Haskap Pie: A Fresh Slice of Dark Matter Detection
Haskap Pie is a new halo-finding algorithm that combines several techniques to more accurately detect and track dark matter halos in simulations. It outperforms existing methods by better identifying subhalos, using efficient particle sampling, and tracking halos over time. This makes it a powerful tool for studying galaxy formation and cosmic structure.
Mapping the Milky Way's DNA: Stellar Parameters and Chemical Abundances Unveiled with S-PLUS
The S-PLUS survey analyzed 5 million Milky Way stars, estimating atmospheric parameters and chemical abundances using machine learning on multi-band photometric data. Neural networks outperformed random forests in accuracy, revealing trends like [Mg/Fe] bimodality and robustly mapping stellar properties. This cost-effective, scalable approach complements spectroscopy, offering new insights into Galactic evolution and paving the way for broader stellar population studies.