Liberal Arts and Sciences
Dittmann Lab
Led by Jason Dittmann · Astronomy
What we study
The lab studies exoplanets: it applies AI and machine-learning methods to data from space missions such as Kepler and TESS to find small, hard-to-spot planets, and uses high-resolution ground-based observations to study what their atmospheres are made of.
What working here is like
- Computational
- No organisms (data only)
- Coding required
- Project
- 1
- Papers
- 5
Current projects
Applying Convolutional Neural Networks to Detect Low‑Abundance Molecules in Exoplanet Atmospheres
Researchers generated tens of thousands of simulated hot‑Jupiter spectra, reduced them to JWST’s resolution (1.0‑12.0 µm, λ/Δλ ≈ 100‑1600), and built a convolutional neural network to identify which molecules are present, testing how well the model can spot low‑abundance signals.
From the Spring 2025 UF Undergraduate Research Symposium · abstract book, p. 494
Summaries are written by AI in plain language from public symposium abstracts and may be imperfect.
Recent papers, explained
- Variable X-Ray Flux May Explain Variable Helium Escape from the Habitable-zone Exoplanet LHS 1140b
2026 · The Astrophysical Journal Letters
Tests whether variable X-ray flux explains helium escape from the exoplanet LHS 1140b.
- Characterization of seven longer period eclipsing binary systems
2026 · Monthly Notices of the Royal Astronomical Society
Studies seven long-period star systems to test models of star formation.
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- Exploring Long-period Architectures: Four New Planet Candidates from Kepler with Periods >342 days
2026 · The Astronomical Journal
Finds four new long-period planets in Kepler data using a neural network.
- Helium escaping from the atmosphere of a nearby rocky exoplanet orbiting in a habitable zone
2026 · Science
Detects escaping helium in a nearby rocky planet's atmosphere.
- The spin–orbit alignment of five long period eclipsing binary systems
2026 · Monthly Notices of the Royal Astronomical Society
Measures the alignment of orbits in five long-period star systems.
Plain-language summaries are AI-written and reviewed by us; check the paper for details.
How to reach out
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