Narayan Khadka

Software

Scientific software · Strong-lensing simulations · Open-source contributions

Scientific Software and Community Development

I develop and contribute to open-source scientific software and analysis pipelines for gravitational-lensing research. My software work connects astrophysical modeling and simulations with the practical needs of strong-lens discovery, characterization, and cosmological analysis.

SLSim: Strong-Lensing Simulation Software

I led the development of SLSim, an open-source Python package for simulating realistic strong-gravitational-lens populations and images. The package supports several classes of systems, including galaxy-galaxy lenses, lensed quasars, lensed supernovae, and cluster lenses.

SLSim is designed to generate realistic lens populations and observational data products for developing, training, and validating strong-lens discovery and characterization methods. It provides a flexible simulation framework for studies ranging from lens population forecasts to the preparation of algorithms for large imaging surveys such as the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST).

SLSim is used in the LSST-DESC strong-lens finding challenge to provide simulated populations and images for evaluating lens-finding methods. Its development brings together lens modeling, population distributions, image simulation, and survey-related considerations.

Repository: SLSim on GitHub

Lenstronomy

I have contributed to Lenstronomy , an open-source Python package for strong-gravitational-lens modeling and analysis.

My contributions to the Lenstronomy ecosystem are part of my broader involvement in developing and using community tools for lens modeling, simulation, and scientific inference. These tools are important for interpreting strong-lensing observations and extracting information about galaxy mass distributions and cosmological distances.

LSST-DESC Analysis Pipelines

Within the LSST Dark Energy Science Collaboration (LSST-DESC), I work on software and analysis workflows that support strong-lens discovery and characterization for Rubin-era data.

My work includes developing and validating simulation-based methods, working with survey data products, and supporting reproducible analysis pipelines. These activities help connect simulated lens populations to the data-analysis requirements of large imaging surveys.

I am also involved in the LSST-DESC strong-lens finding challenge, which provides a framework for developing and evaluating methods to identify strong gravitational lenses in large astronomical imaging data sets.

Software and Research Interests