Identifying the 3FHL catalog: V. Results of the CTIO-COSMOS optical spectroscopy campaign 2019
arXiv:2104.13333 · doi:10.3847/1538-4365/abf656
Abstract
As a follow-up of the optical spectroscopic campaign aimed at achieving completeness in the Third Catalog of Hard Fermi-LAT Sources (3FHL), we present here the results of a sample of 28 blazars of uncertain type observed using the 4m telescope at Cerro Tololo Inter-American Observatory (CTIO) in Chile. Out of these 28 sources, we find that 25 are BL Lacertae objects (BL Lacs) and 3 are Flat Spectrum Radio Quasars (FSRQs). We measure redshifts or lower limits for 16 of these blazars, whereas it is observed that the 12 remaining blazars have featureless optical spectra. These results are part of a more extended campaign of optical spectroscopy follow-up of 3FHL blazars, where until now 51 blazars of uncertain type have been classified into BL Lac and FSRQ categories. Further, this campaign has resulted in redshift measurements and lower limits for 15 of these sources. Our results contribute towards attaining a complete sample of blazars above 10 GeV, which then will be crucial in extending our knowledge on blazar emission mechanisms and the extragalactic background light.
References in corpus (24)
- Roma-BZCAT: A multifrequency catalogue of Blazars
- The Imprint of The Extragalactic Background Light in the Gamma-Ray Spectra of Blazars
- An observational determination of the evolving extragalactic background light from the multiwavelength HST/CANDELS survey in the Fermi and CTA era
- 2WHSP: A catalog of HE and VHE gamma-ray blazars and blazar candidates
- Optical spectroscopic observations of gamma-ray blazars candidates I: preliminary results
- Optical spectroscopic observations of -ray blazar candidates VI. Further observations from TNG, WHT, OAN, SOAR and Magellan telescopes
- 1WHSP: an IR-based sample of 1,000 VHE -ray blazar candidates
- Optical spectroscopic observations of blazars and gamma-ray blazar candidates in the Sloan Digital Sky Survey Data Release Nine
- Optical Spectroscopic Observations of Gamma-ray Blazar Candidates. V. TNG, KPNO, and OAN Observations of Blazar Candidates of Uncertain Type in the Northern Hemisphere
- Optical spectroscopic observations of gamma-ray blazar candidates III. The 2013/2014 campaign in the Southern Hemisphere
- A GeV-TeV Measurement of the Extragalactic Background Light
- Optical spectroscopic observations of gamma-ray blazar candidates IV. Results of the 2014 follow-up campaign
- Optical spectroscopic survey of a sample of Unidentified Fermi objects
- Optical Spectroscopic Observations of Gamma-Ray Blazar Candidates. VII. Follow-up Campaign in the Southern Hemisphere
- Optical spectroscopy of BL Lac objects: TeV candidates
- Optical spectroscopic observations of gamma-ray blazar candidates VIII: the 2016-2017 follow up campaign carried out at SPM, NOT, KPNO and SOAR telescopes
- Systematic Physical Characterization of the Gamma-Ray Spectra of 2FHL Blazars
- New High-z Fermi BL Lacs with the Photometric Dropout Technique
- Identifying the 3FHL catalog: CTIO results
- Optical spectroscopic observations of low-energy counterparts of Fermi-LAT gamma-ray sources
- The First Gamma-ray Emitting BL Lacertae Object at the Cosmic Dawn
- Identifying the 3FHL catalog: I. Archival Swift Observations and Source Classification
- ZBLLAC -- A spectroscopic database of BL Lacertae objects
- Identifying the 3FHL Catalog. IV. Swift Observations of Unassociated Fermi-LAT 3FHL Sources
Cited by in corpus (6)
- Incremental Fermi Large Area Telescope Fourth Source Catalog
- The Fourth Catalog of Active Galactic Nuclei Detected by the Fermi Large Area Telescope -- Data Release 3
- Identifying the 3FHL Catalog. VI. Swift Observations of 3FHL Unassociated Objects with Source Classification via Machine Learning
- Optical spectroscopic characterization of Fermi blazar candidates of uncertain type with TNG and DOT: First Results
- Identifying the 3FHL catalog: VI. Results of the 2019 Gemini optical spectroscopy campaign
- Classification of Fermi-LAT blazars with Bayesian neural networks