| # 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](https://arxiv.org/abs/2510.06962)** | |
| ## 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](https://firmamento.nyuad.nyu.edu/home) 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. | |
| --- | |
| ## Quick Start (Python) | |
| ```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()) | |