Automated Classification of HETDEX Spectra. Ted von Hippel (U Texas, Siena, ERAU)

Transcription

1 Automated Classification of HETDEX Spectra Ted von Hippel (U Texas, Siena, ERAU) Penn State HETDEX Meeting May 19-20, 2011

2 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

3 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

4 distribution of spectral library parameter 2 parameter 1

5 where does new spectrum belong? parameter 2 parameter 1

6 where does new spectrum belong? parameter 2 parameter 1

7 where does new spectrum belong? parameter 2 goals: statistics, rare types, new types parameter 1

8 forward problem: observed spectra in this parameter space parameter 2 parameter 1

9 inverse problem: recover location in parameter space from observed spectra? parameter 2 parameter 1

10 more difficult inverse problem parameter 2 reduced wavelength range decreased signal-to-noise reduced spectral resolution parameter 1

11 How to classify? classical, Chi-by-eye approach? cross correlation? Artificial Neural Networks (ANN)

12 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

13 see

14 spectra 1 input layer w 1 hidden layer(s) f=(1+e - ws ) -1 output layer training (goal) n n-1 n bias w n

15 spectra 1 input layer w 1 hidden layer(s) f=(1+e - ws ) -1 output layer training (1 st iter) n n-1 n bias w n

16 spectra 1 input layer w 1 hidden layer(s) f=(1+e - ws ) -1 output layer classification (n th iteration) n n-1 n bias w n

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

18 intensity Temperature

19 Pickles A0V main sequence star Eisenstein et al. fig 5 DA white dwarf

20 training testing von Hippel et al. 1994, MNRAS, 269, 97

21 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

22 Venus Mars normalized flux Jupiter wavelength (microns)

23 normalized flux wavelength (microns)

24 % correct signal-to-noise

25 stars T, log(g), Z other continuum SFR, age +morphology +photometry emission line(s) HII, AGN

26 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)

27 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

28 End