.12 arXiv:2505.17205v1 Announce Type: new
Abstract: In this work, we reconstruct cosmic history via supervised learning through three methods: Classification and Regression Trees (CART), Multi-layer Perceptron Regressor (MLPR), and Support Vector Regression (SVR). For this purpose, we use ages of simulated galaxies based on 32 massive, early-time, passively evolving galaxies in the range $0.12