4MOST Selection Functions Pipeline (4SP)
4SP will consist of two parts, an object selection function (4SP-OSF) and a geometric selection function (4SP-GSF). The aim of 4SP-OSF is to provide multi-dimensional probability maps as a function of different parameters defined by the individual surveys. The probability maps will evaluate the expected observational biases that the 4MOST instrumental setup will imprint on the spectroscopic success rate as a function of certain parameters (e.g. signal-to-noise ratio, magnitude, redshift, line-width, temperature, metallicity, etc.). The principal task of 4SP-GSF is to track survey completeness as a function of position on the sky, accounting for observational effects such as fibre placement constraints and observing conditions. Additionally, using the survey simulator we will estimate for each object the probability that it is successfully observed. Combining the 4SP-GSF and 4SP-OSF will allow science users to fully model the 4MOST survey.
4MOST Galactic Pipeline (4GP)
4GP will analyse the high-resolution and low-resolution spectra of stellar sources, ranging from O to M spectral types, including variable stars and white dwarfs. For all of the sources, 4GP will measure heliocentric line-of-sight velocities and stellar parameters as well as chemical abundances; for FGK-type stars, up to ~20 individual chemical abundances will be extracted from the spectra. Whenever possible, non-LTE and 3D hydrodynamic models will be used. The pipeline will be able to derive stellar parameters, including ages, from the spectra considering also astrometric, photometric, and asteroseismic information when available, e.g. with data from the Gaia satellite.
4MOST Extragalactic Pipeline (4XP)
4XP will measure the spectral properties of all extragalactic sources observed with 4MOST. The primary, and mandatory, data products produced by 4XP will be robust spectroscopic redshifts and single-componenent emission/absorption lines measurements. Spectroscopic redshifts will be derived using both template cross-correlation and emission line pattern matching, both using photometric priors and without. In addition, 4XP will also derive higher-order galaxy properties from these measurements, such as star-formation rates, gas-phase metallicities, ionization diagnostic line ratios, etc., and fit continuum stellar population models to the spectra to estimate stellar ages and masses.
4MOST Classification Pipeline (4CP)
4CP will use machine learning methods to classify objects. It will divide sources into broad astrophysical categories, such as stars, galaxies and quasars. 4CP will produce probabilistic outputs. 4CP will not use any synthetic templates, in contrast with the 4GP and 4XP pipelines. Instead, it will be trained only on empirical data. Some peculiar objects may clearly not fit into known categories and become outliers. In addition, some other objects may be hard to classify due to, e.g., low signal, which will put them into the unknown category. Both outliers and unknown objects may be the most interesting for potential discoveries.