photoevolver simulates the atmospheric escape of extrasolar planets and their evolution. The code evolves the gaseous atmosphere of a planet backwards and forwards in time, taking into account its internal structure and cooling rate, atmospheric mass loss processes, and the stellar emission history. photoevolver determines whether a palent's atmosphere survives or ise completely stripped by radiation from its host star.

plaNETic uses a Bayesian neural network-based to model small (masses between 0.5 and 15 M_earth) exoplanets. The code efficiently computes posteriors of a planet's internal structure based on its observed planetary and stellar parameters. It uses a full grid accept-reject sampling algorithm. plaNETic also allows for different choices in priors concerning the expected abundance of water (formation inside vs. outside of iceline) and the planetary Si/Mg/Fe ratios (stellar vs. iron-enriched vs. free).

pycdata imports datasets from various telescopes/instruments in pycheops (ascl:2312.034), thus providing the facility pycheops lacks to model transits, eclipses, phase curves from other telescopes/instruments and the PSF photometry produced by PIPE (ascl:2404.002). pycdata automatically puts resultant data products into the pycheops cache directory so that it can be directly readable from the pycheops command line.

RTModel models and interprets microlensing events. It uses photometric time series collected from ground and/or space telescopes to propose one or more of the following possible models:

- single-lens-single-source microlensing;
- single-lens-binary-source microlensing, with or without xallarap; and/or
- binary-lens-single-source microlensing, including planetary microlensing, parallax and orbital motion.

All models include the finite-size of the source(s). The modeling strategy is based on a grid search in the parameter space for single-lens models, whereas a template library for binary-lens models is used including all possible geometries of the source trajectory with respect to the caustics. In addition to this global search, planets are searched where maximal deviations from a Paczynski model occurs. The RTModel package also including subpackages for creating an immediate visualization of models and the possibility to review each individual fitting process as an animated GIF.

Spright predicts planetary masses, densities, and radial velocity semi-amplitudes given a small planet's radius or planetary radii given the small planet's mass. The package contains two relations: one for small planets orbiting M dwarfs and another for planets orbiting FGK stars. The radial velocity semi-amplitude can be predicted given the planet's radius, orbital period, orbital eccentricity (optional), and the host star mass. Spright offers both a command line script and a set of Python classes. The command line script can directly create publication-quality plots, and the classes offer a full access to the predicted numerical distributions.

SWAMPE models the dynamics of exoplanetary atmospheres; it is an intermediate-complexity, two-dimensional shallow-water general circulation model. Benchmarked for synchronously rotating hot Jupiters and sub-Neptunes, the code is modular and can be modified to model dissimilar space objects, from Brown Dwarfs to terrestrial, potentially habitable exoplanets. SWAMPE can be easily run on a personal laptop.

tglc (TESS-Gaia Light Curve) produces PSF-based TESS full-frame image (FFI) light curves for any sector and any star with custom options. Using Gaia DR3 as priors, the code has forward modeled FFIs with the effective point spread function to remove contamination from nearby stars. The resulting light curves show a photometric precision closely tracking the pre-launch prediction of the noise level: TGLC's photometric precision consistently reaches <2% at 16th TESS magnitude even in crowded fields, demonstrating excellent decontamination and deblending power.

TriArc (reTRIeval ARCturus) uses Bayesian statistics to determine the minimum abundance of an atmospheric species in a given model atmosphere (excluding the species of interest) and spectral noise profile. The code is configured for transmission spectroscopy and is built using the forward modeling capabilities of petitRADTRANS (ascl:2207.014); it also uses PandExo (ascl:1906.016). TriArc has been used to calculate prebiosignature detection thresholds for various potential JWST targets.

Turbospectrum_NLTE updates the spectral synthesis code Turbospectrum (ascl:1205.004) with NLTE capabilities. The code takes a 1D model atmosphere, one or several line lists, and computes the emergent spectrum (flux and/or intensities at various angles), with a prescribed chemical composition. Various parameters can be adjusted, such as microturbulence (vmicro), individual abundances, and isotopic ratios. Turbospectrum_NLTE can also handle the computation for a single chunk of spectrum with a constant wavelength step, or for a number of smaller windows, e.g., around lines of interest. Calculations can also be done for plane-parallel or spherically symmetric models.

VSPEC (Variable Star PhasE Curve) simulates exoplanet observations. It combines NASA’s Planetary Spectrum Generator (PSG) with a custom variable star model. Originally built to simulate the infrared excess of non-transiting planets, VSPEC supports transit, eclipse, phase curve geometries as well as spots, faculae, flares, granulation, and the transit light source effect.

CHIMERA (CaltecH Inverse ModEling and Retrieval Algorithms) retrieves exoplanet atmospheres, and can be used for both transmission and emission geometries with options for both the "free" and "chemically consistent" abundance retrievals. The code uses correlated-K opacities (R=100) with the random-overlap resort-rebin procedure and includes full multiple scattering in emission (both planetary and stellar reflected light) using a two stream approximation variant. CHIMERA includes multiple Bayesian samplers, including PyMultiNest (ascl:1606.005) and dynesty (ascl:1809.013).

DYNAMITE (DYNAmical Multi-planet Injection TEster) predicts the presence of unseen planets in multi-planet systems via population statistics. The code uses the specific (yet often incomplete) data on the orbital and physical parameters for the planets in any given system's architecture and combines it with detailed statistical population models and a dynamical stability criterion to predict the likelihood for the parameters of one additional planet in the system. DYNAMITE's predictions are given in the form of observable values (transit depth measurements, RV semi-amplitudes, or direct imaging separation and contrast), which can be tested by follow-up observations.

TROPF (Tidal Response Of Planetary Fluids) enables efficient terrestrial fluid tidal studies across a wide range of parameter space. The software includes several different solutions to the governing equations in classical tidal theory, and can calculate millions of such solutions on several-minute-long timescales. Written in MATLAB/Octave, TROPF can be ported to Python and other languages, as the instructions for building the operator matrices are described in detail and the coding of core TROPF routines adheres to generic sparse matrix operations and avoids functions specific to MATLAB.

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.

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.

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.

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.

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.

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.

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.

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.

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.