Predicting the Physical Properties of Dark Matter Subhalos from Baryonic Parameters Using Machine Learning

Dark matter subhalos play an important role in galaxy formation and evolution. However, accurate prediction of dark matter properties remains a challenge of modern-day astronomy. In recent times, machine learning (ML) tools have shown promising results in solving numerous astrophysical problems. In this paper, we use data from the EAGLE simulations to determine the total mass and the half-mass radius of dark matter subhalos using structural properties of gas, star, black hole, and photometric features using gradient boosted decision trees (GBDT) and dense neural network. GBDT does not require data preprocessing, and results in better performance compared to the neural network. According to GBDT, the most important feature for subhalo radius and mass estimation is gas radius and black hole mass respectively. The all-features combined approach results in the highest test accuracy — Pearson’s correlation coefficient = 0.947 and 0.981, coefficient of determination = 0.898 and 0.962, normalized median absolute deviation = 0.111 and 0.114 for radius and mass respectively. We evaluate our model for masses and redshifts beyond its training range and find that GBDT demonstrates significantly better extrapolation capabilities than the neural network. We also test our model on simulations with different resolutions, and find that the discrepancies lie within 10% if the resolution is changed. This novel study incorporates the structural parameters of gas and black hole to determine the dark matter properties using a ML-based approach. The promising results of this study prove that ML tools can improve our current understanding of dark matter, and answer some of the basic cosmological questions.