Determining satellite infall times using machine learning. Issue 2 (23rd January 2023)
- Record Type:
- Journal Article
- Title:
- Determining satellite infall times using machine learning. Issue 2 (23rd January 2023)
- Main Title:
- Determining satellite infall times using machine learning
- Authors:
- Barmentloo, Stan
Cautun, Marius - Abstract:
- ABSTRACT: A key unknown of the Milky Way (MW) satellites is their orbital history, and, in particular, the time they were accreted onto the MW system since it marks the point where they experience a multitude of environmental processes. We present a new methodology for determining infall times, namely using a neural network (NN) algorithm. The NN is trained on MW-analogues in the EAGLE hydrodynamical simulation to predict if a dwarf galaxy is at first infall or a backsplash galaxy and to infer its infall time. The resulting NN predicts with 85-per cent accuracy if a galaxy currently outside the virial radius is a backsplash satellite and determines the infall times with a typical 68-per cent confidence interval of 4.4 Gyr. Applying the NN to MW dwarfs with Gaia EDR3 proper motions, we find that all of the dwarfs within 300 kpc had been inside the Galactic halo. The overall MW satellite accretion rate agrees well with the theoretical prediction except for late times when the MW shows a second peak at a lookback time of 1.5 Gyr corresponding to the infall of the LMC and its satellites. We also find that the quenching times for ultrafaint dwarfs show no significant correlation with infall time and thus supporting the hypothesis that they were quenched during reionization. In contrast, dwarfs with stellar masses above 10 5 M⊙ are found to be consistent with environmental quenching inside the Galactic halo, with star-formation ceasing on average at $0.5^{+0.9}_{-1.2}$ Gyr afterABSTRACT: A key unknown of the Milky Way (MW) satellites is their orbital history, and, in particular, the time they were accreted onto the MW system since it marks the point where they experience a multitude of environmental processes. We present a new methodology for determining infall times, namely using a neural network (NN) algorithm. The NN is trained on MW-analogues in the EAGLE hydrodynamical simulation to predict if a dwarf galaxy is at first infall or a backsplash galaxy and to infer its infall time. The resulting NN predicts with 85-per cent accuracy if a galaxy currently outside the virial radius is a backsplash satellite and determines the infall times with a typical 68-per cent confidence interval of 4.4 Gyr. Applying the NN to MW dwarfs with Gaia EDR3 proper motions, we find that all of the dwarfs within 300 kpc had been inside the Galactic halo. The overall MW satellite accretion rate agrees well with the theoretical prediction except for late times when the MW shows a second peak at a lookback time of 1.5 Gyr corresponding to the infall of the LMC and its satellites. We also find that the quenching times for ultrafaint dwarfs show no significant correlation with infall time and thus supporting the hypothesis that they were quenched during reionization. In contrast, dwarfs with stellar masses above 10 5 M⊙ are found to be consistent with environmental quenching inside the Galactic halo, with star-formation ceasing on average at $0.5^{+0.9}_{-1.2}$ Gyr after infall. … (more)
- Is Part Of:
- Monthly notices of the Royal Astronomical Society. Volume 520:Issue 2(2023)
- Journal:
- Monthly notices of the Royal Astronomical Society
- Issue:
- Volume 520:Issue 2(2023)
- Issue Display:
- Volume 520, Issue 2 (2023)
- Year:
- 2023
- Volume:
- 520
- Issue:
- 2
- Issue Sort Value:
- 2023-0520-0002-0000
- Page Start:
- 1704
- Page End:
- 1720
- Publication Date:
- 2023-01-23
- Subjects:
- Galaxy: formation -- Galaxy: halo -- galaxies: dwarf -- galaxies: interactions -- cosmology: theory
Astronomy -- Periodicals
Periodicals
520.5 - Journal URLs:
- http://mnras.oxfordjournals.org/ ↗
http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1365-2966 ↗
http://www.blackwell-synergy.com/issuelist.asp?journal=mnr ↗
http://www.blackwell-synergy.com/loi/mnr ↗
http://ukcatalogue.oup.com/ ↗ - DOI:
- 10.1093/mnras/stad222 ↗
- Languages:
- English
- ISSNs:
- 0035-8711
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 5943.000000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 25686.xml