ALPACA I: Controlling source and PSF systematics in JWST time-delay cosmography with differentiable lens modeling
Abstract
Time-delay cosmography provides an independent probe of the Hubble constant (H0), but exploiting the high-resolution imaging of the James Webb Space Telescope (JWST) requires strong-lens models that capture complex source-galaxy morphologies and the point spread function (PSF) with high fidelity. Flexible source and joint PSF reconstruction are computationally expensive, motivating more rigid parameterizations that can bias the inferred time-delay distance DΔt. We present ALPACA, a semi-automatic, GPU-accelerated, differentiable strong-lens modeling pipeline built on Herculens, Starred, and JAX. ALPACA jointly reconstructs a pixelated correlated-field source, the lens mass, and the PSF within a single likelihood, reducing per-system runtimes from weeks to less than 15 hours. We validate the pipeline on realistic mock JWST/NIRCam observations built from morphologically complex COSMOS galaxy sources. Holding an imperfect PSF (RMSE = 12% from truth) fixed biases DΔt by up to 14%, whereas joint PSF reconstruction reduces this bias to the percent level. We further show that the reconstructed PSF core can be marginalized over jointly with the lens parameters during posterior sampling. Propagating the per-lens posteriors of a ten-lens mock sample through hierarchical inference, we recover the input cosmology without significant bias. ALPACA shows that rigorous control of source and PSF systematics is computationally affordable, supporting reliable percent-level precision in the JWST era.
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BibTeX
@misc{krizic2026alpaca,
title = {{ALPACA I: Controlling source and PSF systematics in JWST time-delay cosmography with differentiable lens modeling}},
author = {Hrvoje Krizic and Martin Millon and Aymeric Galan and Sydney Erickson and Phil Marshall},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.04312},
eprint = {2609.04312},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.04312},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - ALPACA I: Controlling source and PSF systematics in JWST time-delay cosmography with differentiable lens modeling
AU - Hrvoje Krizic
AU - Martin Millon
AU - Aymeric Galan
AU - Sydney Erickson
AU - Phil Marshall
PY - 2026
DA - 2026/09/03
PB - arXiv
DO - 10.48550/arxiv.2609.04312
UR - https://arxiv.org/abs/2609.04312
AB - Time-delay cosmography provides an independent probe of the Hubble constant (H_0), but exploiting the high-resolution imaging of the James Webb Space Telescope (JWST) requires strong-lens models that capture complex source-galaxy morphologies and the point spread function (PSF) with high fidelity. Flexible source and joint PSF reconstruction are computationally expensive, motivating more rigid parameterizations that can bias the inferred time-delay distance D_Δt. We present ALPACA, a semi-automatic, GPU-accelerated, differentiable strong-lens modeling pipeline built on Herculens, Starred, and JAX. ALPACA jointly reconstructs a pixelated correlated-field source, the lens mass, and the PSF within a single likelihood, reducing per-system runtimes from weeks to less than 15 hours. We validate the pipeline on realistic mock JWST/NIRCam observations built from morphologically complex COSMOS galaxy sources. Holding an imperfect PSF (RMSE = 12% from truth) fixed biases D_Δt by up to 14%, whereas joint PSF reconstruction reduces this bias to the percent level. We further show that the reconstructed PSF core can be marginalized over jointly with the lens parameters during posterior sampling. Propagating the per-lens posteriors of a ten-lens mock sample through hierarchical inference, we recover the input cosmology without significant bias. ALPACA shows that rigorous control of source and PSF systematics is computationally affordable, supporting reliable percent-level precision in the JWST era.
ER -