1 / 8

DEEP2: Redshift Identification of Single-Line Emission Galaxies

Evan Kirby Raja Guhathakurta, Sandy Faber, Benjamin Weiner, Michael Cooper, DEEP2 Team. DEEP2: Redshift Identification of Single-Line Emission Galaxies. Easy Redshift. z = 0.112!. Difficult Redshift. z = 0.093. Single line! z = ???. A single line is …. H α 6563 Å [O III ] 5007 Å

anana
Download Presentation

DEEP2: Redshift Identification of Single-Line Emission Galaxies

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Evan Kirby Raja Guhathakurta, Sandy Faber, Benjamin Weiner, Michael Cooper, DEEP2 Team DEEP2: Redshift Identification of Single-Line Emission Galaxies

  2. Easy Redshift z = 0.112!

  3. Difficult Redshift z = 0.093 Single line! z = ???

  4. A single line is … Hα 6563 Å [OIII] 5007 Å Hβ 4861Å [OII] 3727Å A single line is not[SII] 6716Å or Lyα 1216 Å

  5. BRI Evolution with Redshift • Training set: DEEP2 galaxies with • Q=3 or Q=4 • At least one 5σ emission line

  6. Neural Network Kirby et al. 2007 (submitted)

  7. Accuracy and Results Kirby et al. 2007 (submitted)

  8. Conclusions A neural network can pin down single emission lines with BRI magnitudes and colors alone The accuracy exceeds 90% in all four line categories Additional information (angular size, correlation fuction, AEGIS) can help even more

More Related