Semi-automatic analysis for echelle spectra of stars.

The major parts are:
(1) full spectrum fit with a neural network emulator to estimate stellar parameters
(2) automatic continuum normalization with theoretical masks
(3) automatic equivalent width fits with theoretical masks
(4) ATLAS model atmosphere interpolation and equivalent width abundance determination using MOOG
(5) spectrum synthesis fitting using MOOG
(6) automatic abundance uncertainty analysis with error propagation and summary tables

LESSPayne can be run in a completely automatic mode, which is best used as a quick check of outputs during observing or an initial inspection. However, science-quality results still require a classic line-by-line analysis, where the quality of all fits is inspected by the user using the Spectroscopy Made Harder (smhr) graphical user interface or other automatic output plots. LESSPayne should be viewed as providing a high-quality initialization for an smhr file that reduces the time for a standard analysis.

If using LESSPayne, please cite Casey (2014) (https://ui.adsabs.harvard.edu/abs/2014PhDT.......394C/abstract), Ting et al. (2019) (https://ui.adsabs.harvard.edu/abs/2019ApJ...879...69T/abstract), and Ji et al. (2020) (https://ui.adsabs.harvard.edu/abs/2020AJ....160..181/abstract) in addition to this ASCL entry.

Additionally as always, please cite the model atmospheres used (default is ATLAS, https://ui.adsabs.harvard.edu/abs/2003IAUS..210P.A20C/abstract), radiative transfer code (default is MOOG including scattering, https://ui.adsabs.harvard.edu/abs/1973PhDT.......180S/abstract, https://ui.adsabs.harvard.edu/abs/2011AJ....141..175S/abstract, https://ui.adsabs.harvard.edu/abs/2012ascl.soft02009S/abstract), and atomic data (if using any built into this package, see references in https://ascl.net/2104.027 and https://ui.adsabs.harvard.edu/abs/2021RNAAS...5...92P/abstract).