1FLAT.fits

This dataset is associated with the paper:

📄 1FLAT: a Firmamento-based catalog of AGN in Fermi-LAT high-Galactic-latitude γ-ray sources

Description

1FLAT.fits is the first Firmamento–LAT AGN table derived from an independent reassessment of 5,062 high-Galactic-latitude (|b| ≥ 10°) γ-ray sources in Fermi-LAT 4FGL-DR4, using the Firmamento multi-frequency discovery platform with human-in-the-loop validation. The catalog provides updated/confirmed associations and new candidates, plus homogeneous estimates of the synchrotron-peak frequency and peak flux for blazars.

Key outcomes (from the paper)

• Agreement with 4FGL/4LAC in 4,493 cases (88.8%).
• 421 new blazar associations for previously unassociated 4FGL sources.
• 64 alternative blazar associations where 1FLAT prefers a different counterpart.
• 49 cases where the 4FGL/4LAC association is not confirmed by 1FLAT.
• 854 sources remain without a plausible counterpart (confirmed “no-counterpart” cases).

These results significantly reduce the fraction of unidentified extragalactic LAT sources at high latitude and furnish uniform blazar SED peak metrics for population studies.

File and Format

• File: 1FLAT.fits
• Type: FITS binary table (HDU 1)
• Rows: one per 4FGL high-latitude source
• Access: Readable with common astronomy tools (e.g., astropy.io.fits). Column names are sanitized (letters/digits/underscores).

Main Fields (selected)

1FLAT layer

• 1FLAT_name — Firmamento identifier (e.g., 1FLAT JHHMMSS.s±DDMMSS); for unassociated sources this mirrors the 4FGL source.
• RAJ2000, DEJ2000 — J2000 coordinates (association or, for unassociated, 4FGL position).
• CLASS — Blazar SED class: LSP, IBL, HSP, or galaxy / uncertain. Thresholds:
  • log10(ν_peak/Hz) < 13.5 (LSP), 13.5 ≤ log10(ν_peak/Hz) < 15 (IBL), log10(ν_peak/Hz) ≥ 15 (HSP).
• nu_syn, nuFnu_syn — Synchrotron-peak frequency (log10 Hz, observer frame) and peak νFν (log10 erg cm⁻² s⁻¹), estimated with W-Peak; BLAST estimates are used for validation.
• TAG — Classification tag summarizing agreement/novelty (e.g., BLAZAR confirmed, BLAZAR new, BLAZAR different, NOC confirmed, UNCERTAIN, etc.). :contentReference[oaicite:4]{index=4}

4FGL-DR4 provenance - 4FGL_Source_Name, 4FGL_ASSOC1/CLASS1, 4FGL_ASSOC2/CLASS2
- 4FGL_Signif_Avg, 4FGL_PL_Index, 4FGL_Energy_Flux100, 4FGL_Frac_Variability, ASSOC_PROB_BAY, ASSOC_PROB_LR.

4LAC-DR3 provenance

• 4LAC_ASSOC1, 4LAC_CLASS, 4LAC_nu_syn, 4LAC_nuFnu_syn.

See the paper’s Appendix D/Table 6–7 for a full column dictionary and allowed values.

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Quick Start (Python)

from astropy.io import fits
import numpy as np
import pandas as pd

hdul = fits.open("1FLAT.fits")
data = hdul[1].data
# Convert to DataFrame (handle FITS endian)
df = pd.DataFrame(np.array(data).byteswap().newbyteorder())

# Example: list newly discovered blazars with their peak metrics
new_blazars = df[df["TAG"] == "BLAZAR new"][
    ["1FLAT_name", "RAJ2000", "DEJ2000", "CLASS", "nu_syn", "nuFnu_syn"]
].sort_values("1FLAT_name")
print(new_blazars.head())