Automated Classification of HETDEX Spectra

Ted von Hippel (U Texas, Siena, ERAU)

Penn State HETDEX Meeting May 19-20, 2011

Outline

•  HETDEX with stable instrumentation and lots of data ideally suited to automated classifiers

•  ANNs as classifiers •  examples: stars an planets •  how ANNs might fit into HETDEX pipeline

and what they might do for project

Example: Stellar Classification

•  spectra span a wide range of patterns within three physical dimensions (temperature, pressure, heavy element abundance).

•  have many thousands of new spectra and want to quickly determine their classification (interpolated position in parameter space) –  goals are statistics, rare types, anomalies

parameter 1

para

met

er 2

distribution of spectral library

parameter 1

para

met

er 2

where does new spectrum belong?

parameter 1

para

met

er 2

where does new spectrum belong?

parameter 1

para

met

er 2

where does new spectrum belong?

goals: statistics, rare types, new types

parameter 1

para

met

er 2

forward problem: observed spectra in this parameter space

parameter 1

para

met

er 2

inverse problem: recover location in parameter space from observed spectra?

parameter 1

para

met

er 2

reduced wavelength range

reduced spectral resolution decreased

signal-to-noise

more difficult inverse problem

How to classify?

•  classical, Chi-by-eye approach? •  cross correlation? •  Artificial Neural Networks (ANN)

Artificial Neural Networks

•  embed expertise without being an expert •  multi-dimensional interpolator •  which data properties correlate with which

classification parameters? •  uses entire spectral range of input data,

unbiased by preconceived notions of utility •  best fit / global minimum? •  can be Bayesian classifier

see http://www.neuro.mpg.de/english/rd/csn/research/index.html

input layer

hidden layer(s)

output layer

spectra training (goal)

bias

1

2

3

n-2

n-1

w1

wn

0.000

1.000

0.000

0.000

0.000

f=(1+e-∑ws)-1

n

input layer

hidden layer(s)

output layer

spectra training (1st iter)

bias

1

2

3

n-2

n-1

w1

wn

0.211

0.018

0.411

0.301

0.077

f=(1+e-∑ws)-1

n

input layer

hidden layer(s)

output layer

spectra classification (nth iteration)

bias

1

2

3

n-2

n-1

w1

wn

0.003

0.807

0.101

0.054

0.008

f=(1+e-∑ws)-1

n

von Hippel et al. 1994, MNRAS, 269, 97

Tem

pera

ture

inte

nsity

A0V

Eisenstein et al. fig 5

Pickles

DA

main sequence star

white dwarf

von Hippel et al. 1994, MNRAS, 269, 97

training

testing

Example: Planetary Classification Problem

•  spectra for object (planets) that belongs in a multidimensional model parameter space (abundances of range of atmospheric gases)

•  test ability to recognize spectra in this parameter space as a function of data quality

Jupiter

Venus Mars

wavelength (microns)

norm

aliz

ed fl

ux

wavelength (microns)

norm

aliz

ed fl

ux

signal-to-noise

% c

orre

ct

1994MNRAS.269...97V

1994MNRAS.269...97V

1994MNRAS.269...97V

1994MNRAS.269...97V

stars

other continuum

SFR, age

T, log(g), Z

emission line(s)

HII, AGN

+morphology

+photometry

Automated Classification Could …. •  hot stars and weak-lined stars as continuum calibrators

(varying throughput affect window function) –  many degrees of freedom in the instrument + unknown

LAE environmental effects –  channel-to-channel: optical or electronic effects –  field-size effects: pupil efficiency (psf, guiding drift) –  time-dependent effects: Temp-drifts in instrument,

gunk on optical surfaces, electronics drifts –  astrophysical effects: reddening changes over field

(may be able to use nearly all stars for this)

Automated Classification Could …. •  stellar science:

–  statistical population studies, WD search, extremely metal-poor stars, outer halo stars, C-studies via G-band, EHB stars, very rare stellar types

•  continuum galaxies –  classify by SFR/age to study as a function of redshift,

clustering •  AGN:

–  ANN good at digging out low S/N versions with a known recovery and contamination fraction; possibly faster than template matching

•  unusual objects discovery potential –  automated classifier looks through data in real time and

flags poor matches to training library, yet at good S/N

End