picasso is a model that allows making predictions for the thermodynamic properties of the gas in massive dark matter halos from gravity-only cosmological simulations. It combines an analytical model of gas properties as a function of gravitational potential with a neural network predicting the parameters of said model. It is released here as a Python package, combining an implementation of the gas model based on JAX and Flax, and models that have been pre-trained to reproduce gas properties from hydrodynamic simulations.

RFIClean excises periodic RFI (broadband as well as narrow-band) in the Fourier domain, and then mitigates narrow-band spectral line RFI as well as broadband bursty time-domain RFI using robust statistics. Primarily designed to efficiently search and mitigate periodic RFI from GMRT time-domain data, RFIClean has evolved to mitigate any spiky (in time or frequency) RFI as well, and from any SIGPROC filterbank format data file. RFIClean uses several modules from SIGPROC (ascl:1107.016) to handle the filterbank format I/O.

PDQ predicts the positions on the sky of high-redshift quasars that should provide photons that are both acausal and uncorrelated. The predicted signal-to-noise ratios are calculated at framerate sufficient for random-number generation input to a loophole-free Bell test, and are calibrated against a public archival dataset of four pairs of highly-separated bright stars observed simultaneously (and serendipitously) at 17 Hz with that same instrumentation in 2019 to 2021.

AstroPT trains astronomical large observation models using imagery data. The code follows a similar saturating log-log scaling law to textual models and the models' performances on downstream tasks as measured by linear probing improves with model size up to the model parameter saturation point. Other modalities can be folded into the AstroPT model, and use of a causally trained autoregressive transformer model enables integration with the wider deep learning FOSS community.

MultiREx generates synthetic transmission spectra of exoplanets. This tool extends the functionalities of the TauREx (ascl:2209.015) framework, enabling the mass production of spectra and observations with added noise. Though the package was originally conceived to train machine learning models in the identification of biosignatures in noisy spectra, it can also be used for other purposes.

GalClass facilitates visual morphological classifications of large samples of galaxies taking advantage of multi-wavelength imaging and ancillary information. It offers a versatile Graphic User Interface (GUI), which adapts to the provided classification scheme. It displays a series of pre-prepared PDF files for classification, grouping by galaxy and filter, while also listing relevant metadata and displaying a color image of each source. GalClass enables easy navigation through the sample and continuously outputs classification results in a JSON file. Finally, it offers an analysis submodule which combines and processes output files of multiple classifications.

ChromaStarPy computes the vertical structure of a static, plane-parallel, one-dimensional stellar atmosphere in local thermodynamic equilibrium (LTE); it also computes the emergent spectrum incorporating opacity computed with a comprehensive atomic line list from the NIST Atomic Spectra Database. The code provides post-processed data products that are ready to visualize in a Python IDE such as spyder. ChromaStarPy is a port of ChromaStarServer (ascl:1701.009); the code enables users to experiment with and develop a stellar astrophysical modeling code in a graphical IDE, and to compare observational data to ad hoc model output.

ExoInt devolatilizes stellar abundances to produce rocky exoplanetary bulk composition to constrain the modeling of the exoplanet interiors; the code uses Monte Carlo simulations that assume that each element’s abundance (within its uncertainty) follows a Gaussian distribution. ExoInt also contains a module to provide the mineralogy based on the stoichemitric output of mantle and core compositions, core mass fraction, along with the given mass and radius information.

tpfplotter creates a TESS Target Pixel File of a source, overplotting the aperture mask used by the SPOC pipeline and the Gaia catalog to check for possible contaminations within the aperture. The software can create 1-column paper-ready figures, overplotting the Gaia DR2 catalog to the TESS Target Pixel Files, and can create plots for any target observed by TESS. tpfplotter can search by coordinates if the TIC number of the source is not known.

Pytmosph3R computes transmission and emission spectra based on 3D atmospheric simulations, for example, performed with the LMDZ generic global climate model. It produces transmittance maps of the atmospheric limb at all wavelengths that can then be spatially integrated to yield the transmission spectrum. Pytmosph3R can use 3D time-varying atmospheric structures from a GCM as well as simpler, parameterized 1D or 2D structures, and can be used in notebooks or on the command line.