From Images to Dark Matter: End-to-end Inference of Substructure from Hundreds of Strong Gravitational Lenses
Abstract
Constraining the distribution of small-scale structure in our universe allows us to probe alternatives to the cold dark matter paradigm. Strong gravitational lensing offers a unique window into small dark matter halos (<1010 M ⊙) because these halos impart a gravitational lensing signal even if they do not host luminous galaxies. We create large data sets of strong lensing images with realistic low-mass halos, Hubble Space Telescope (HST) observational effects, and galaxy light from HST's COSMOS field. Using a simulation-based inference pipeline, we train a neural posterior estimator of the subhalo mass function (SHMF) and place constraints on populations of lenses generated using a separate set of galaxy sources. We find that by combining our network with a hierarchical inference framework, we can both reliably infer the SHMF across a variety of configurations and scale efficiently to populations with hundreds of lenses. By conducting precise inference on large and complex simulated data sets, our method lays a foundation for extracting dark matter constraints from the next generation of wide-field optical imaging surveys.
- Publication:
-
The Astrophysical Journal
- Pub Date:
- January 2023
- DOI:
- 10.3847/1538-4357/aca525
- arXiv:
- arXiv:2203.00690
- Bibcode:
- 2023ApJ...942...75W
- Keywords:
-
- Strong gravitational lensing;
- Cosmology;
- Dark matter;
- Convolutional neural networks;
- Hierarchical models;
- Dark matter distribution;
- 1643;
- 343;
- 353;
- 1938;
- 1925;
- 356;
- Astrophysics - Cosmology and Nongalactic Astrophysics;
- Astrophysics - Instrumentation and Methods for Astrophysics
- E-Print:
- Code available at https://github.com/swagnercarena/paltas