DESI Research
Galaxy evolution, quasar physics, and ML-driven spectral analysis
Our research uses DESI Data Release 1 — the largest spectroscopic survey to date — to investigate fundamental questions about galaxy evolution and quasar physics. We build Analysis-Ready Datasets (ARDs) that transform raw survey data into enriched, science-ready products.
Active Research Areas
1. Environmental Quenching in Cosmic Voids
Cosmic voids are vast underdense regions — the "bubbles" between filaments of the cosmic web. Galaxies in voids experience minimal environmental interactions, making them ideal laboratories for studying intrinsic evolution. We compare void galaxies to wall galaxies to disentangle "nature" (mass-driven) from "nurture" (environment-driven) quenching mechanisms.
2. AGN Feedback and Outflow Energetics
Quasar-driven outflows may regulate galaxy growth through AGN feedback. We use semi-automated spectral fitting and Cloudy photoionization modeling to measure outflow properties at scale — distances, mass outflow rates, and kinetic luminosities — creating the first comprehensive catalog of quasar outflow energetics.
3. ML-Driven Anomaly Detection
With 1.6 million quasar spectra, systematic outlier detection reveals rare objects that manual inspection would miss. We use autoencoder architectures to identify statistical anomalies that may represent unusual accretion physics, rare evolutionary phases, or potentially new source types.
Value-Added Catalogs (VACs)
Our ARD integrates 9 DESI DR1 Value-Added Catalogs:
Galaxy VACs
| VAC | Purpose |
|---|---|
| FastSpecFit | Stellar continuum + emission lines |
| PROVABGS | Bayesian SED fitting with posteriors |
| DESIVAST | Cosmic void classifications (4 algorithms) |
| Gfinder | Halo-based group catalog |
QSO VACs
| VAC | Purpose |
|---|---|
| AGN/QSO | Spectral + IR classification |
| CIV Absorber | Intervening CIV systems |
| MgII Absorber | Intervening MgII systems |
| QMassIron | Black hole masses |
| Stellar Mass/EmLine | CIGALE masses + emission lines |
Methodology
We follow a three-layer enrichment model:
- Foundation Layer: Unified catalog with cross-match linkage, environmental classifications
- Physics Layer: Derived quantities — Lick indices, pPXF kinematics, SED posteriors
- AI/Embeddings Layer: Neural spectral embeddings, similarity metrics
This computation is performed on our dedicated cluster. View Infrastructure Details →