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Multi-Source Astronomical Data Requirements

Problem: GAIA DR3 Limitations

GAIA DR3 provides:

  • ✅ Astrometry (RA, Dec, parallax, proper motion)
  • ✅ Photometry (G, BP, RP bands)
  • ✅ Radial velocity (for subset of stars)
  • ✅ 50M+ sources

GAIA DR3 MISSING:

  • ❌ Spectroscopy (detailed spectral lines)
  • ❌ Infrared photometry (thermal emission)
  • ❌ Molecular data (gas, dust)
  • ❌ Object classification (beyond basic)
  • ❌ Extended sources (galaxies, nebulae)
  • ❌ Complete all-sky coverage

Required Data Sources

1. SIMBAD (CDS Strasbourg)

Purpose: Object identification & cross-references

Provides:

  • Object names and aliases
  • Object types (star, galaxy, nebula, etc.)
  • Cross-references to other catalogs
  • Bibliographic data
  • Basic measurements

Use cases:

  • Name resolution ("Betelgeuse" → coordinates)
  • Object classification
  • Finding related observations

API: http://simbad.u-strasbg.fr/simbad/sim-script

2. ESO Archive (European Southern Observatory)

Purpose: High-resolution spectroscopy & imaging

Provides:

  • VLT spectroscopy (UVES, FLAMES, X-SHOOTER)
  • High-resolution imaging (SPHERE, MUSE)
  • Molecular line data
  • Detailed chemical composition

Use cases:

  • Stellar atmosphere analysis
  • Velocity structure
  • Chemical abundances

API: http://archive.eso.org/tap_obs

3. ALMA Archive (Atacama Large Millimeter Array)

Purpose: Molecular & dust emission

Provides:

  • Molecular line observations (CO, H2O, etc.)
  • Continuum emission (dust)
  • High angular resolution (milliarcsec)
  • Circumstellar disks, outflows

Use cases:

  • Molecular cloud physics
  • Dust properties
  • Mass-loss rates

API: https://almascience.eso.org/tap

4. AKARI (Japanese IR satellite)

Purpose: Infrared photometry

Provides:

  • 9, 18, 65, 90, 140, 160 μm bands
  • All-sky infrared survey
  • Thermal emission
  • Dust temperatures

Use cases:

  • Stellar temperatures
  • Dust content
  • Circumstellar material

Catalogs:

  • IRC: Infrared Camera (9/18 μm)
  • FIS: Far-Infrared Surveyor (65-160 μm)

Access: VizieR catalogs II/297 (IRC), II/298 (FIS)

5. VizieR (CDS catalog service)

Purpose: Catalog aggregation & cross-matching

Provides:

  • 20,000+ astronomical catalogs
  • Cross-matching between catalogs
  • Unified query interface
  • Historical data

Use cases:

  • Finding complementary data
  • Literature values
  • Multi-wavelength photometry

API: http://vizier.u-strasbg.fr/viz-bin/votable

6. 2MASS (Two Micron All-Sky Survey)

Purpose: Near-infrared photometry

Provides:

  • J, H, K bands (1.25, 1.65, 2.17 μm)
  • All-sky coverage
  • Point sources & extended

Use cases:

  • Extinction measurements
  • Stellar populations
  • Cool stars

Access: VizieR catalog II/246

Data Integration Strategy

Priority Levels:

  1. GAIA (base astrometry) → Always query first
  2. SIMBAD (identification) → Get object type & names
  3. AKARI/2MASS (IR) → Thermal properties
  4. ESO/ALMA (spectroscopy) → Detailed physics
  5. VizieR (supplementary) → Fill gaps

Workflow:

1. Query GAIA for astrometry
   ↓
2. Query SIMBAD for identification & cross-refs
   ↓
3. Query AKARI/2MASS for IR photometry
   ↓
4. Query ESO/ALMA for spectroscopy (if available)
   ↓
5. Cross-match with VizieR for additional data
   ↓
6. Merge into unified database

Implementation Status

✅ Implemented:

  • SIMBAD query function
  • VizieR query function
  • AKARI catalog access
  • Multi-source query wrapper
  • Data merging function

⏳ TODO:

  • ESO TAP authentication
  • ALMA query integration
  • Automated cross-matching
  • Database caching
  • Quality flag handling

Column Mapping

Property GAIA SIMBAD AKARI ESO ALMA
RA/Dec ✅ ✅ ❌ ✅ ✅
Parallax ✅ ✅ ❌ ❌ ❌
PM ✅ ❌ ❌ ❌ ❌
G-band ✅ ❌ ❌ ❌ ❌
IR bands ❌ ❌ ✅ ❌ ❌
Spectroscopy ❌ ❌ ❌ ✅ ❌
Molecular ❌ ❌ ❌ ✅ ✅
Type ❌ ✅ ❌ ❌ ❌

Usage Example

from multi_source_data import query_multi_source, merge_multi_source_data

# Query Betelgeuse from multiple sources
results = query_multi_source(
    object_name="Betelgeuse",
    radius=1,
    sources=['SIMBAD', 'AKARI', 'VizieR']
)

# Merge into unified DataFrame
data = merge_multi_source_data(results)

print(data[['ra', 'dec', 'simbad_type', 'akari_9um', 'akari_18um']])

References