Where are all the dark galaxies? Predicting galaxy/halo locations from their bright neighbors
Alice Chen, Niayesh Afshordi

TL;DR
This paper employs symbolic regression to predict the locations of dark, faint, or non-luminous objects in the universe based on their proximity to observable bright galaxies, enhancing detection strategies.
Contribution
It introduces an interpretable machine learning model that accurately predicts dark object densities from their distances to bright neighbors in galaxy simulations.
Findings
Predicts dark object density using an analytic expression based on neighbor distances.
Outperforms linear biasing models at certain scales.
Potentially enables targeted searches for dark objects in astronomical surveys.
Abstract
Astronomical objects in our universe that are too faint to be directly detectable exist and are important - an obvious example being dark matter. The same can also apply to very faint baryonic objects, such as low luminosity dwarf galaxies and gravitationally compact objects (e.g., rogue planets, white dwarfs, neutron stars, black holes, dark sirens). While they are very difficult to observe directly, they have locations that are highly important when studying astrophysical phenomena. Here, we use a machine learning algorithm known as symbolic regression to model the probability of a dark object's existence as a function of their separation distances to their closest two ``bright" (directly observable) neighbors, and the distances of these bright objects to each other. An advantage of this algorithm is that it is interpretable by humans and can be used to make reproducible predictions.…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsGalaxies: Formation, Evolution, Phenomena · Astronomy and Astrophysical Research · Computational Physics and Python Applications
