Spectroscopic Surveys: PresentSpectroscopic Surveys: Present

Ching-Wa YipChing-Wa YipThe Johns Hopkins UniversityThe Johns Hopkins University

March 21March 21stst, 2007, 2007

Astronomical Spectroscopy & The Virtual Observatory, Workshop at ESAC 2007

Outline

Present Spectroscopic Surveys & Science Highlights

SDSS & Science

Techniques

Needs and Challenges

On-going efforts at JHU

Spectroscopic Science & VO

Motivations: stellar populations and galaxy evolution

(http://map.gsfc.nasa.gov/)

379,000 yr -- recombination; 1 Myr -- formation of first stars (population III); 1 Myr to 100 Myr -- reionization; 1 Gyr -- formation of galaxy and star (population II & I); 13.7 Gyr -- present day

• In the hierarchical galaxy formation, galaxies interact or merge with each other through dark matter halo interactions.• Star form and die along the way, resulting in the galaxy spectra we see.• How well do the properties of galaxies locally and at high-redshift fit in the scenario of hierarchical galaxy formation?

Stellar populations and galaxy evolution: e.g. K+A galaxies

• The ``K+A'' (or post-starburst) galaxies show a mixed populations of K stars and A stars (Dressler & Gunn 1983).

• Attract many recent interests in studies of star formation rates in different density environments, showing that high density environment suppresses star formation. (Balogh et al. 2001; Martinez et al. 2002, Lewis et al. 2002; Gomez et al. 2003; Blanton et al. 2003b; Hogg et al. 2003, 2004; Kauffmann et al. 2004; Blanton et al. 2005)

• (The studies of) stellar populations in galaxies are intimately linked to large scale structures.

+ A star absorption features?

Moore’s Law in Surveys

•The spectra in redshift surveys increase rapidly per year, following nearly the Moore's Law.

•The large number of spectra requires new methods to consider the higher-dimensional data sets objectively and automatically, and to reduce the error rate, e.g., in classification.

Spectroscopic Surveys

Year Number R Redshifts Magnitude Limit

SDSS 2000 106 2000 0 to 0.3 r < 17.77 (for galaxies)

105 2000 0 to 6 r < 19.1 (for quasars)

DEEP2 2002 5x104 5000 0.7 to 1.5 I < 24

VVDS 2002 1.5x105 250 0.2 to 4.0 I < 22.5

5x104 250 0.2 to 4.0 I < 24

103 250 0.2 to 4.0 I < 26

zCosmos 2005 2x104 600 0.1 to 1.2 I < 22.5

2x104 230 1.4 to 3.0 color selection

• The surveys have recently starting to go deeper instead of more redshifts.• Magnitude selections are used instead of color selections.

Spectroscopic Surveys - Stars

SEGUE – the Sloan Extension for Galactic Understanding and Exploration

• It will map structure and stellar content of the Milky Way with 240,000 stars ( accuracy ~10 km/s, [Fe/H] accuracy ~0.3 dex) by 2008.

RAVE – the Radial Velocity Experiment

• It will provide 1 million radial velocity and spectra (R ~ 7500) by 2010.

• SDSS– High-z quasars (z ~ 6, Fan et al.)– New Milky Way Stellar Streams and Satellites (SDSS collaboration)

• VVDS– High-z galaxy populations (z = 1.4 – 5, 2-6 times larger then previous

Lyman-break galaxies using color-selection) (Le Fèvre et al.)• DEEP2

– Luminosity function evolution (Faber et al.): the number density of red galaxies increased from z = 1 to now, where that for the blue galaxies is nearly constant.

• RAVE– Stellar dynamics of galaxy agree with dark matter model (Wyse et al.)– Mass of the Milky Way (0.7 - 2.0 x 1012 solar) from high-velocity stars

• etc…

Spectroscopic Surveys – Discovery Highlights

Spectroscopic Surveys – Discovery Highlights

(Le Fèvre et al.)

VVDS found high-redshift (z = 1.4-5, look-back time 9-12 Gyr) galaxy populations• 2-6 times larger then previous sample of Lyman-break galaxies.• Composite spectra do not show strong Ly, implying that the galaxies may be dusty.

Spectroscopic Surveys – Discovery Highlights

(Walter et al.)

SDSS found high-redshift quasars (Fan et al.)• Gunn-Peterson absorption trough is found – reionization time scales can be deduced.• Combine with other studies, this discovery is also a strong evidence for the important role of supermassive black hole (106 – 109 solar) in galaxy evolution.

