{
  "id": "1812.07190",
  "version": "v1",
  "published": "2018-12-18T06:27:03.000Z",
  "updated": "2018-12-18T06:27:03.000Z",
  "title": "A Machine Learning Based Morphological Classification of 14,251 Radio AGNs Selected From The Best-Heckman Sample",
  "authors": [
    "Zhixian Ma",
    "Haiguang Xu",
    "Jie Zhu",
    "Dan Hu",
    "Weitian Li",
    "Chenxi Shan",
    "Zhenghao Zhu",
    "Liyi Gu",
    "Jinjin Li",
    "Chengze Liu",
    "Xiangping Wu"
  ],
  "comment": "Accepted by ApJS. The full table of the catalog and code for our network can be downloaded from https://github.com/myinxd/mcrgnet",
  "categories": [
    "astro-ph.GA"
  ],
  "abstract": "We present a morphological classification of 14,251 radio active galactic nuclei (AGNs) into six types, i.e., typical Fanaroff-Riley Class I / II (FRI/II), FRI/II-like bent tailed (BT), X-shaped radio galaxy (XRG), and ring-like radio galaxy (RRG), by designing a convolutional neural network (CNN) based autoencoder (CAE), namely MCRGNet, and applying it to a labeled radio galaxy (LRG) sample containing 1,442 AGNs and an unlabled radio galaxy (unLRG) sample containing 14,251 unlabeled AGNs selected from the Best-Heckman sample. We train the MCRGNet and implement the classification task by a three-step strategy, i.e., pre-training, fine-tuning, and classification, which combines both unsupervised and supervised learnings. A four-layer dichotomous tree is designed to classify the radio AGNs, which leads to a significantly better performance than the direct six-type classification. On the LRG sample, our MCRGNet achieves a total precision of \\$\\sim 93\\%\\$ and an averaged sensitivity of \\$\\sim 87\\%\\$, which are better than those obtained in previous works. On the unLRG sample, whose labels have been human-inspected, the neural network achieves a total precision of \\$\\sim 84\\%\\$. Also, by using the Sloan Digital Sky Survey (SDSS) Data Release 7 (DR7) to calculate the $r$-band absolute magnitude (\\$M_\\mathrm{opt}\\$), and using the flux densities to calculate the radio luminosity (\\$L_\\mathrm{radio}\\$), we find that the distributions of the unLRG sources on the \\$L_\\mathrm{radio}\\$-\\$M_\\mathrm{opt}\\$ plane do not show an apparent redshift evolution, and could confirm with a sufficiently large sample that there could not exist an abrupt separation between FRIs and FRIIs as reported in some previous works.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2018-12-18T06:27:03.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "radio agns",
      "best-heckman sample",
      "morphological classification",
      "radio galaxy",
      "machine learning"
    ],
    "tags": [
      "github project"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}