MK spectral types of MaStar stars using NutMaat

We present NutMaat, an open-source Python-based package inspired by MKCLASS, designed to automate MK classification while addressing scalability and usability limitations. It employs modern computational tools for batch processing and offers a modular architecture that enables efficient, platform-independent analysis of large spectral data sets.

It also includes modules for detecting classical chemically peculiar stars, such as Am, Ap, and {lambda} Boo types, using internal consistency checks between different line diagnostics. Tested on the CFLIB and MILES libraries, NutMaat achieved spectral and luminosity classification accuracies comparable to MKCLASS, with minimal systematic offsets and a robust performance down to a signal-to-noise ratio <=10. NutMaat successfully identified chemically peculiar stars, tested on LAMOST DR7 ACV variables, and processed the SDSS-IV MaStar library-which lacks native MK classifications-to produce a stellar catalog, demonstrating survey readiness.