Soumadeep Maiti

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,