{
  "id": "2203.00690",
  "version": "v1",
  "published": "2022-03-01T19:00:00.000Z",
  "updated": "2022-03-01T19:00:00.000Z",
  "title": "From Images to Dark Matter: End-To-End Inference of Substructure From Hundreds of Strong Gravitational Lenses",
  "authors": [
    "Sebastian Wagner-Carena",
    "Jelle Aalbers",
    "Simon Birrer",
    "Ethan O. Nadler",
    "Elise Darragh-Ford",
    "Philip J. Marshall",
    "Risa H. Wechsler"
  ],
  "comment": "Code available at https://github.com/swagnercarena/paltas",
  "categories": [
    "astro-ph.CO",
    "astro-ph.IM"
  ],
  "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 ($<10^{10} M_\\odot$) because these halos impart a gravitational lensing signal even if they do not host luminous galaxies. We create large datasets 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 datasets, our method lays a foundation for extracting dark matter constraints from the next generation of wide-field optical imaging surveys.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2022-03-01T19:00:00.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "strong gravitational lenses",
      "end-to-end inference",
      "cold dark matter paradigm",
      "small dark matter halos",
      "substructure"
    ],
    "tags": [
      "github project"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}