skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


  • Home
  • Search Results
  • Page 1 of 1

Search for: All records

Creators/Authors contains: "Jong, Eun Ju"

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. ; (, American Astronomical Society, AAS Meeting)
    Manual flagging of RFI is extremely time-consuming and error-prone. We present a machine learning algorithm which automatically identifies radio frequency interference (RFI) in HI spectra. Our algorithm uses the features of polarization asymmetry (defined as |polA - polB|/[polA + polB] ) along with the skew and standard deviation of each channel over time to evaluate the presence of RFI. The algorithm was tested on hundreds of spectra taken by the Undergraduate ALFALFA Team (UAT) as part of the APPSS survey. It outperforms humans not only in speed, but in visually identifying RFI when it is weak or mimics properties of signals. This work has been supported by NSF grants AST-1211005 and AST-1637339. 
    more » « less
    Accepted Manuscript Full Text Available
  2. ; ; (, American Astronomical Society, AAS Meeting)
    The number of extragalactic sources of HI detected in radio surveys is growing exponentially. It will soon no longer be feasible for human researchers to individually fit spectra. We present algorithms for automatically extracting the typical parameters of interest for the 21 cm HI line—recessional velocity, velocity width, and integrated flux—using neural networks. Features are produced by convolving spectra with templates generated with the Busy Function. We present the results of fitting hundreds of spectra with many different shapes, and at a wide range of signal to noise ratio. Additionally, we compare with prior methods of automated source extraction. This work has been supported by NSF grants AST-1211005 and AST-1637339. 
    more » « less
    Accepted Manuscript Full Text Available