Population Synthesis Toolkit (PST) facilitates reproducible, customizable modeling of galaxy spectral and photometric observables in extragalactic astrophysics. It synthesizes stellar populations using a variety of simple stellar population (SSP) models to compute observable quantities such as integrated spectra, broadband photometry, and equivalent widths from synthetic or observed data. The code combines individual SSPs with user-defined star formation and chemical evolution histories to simulate composite stellar populations and derive their spectral energy distributions across wavelengths. PST includes a flexible interface to handle multiple SSP libraries, assemble composite populations, and calculate synthetic observables for galaxy models, with optional modules for dust extinction and kinematics effects.

FABADA (Fully Adaptive Bayesian Algorithm for Data Analysis) performs non-parametric noise reduction using Bayesian inference. It iteratively evaluates possible smoothed models of the data to estimate the underlying signal that is statistically compatible with the noisy measurements. Iterations stop based on the evidence E and the χ² statistic of the last smooth model, and the expected value of the signal is computed as a weighted average of the smooth models. Though FABADA was written for astronomical data, such as spectra (1D) or images (2D), it can be used as a general noise reduction algorithm for any one- or two-dimensional data; the only requisite of the input data is an estimation of its associated variance.