ISW and Weak Lensing Likelihood code is the likelihood code that calculates the likelihood of Integrated Sachs Wolfe and Weak Lensing of Cosmic Microwave Background using the WMAP 3year CMB maps with mass tracers such as 2MASS (2-Micron All Sky Survey), SDSS LRG (Sloan Digital Sky Survey Luminous Red Galaxies), SDSS QSOs (Sloan Digital Sky Survey Quasars) and NVSS (NRAO VLA All Sky Survey) radio sources. The details of the analysis (*thus the likelihood code) can be understood by reading the papers ISW paper and Weak lensing paper. The code does brute force theoretical matter power spectrum and calculations with CAMB. See the paper for an introduction, descriptions, and typical results from some pre-WMAP data. The code is designed to be integrated into CosmoMC. For further information concerning the integration, see Code Modification for integration into COSMOMC.

We present a new method for detecting the missing baryons by generating a template for the kinematic Sunyaev-Zel'dovich effect. The template is computed from the product of a reconstructed velocity field with a galaxy field. We provide maps of such templates constructed from SDSS Data Release 7 spectroscopic data (SDSS VAGC sample) along side with their expected two point correlation functions with CMB temperature anisotropies. Codes of generating such coefficients of the two point correlation function are also released to provide users of the gas-momentum map a way to change the parameters such as cosmological parameters, reionization history, ionization parameters, etc.

Mangrove uses Graph Neural Networks to regress baryonic properties directly from full dark matter merger trees to infer galaxy properties. The package includes code for preprocessing the merger tree, and training the model can be done either as single experiments or as a sweep. Mangrove provides loss functions, learning rate schedulers, models, and a script for doing the training on a GPU.

AstroCLIP performs contrastive pre-training between two different kinds of astronomical data modalities (multi-band imaging and optical spectra) to yield a meaningful embedding space which captures physical information about galaxies and is shared between both modalities. The embeddings can be used as the basis for competitive zero- and few-shot learning on a variety of downstream tasks, including similarity search, redshift estimation, galaxy property prediction, and morphology classification.