Lina Necib
Massachusetts Institute of Technology, MA·Astronomy & Astrophysics·Early-career
The Milky Way grew by colliding with and absorbing smaller galaxies. Stars from those collisions still move through our galaxy as debris, and they hold a record of how it formed.
- Fields
Astronomy & Astrophysics
Physics
Computer Science
- Topics
Milky Way
galactic archaeology
galaxy mergers
- Grants
- 2 known grants, through Jun 2029
What they work on
- Develop a clustering algorithm that accounts for measurement uncertainties and scales to more than 1.5 billion stars, to find debris from galaxies that merged with the Milky Way in data from the Gaia space mission.
- Train an interpretable equivariant graph neural network on the Illustris-TNG50 simulation to predict the merging times and infall masses of past mergers, and apply it to Gaia DR3/DR4 data to build the Milky Way's merger tree.
- Implement key classes of dark matter particle models as validated public software modules in the GIZMO cosmological-hydrodynamical simulation code.
- Simulate Milky Way-like and dwarf galaxies with identical starting conditions and ordinary-matter physics under different dark matter models, and derive observable predictions that could distinguish the models.
More about the research
A second open question is the nature of dark matter, an invisible substance that makes up over 80% of the matter in the universe and is detected only through its gravity. The lab's projects use machine learning and computer simulations to connect what we observe in galaxies to their histories and to what dark matter is.
Why it matters
This is basic research into how our galaxy formed and what most of the universe's matter is made of. Reconstructing the Milky Way's merger history adds a timeline of the collisions that built it. Testable predictions from controlled simulations could help astronomers use current and future telescope data to narrow down or rule out classes of dark matter models.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Gary Bernstein
University of Pennsylvania, PA·Astronomy & Astrophysics
Measure precise astrometric positions of stars in hundreds of thousands of Dark Energy Camera images from the Blanco Telescope, collected over 12 years.
Katherine Whitaker
University of Massachusetts, MA·Astronomy & Astrophysics·Early-career
Map large statistical samples of massive galaxies and their environments across cosmic history using the Prime Focus Spectrograph on the Subaru Telescope.
Dennis Zaritsky
University of Arizona, AZ·Astronomy & Astrophysics
Measure the reddening of thousands of faint background sources projected near 25 nearby galaxies, using extremely deep images, to estimate how much dust surrounds each galaxy.
The school
Massachusetts Institute of Technology: 25 labs on PI Pages, $1.1B a year on research, 692 research doctorates a year.
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