The Blooming Tree (BT) algorithm, based on the hierarchical clustering method, is designed to identify clusters, groups, and substructures from galaxy redshift surveys.

ExoSim 2 is the next generation of the Exoplanet Observation Simulator (ExoSim) tailored for the spectro-photometric observations of transiting exoplanets from space, ground, and sub-orbital platforms. The code execution in ExoSim 2 follows a three-step workflow: the creation of focal planes, the production of Sub-Exposure blocks, and the generation of non-destructive reads (NDRs). ExoSim 2 has demonstrated consistency in estimating photon conversion efficiency, saturation time, and signal generation. The simulator has also been validated independently for instantaneous read-out and jitter simulation, and for astronomical signal representation

luas builds Gaussian processes (GPs) primarily for two-dimensional data sets. It uses different optimizations to make the application of GPs to 2D data sets possible within a reasonable timeframe. The code is implemented using Jax (ascl:2111.002), which helps calculate derivatives of the log-likelihood as well as permitting the code to be easily run on either CPU or GPU. luas can be used with popular inference frameworks such as NumPyro and PyMC. The package makes it easier to account for systematics correlated across two dimensions in data sets, in addition to being helpful for any other applications (e.g., interpolation).

kpic_pipeline reduces data taken with the Keck Planet Imager and Characterizer (KPIC). Written in Python, the code processes high resolution spectroscopy data taken with KPIC to study exoplanet atmospheres; it processes and calibrate the data to enable spectroscopic model fitting. kpic_pipeline can reduce the observed data into 1D spectra for one given science target or can be used to reduce the full nightly data.

IsoFATE (Isotopic Fractionation via ATmospheric Escape) models mass fractionation resulting from diffusive separation in escaping planetary atmospheres and numerically computes atmospheric species abundance over time. The model is tuned to sub-Neptune sized planets with rocky cores of Earth-like bulk composition and primordial H/He atmospheres. F, G, K, and M type stellar fluxes are readily implemented. IsoFATE has two versions, the first of which simulates a ternary mixture of H, He, and D (deuterium); the second version is coupled to the magma ocean-atmosphere equilibrium chemistry model Atmodeller.

The IGRINS_transit data reduction pipeline takes high-resolution observations of transiting exoplanets with Gemini-S/IGRINS and produces cross-correlation detections of molecules in the exoplanet's atmosphere. IGRINS_transit removes low signal-to-noise orders, performs a secondary wavelength calibration, and uses a singular value decomposition (SVD) to separate out the signature of the transiting planet from the host star and telluric contamination.

GPS (Genesis Population Synthesis) develops population synthesis models. The code suite uses the Genesis database of planet formation models for small exoplanets (super-Earths and Mini-Neptunes). Although the codebase focuses on the Genesis models, aother models can easily be integrated with GPS. It computes the bulk compositions of the planets and simulates atmospheric loss and evolution to find the final states of the planets that can be observationally verified. GPS also offers tools to process and analyze the data from recent observations of small exoplanets in order to compare them with the models.

Gollum performs spectral visualization and analysis. It offers both a programmatic interface and a visual interface that help users analyze stellar and substellar spectra, with support included for a set of precomputed synthetic spectral model grids.

GEOCLIM.jl, written in Julia, replicates some features of the original GEOCLIM model written in Fortran. It also extends the original weathering equations WHAK and MAC, which ignore direct dependence on pCO2 and include direct pCO2 dependence respectively. The code estimates global silicate weathering rates from gridded climatology. GEOCLIM.jl estimates weathering during periods of Earth history when the continental configuration was radically different, typically more than 100 million years ago, and includes functions to compute, for example, land/ocean fraction, area-weighted average, area-weighted sum, and land mass perimeter, among other values.

Given mass, radius, and equilibrium temperature, ExoMDN can deliver a full posterior distribution of mass fractions and thicknesses of each planetary layer. A machine-learning model for the interior characterization of exoplanets based on Mixture Density Networks (MDN), ExoMDN is trained on a large dataset of more than 5.6 million synthetic planets below 25 Earth masses. These synthetic planets consist of an iron core, a silicate mantle, a water and high-pressure ice layer, and a H/He atmosphere. ExoMDN uses log-ratio transformations to convert the interior structure data into a form that the MDN can easily handle.