# Dark Matter Halo

Galaxy rotation curves have long favored cored dark-matter halos over the steeply rising cusps predicted by cold dark matter simulations. Fitting five SPARC galaxies with Bayesian inference while modeling correlated errors between neighboring radii largely closes that gap. In DDO 154 the reduced chi-square moves from 2.40 versus 9.41 to 0.94 versus 1.04, leaving one-dimensional rotation curves unable to separate the two halo shapes.

Galaxy rotation curves have long favored cored dark-matter halos over the steeply rising cusps predicted by cold dark matter simulations. Fitting five SPARC galaxies with Bayesian inference while modeling correlated errors between neighboring radii largely closes that gap. In DDO 154 the reduced chi-square moves from 2.40 versus 9.41 to 0.94 versus 1.04, leaving one-dimensional rotation curves unable to separate the two halo shapes.

The work presents and validates a hybrid pipeline that first uses a volumetric convolutional neural network (a D3M-based VNet taking six channels of displacement and velocity) to classify each simulation particle as a halo or non-halo member, then applies a highly optimised and parallelised Friends-of-Friends algorithm to group the predicted halo members into distinct dark matter haloes; trained on GADGET-4 simulations labelled by ROCKSTAR, it reaches over 98% across all primary metrics for particle classification at the highest-resolution L100-N1283 configuration, yields catalogues with purity generally above 95% and completeness stable at about 93% above 5×10¹¹ M⊙, reproduces the halo mass function to within 5% of the reference while faithfully reconstructing internal density profiles,

The work presents and validates a hybrid pipeline that first uses a volumetric convolutional neural network (a D3M-based VNet taking six channels of displacement and velocity) to classify each simulation particle as a halo or non-halo member, then applies a highly optimised and parallelised Friends-of-Friends algorithm to group the predicted halo members into distinct dark matter haloes; trained on GADGET-4 simulations labelled by ROCKSTAR, it reaches over 98% across all primary metrics for particle classification at the highest-resolution L100-N1283 configuration, yields catalogues with purity generally above 95% and completeness stable at about 93% above 5×10¹¹ M⊙, reproduces the halo mass function to within 5% of the reference while faithfully reconstructing internal density profiles,