Detection of new gamma-ray AGN candidates using SIMEFIC III and DBSCAN in Fermi-LAT data
ABSTRACT We present the detection of new high-energy gamma-ray active galactic nucleus candidates using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm on two-dimensional gamma-ray data from Fermi Large Area Telescope. Our approach involves iterative app...
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| Vydané v: | Monthly notices of the Royal Astronomical Society Ročník 537; číslo 2; s. 730 - 738 |
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| Hlavní autori: | , , |
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| Jazyk: | English |
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Oxford University Press
01.02.2025
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| Abstract | ABSTRACT
We present the detection of new high-energy gamma-ray active galactic nucleus candidates using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm on two-dimensional gamma-ray data from Fermi Large Area Telescope. Our approach involves iterative applications of the SIeving MEthod for FInding Core (SIMEFIC III) algorithm, designed to enhance point source detection. By integrating DBSCAN at each denoising step, we track and evaluate the significance of potential sources across iterations. Our findings indicate that source significance increases to a specific threshold, beyond which further denoising may remove genuine sources. At the optimal denoising stage, we identified 18 sources not listed in the Fermi catalogue, with several of these sources potentially matching entries in the the Candidate Gamma-Ray Blazar Survey (CRATES) and Roma-BZCAT (Roma-BZCAT Multi-Frequency Catalog of Blazars) catalogues. |
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| AbstractList | ABSTRACT
We present the detection of new high-energy gamma-ray active galactic nucleus candidates using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm on two-dimensional gamma-ray data from Fermi Large Area Telescope. Our approach involves iterative applications of the SIeving MEthod for FInding Core (SIMEFIC III) algorithm, designed to enhance point source detection. By integrating DBSCAN at each denoising step, we track and evaluate the significance of potential sources across iterations. Our findings indicate that source significance increases to a specific threshold, beyond which further denoising may remove genuine sources. At the optimal denoising stage, we identified 18 sources not listed in the Fermi catalogue, with several of these sources potentially matching entries in the the Candidate Gamma-Ray Blazar Survey (CRATES) and Roma-BZCAT (Roma-BZCAT Multi-Frequency Catalog of Blazars) catalogues. ABSTRACT We present the detection of new high-energy gamma-ray active galactic nucleus candidates using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm on two-dimensional gamma-ray data from Fermi Large Area Telescope. Our approach involves iterative applications of the SIeving MEthod for FInding Core (SIMEFIC III) algorithm, designed to enhance point source detection. By integrating DBSCAN at each denoising step, we track and evaluate the significance of potential sources across iterations. Our findings indicate that source significance increases to a specific threshold, beyond which further denoising may remove genuine sources. At the optimal denoising stage, we identified 18 sources not listed in the Fermi catalogue, with several of these sources potentially matching entries in the the Candidate Gamma-Ray Blazar Survey (CRATES) and Roma-BZCAT (Roma-BZCAT Multi-Frequency Catalog of Blazars) catalogues. We present the detection of new high-energy gamma-ray active galactic nucleus candidates using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm on two-dimensional gamma-ray data from Fermi Large Area Telescope. Our approach involves iterative applications of the SIeving MEthod for FInding Core (SIMEFIC III) algorithm, designed to enhance point source detection. By integrating DBSCAN at each denoising step, we track and evaluate the significance of potential sources across iterations. Our findings indicate that source significance increases to a specific threshold, beyond which further denoising may remove genuine sources. At the optimal denoising stage, we identified 18 sources not listed in the Fermi catalogue, with several of these sources potentially matching entries in the the Candidate Gamma-Ray Blazar Survey (CRATES) and Roma-BZCAT (Roma-BZCAT Multi-Frequency Catalog of Blazars) catalogues. |
| Author | Akhondi, F Soor, M Hedayati Kh, H |
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| Cites_doi | 10.1051/0004-6361/202141003 10.1093/mnras/stac1875 10.1007/978-3-662-04245-8 10.1051/0004-6361/201833005 10.3847/1538-4357/ad49a4 10.1051/0004-6361/201220133 10.1146/annurev-astro-081817-051948 10.1111/j.1365-2966.2007.12616.x 10.1088/0004-637X/740/2/98 10.1086/513742 10.1086/161295 10.1086/186531 10.1007/s10509-015-2254-2 10.1016/j.chemolab.2012.11.006 10.1088/0004-6256/140/6/1868 10.1046/j.1365-8711.2002.04939.x 10.1051/0004-6361/202141193 10.1093/mnras/staa368 10.1051/0004-6361/201833360 |
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We present the detection of new high-energy gamma-ray active galactic nucleus candidates using the Density-Based Spatial Clustering of Applications... We present the detection of new high-energy gamma-ray active galactic nucleus candidates using the Density-Based Spatial Clustering of Applications with Noise... ABSTRACT We present the detection of new high-energy gamma-ray active galactic nucleus candidates using the Density-Based Spatial Clustering of Applications... |
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| SubjectTerms | Active galactic nuclei Algorithms Blazars Clustering Gamma rays Noise reduction Point sources |
| Title | Detection of new gamma-ray AGN candidates using SIMEFIC III and DBSCAN in Fermi-LAT data |
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