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pyMCZ calculates metallicity according to a number of strong line metallicity diagnostics from spectroscopy line measurements and obtains uncertainties from the line flux errors in a Monte Carlo framework. Given line flux measurements and their uncertainties, pyMCZ produces synthetic distributions for the oxygen abundance in up to 13 metallicity scales simultaneously, as well as for E(B-V), and estimates their median values and their 68% confidence regions. The code can output the full MC distributions and their kernel density estimates.
The rise of synoptic sky surveys has ushered in an era of big data in time-domain astronomy, making data science and machine
learning essential tools for studying celestial objects. Tree-based (e.g. Random Forests) and deep learning models represent the
current standard in the field. We explore the use of different distance metrics to aid in the classification of objects. For this,
we developed a new distance metric based classifier called DistClassiPy. The direct use of distance metrics is an approach
that has not been explored in time-domain astronomy, but distance-based methods can aid in increasing the interpretability of the
classification result and decrease the computational costs. In particular, we classify light curves of variable stars by comparing the
distances between objects of different classes. Using 18 distance metrics applied to a catalog of 6,000 variable stars in 10 classes,
we demonstrate classification and dimensionality reduction. We show that this classifier meets state-of-the-art performance but
has lower computational requirements and improved interpretability. We have made DistClassiPy open-source and accessible
at https://pypi.org/project/distclassipy/ with the goal of broadening its applications to other classification scenarios
within and beyond astronomy.