January 9, 2007January 9, 2007

11 / 8 / 8

DEEP2: Redshift DEEP2: Redshift Identification of Single-Line Identification of Single-Line

Emission GalaxiesEmission Galaxies

Evan KirbyEvan Kirby

Raja Guhathakurta, Sandy Faber,Raja Guhathakurta, Sandy Faber,

Benjamin Weiner, Michael Cooper,Benjamin Weiner, Michael Cooper,

DEEP2 TeamDEEP2 Team

22 / 8 / 8

Easy RedshiftEasy Redshift

z = 0.112!z = 0.112!

33 / 8 / 8

Difficult RedshiftDifficult Redshift

Single line!

z = ???

Single line!

z = ???z = 0.093z = 0.093

44 / 8 / 8

A single line is …A single line is …

• HHαα 6563 6563 ÅÅ• [O[OIIIIII]] 5007 Å 5007 Å• HHββ 4861 4861 ÅÅ• [O[OIIII]] 3727 3727 ÅÅ

• A single line is A single line is notnot [S[SIIII]] 6716 6716 ÅÅ or or LyLyαα 1216 1216 ÅÅ

55 / 8 / 8

BRI Evolution with RedshiftBRI Evolution with Redshift• Training set: DEEP2 Training set: DEEP2

galaxies withgalaxies with– Q=3 or Q=4Q=3 or Q=4

– At least one 5At least one 5σσ emission emission lineline

66 / 8 / 8

Neural NetworkNeural Network

Kirby et al. 2007 (submitted)Kirby et al. 2007 (submitted)

77 / 8 / 8

Accuracy and ResultsAccuracy and Results

Kirby et al. 2007 (submitted)Kirby et al. 2007 (submitted)

88 / 8 / 8

ConclusionsConclusions

• A neural network can pin down single A neural network can pin down single emission lines with emission lines with BRIBRI magnitudes magnitudes and colors aloneand colors alone

• The accuracy exceeds 90% in all four The accuracy exceeds 90% in all four line categoriesline categories

• Additional information (angular size, Additional information (angular size, correlation fuction, AEGIS) can help correlation fuction, AEGIS) can help even moreeven more