Astrophysics Source Code Library

Making codes discoverable since 1999

Searching for codes credited to 'Lovell, Christopher C.'

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[ascl:2006.018] Powderday: Dust radiative transfer package

The dust radiative transfer software Powderday interfaces with galaxy formation simulations to produce spectral energy distributions and images. The code uses fsps (ascl:1010.043) and its Python bindings python-fsps for stellar SEDs, Hyperion (ascl:1207.004) for dust radiative transfer, and works with a variety of packages, including Arepo (ascl:1909.010), Changa (ascl:1105.005), Gasoline (ascl:1710.019), and Gizmo (ascl:1410.003); threaded throughout is yt (ascl:1011.022).

[ascl:2012.003] Sengi: Interactive viewer for spectral outputs from stellar population synthesis models

Sengi enables online viewing of the spectral outputs of stellar population synthesis (SPS) codes. Typical SPS codes require significant disk space or computing resources to produce spectra for simple stellar populations with arbitrary parameters, making it difficult to present their results in an interactive, web-friendly format. Sengi uses Non-negative Matrix Factorisation (NMF) and bilinear interpolation to estimate output spectra for arbitrary values of stellar age and metallicity; this reduces the disk requirements and computational expense, allowing Sengi to serve the results in a client-based Javascript application.

[ascl:2403.011] LtU-ILI: Robust machine learning in astro

LtU-ILI (Learning the Universe Implicit Likelihood Inference) performs machine learning parameter inference. Given labeled training data or a stochastic simulator, the LtU-ILI piepline automatically trains state-of-the-art neural networks to learn the data-parameter relationship and produces robust, well-calibrated posterior inference. The package comes with a wide range of customizable complexity, including posterior-, likelihood-, and ratio-estimation methods for ILI, including sequential learning analogs, and various neural density estimators, including mixture density networks, conditional normalizing flows, and ResNet-like ratio classifiers. It offers fully-customizable, exotic embedding networks, including CNNs and Graph Neural Networks, and a unified interface for multiple ILI backends such as sbi, pydelfi, and lampe. LtU-ILI also handles multiple marginal and multivariate posterior coverage metrics, and offers Jupyter and command-line interfaces and a parallelizable configuration framework for efficient hyperparameter tuning and production runs.