We present new constraints on the masses of the halos hosting the Milky Way and Andromeda galaxies derived using graph neural networks. Our models, trained on thousands of state-of-the-art hydrodynamic simulations of the CAMELS project, only make use of the positions, velocities and stellar masses of the galaxies belonging to the halos, and are able to perform likelihood-free inference on halo masses while accounting for both cosmological and astrophysical uncertainties. Our constraints are in agreement with estimates from other traditional methods.
翻译:我们利用图形神经网络对主持银河和安朵美达星系的豪华人的质量提出了新的限制。 我们的模型在CAMELS项目数千次最先进的流体动力模拟中接受了培训,只利用了属于豪华星系的星系的位置、速度和星团,并且能够对豪华星体进行无概率的推断,同时考虑到宇宙和天体物理的不确定性。我们的限制与其他传统方法的估计是一致的。