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Searching for codes credited to 'Moya, A.'

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Found 3 codes.

[ascl:2601.007] ATAT: Astronomical Transformer for time series And Tabular data
ATAT (Astronomical Transformer for time series And Tabular data) implements Transformer-based models for classifying astronomical sources from light-curve time series and tabular metadata. It provides scripts to download, process, and partition survey data, extracting and normalizing light curves and assembling metadata into training, validation, and test sets. Specialized layers for time modulation and multi-head attention encode light curves and tabular features, while training drivers construct models, run optimization and validation loops, and save trained configurations. ATAT also provides utilities to compute classification metrics at multiple observation durations and generate visualizations such as performance curves and confusion matrices.​
[ascl:2506.004] TESS-cont: TESS contamination tool
TESS-cont quantifies the flux fraction coming from nearby stars in the TESS photometric aperture of any observed target. The package identifies the main contaminant Gaia DR2/DR3 sources, quantifies their individual and total flux contributions to the aperture, and determines whether any of these stars could be the origin of the observed transit and variability signals. Written in Python, TESS-cont is based on building the pixel response functions (PRFs) of nearby Gaia sources and computing their flux distributions across the TESS Target Pixel Files (TPFs) or Full Frame Images (FFIs).
[ascl:2504.002] PBjam: Automating asteroseismology of solar-like oscillators
PBjam analyzes the oscillation spectra of solar-like oscillators. The code performs two main tasks: identifying a set of modes of interest in a spectrum of oscillations, and accurately modeling those modes to measure their frequencies. Mode identification relies on a large set of previous observations of the model parameters, which are then used to construct a prior distribution to inform the sampling. PJjam models the modes using a nested sampling or MCMC algorithm, where Lorentzian profiles are fit to each of the identified modes.