Polar-transformed strong-lensing images raise CNN subhalo detection fractions by ~15% relative to Cartesian inputs for 10^9–10^9.5 solar-mass subhalos on simulated HST data.
Galaxy Mass Modelling from Multi-Wavelength JWST Strong Lens Analysis: Dark Matter Substructure, Angular Mass Complexity, or Both?
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
We analyze two galaxy-scale strong gravitational lenses, SPT0418-47 and SPT2147-50, using JWST NIRCam imaging across multiple filters. To account for angular complexity in the lens mass distribution, we introduce multipole perturbations with orders $m=1, 3, 4$. Our results show strong evidence for angular mass complexity in SPT2147, with multipole strengths of 0.3-1.7 $\%$ for $m=3, 4$ and 2.4-9.5 $\%$ for $m=1$, while SPT0418 shows no such preference. We also test lens models that include a dark matter substructure, finding a strong preference for a substructure in SPT2147-50 with a Bayes factor (log-evidence change) of $\sim 60$ when multipoles are not included. Including multipoles reduces the Bayes factor to $\sim 11$, still corresponding to a $5\sigma$ detection of a subhalo with an NFW mass of $\log_{10}(M_{200}/M_{\odot}) = 10.87\substack{+0.53\\ -0.71}$. While SPT2147-50 may represent the fourth detection of a dark matter substructure in a strong lens, further analysis is needed to confirm that the signal is not due to systematics associated with the lens mass model.
fields
astro-ph.GA 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Polar coordinate transformations for machine learning based dark matter subhalo detection in strong gravitational lenses
Polar-transformed strong-lensing images raise CNN subhalo detection fractions by ~15% relative to Cartesian inputs for 10^9–10^9.5 solar-mass subhalos on simulated HST data.