• z = 6.42 QSO (SDSS J114816.64+525150.3)• Carbon-monoxide (CO) image suggests a bulge mass of 109 solar, inconsistent with local bulge mass.• Black hole forms before galaxy bulge?

Spectroscopic Surveys – Discovery Highlights

SDSS found new stellar streams and satellites of the Milky Way Galaxy• The Milky Way Galaxy has undergone a rich interaction/merging history with its satellites during its formation.

(Belokurov et al.)† The spatial density of SDSS stars around the North Galactic Cap. Most distant stars (red), nearest stars (blue)

The Sloan Digital Sky Survey

(2.5m telescope in APO.)

• The SDSS have produced the biggest map of the nearby universe (200,000,000 celestial objects in a quarter of the sky).• The SDSS and SDSS-II will together provide spectra of approximately 1,000,000 galaxies, 100,000 QSOs, 500,000 stars.• Fiber spectroscopy with R = 1800, 3800 – 9200 Å in the observed frame.• It has produced 2000 publications.• It has excellent user interfaces (e.g. CasJobs, Thakar et al.) and active public outreach (e.g. Roddick et al.).

(SDSS fiber plug plate.)

Using SDSS Spectra

• Flux-limited survey:– Need a volume limited sample to infer statistical properties on galaxy redshifts.

• Fiber-fed survey: – Aperture effects in physical parameters e.g. spectral type, line equivalent width. – Spectrophotometry at present (DR5) it is 10% accurate.– Spectra were not corrected against foreground extinction (e.g. using SFD map)

from the pipeline (DR5).– Stellar absorptions: needed to be corrected if use pipeline values (e.g. constant

increment of 0.7 Å, Hopkins et al. 2003).

† The central 3 arcsec of the galaxy (green circle).

MPA/Garching galaxy value-added catalog (DR4)

• Mass-metallicity relationship– Galaxy evolution is not closed box, galactic wind

may play a role in driving out metal

• Stellar mass of galaxies– (Dn(4000), HA) as a stellar mass estimator

– Average galaxy stellar mass ~ 5x1010 solar– Most galaxies lie on locus of smooth star

formation history

(Tremonti et al. 2004)(Kauffmann et al. 2002)

Galaxy/Narrow Line AGN Host Classifications: Baldwin-Phillips-Terlevich (BPT) Diagrams

(Groves 2006)

(Kewley et al. 2006)

• BPT diagram is shown to be a good way to classify ~85000 SDSS galaxies. • Seyfert 2 and LINERs seem to form acontinuous sequence of decreasingblack hole accretion rate.

Galaxy Classification by Eigenspectra: the eClass

• KL transform (or called PCA) on observed galaxy spectra to get eigenspectra (Connolly et al. 1995).

• The Main Galaxy Sample (Strauss 2002), ~170,000 spectra

• 1st order: mean galaxy spectrum (continuum is similar to stellar populations usually found in early-type galaxies)

• 2nd order: modulation of the continuum slope

• 3rd order: discriminate post-starburst activities in galaxies (Connolly et al. 1995)

(Yip et al. 2004)

• Decompose each observed galaxy spectrum into an orthogonal set of eigen-function {ei}:

f = a0 e0 + a1 e1 + a2 e2 + ···

Removing host galaxy component from QSO spectra

(Yip et al. 2004)(Vanden Berk et al. 2006)

f(QSO; cleaned) = f(QSO; observed) – a2 e2(host galaxy)

• To apply the eigenspectrum in host-galaxy removal, we construct the cleaned QSO spectrum as follows

† QSO 2nd eigenspectrum (black) vs.galaxy mean spectrum (red).

† QSO spectrum (top black) is decomposed into AGN (blue) and host galaxy (green).

Analyzing spectra

• Model based– e.g. stellar population

synthesis models for galaxy continua, Iron templates for quasars

• Pro– easier to extract physical

parameters as described by the models

• Con– models are imperfect

• e.g. ingredient incompleteness

– may not be the best way to make discovery about the data

• Empirical approach– e.g. composite spectra to get

average properties, eigenspectra to get correlations and diversities.

• Pro– does not rely on models– may be easier to make

discovery• Con

– need more interpretations to relate to real physics

– may need to go back to modeling later on

Present Challenges

• Model Galaxy Spectra• Spectrophotometry is not perfect in observed or/and synthetic integrated

spectra of stellar populations.• Model ingredients are incomplete, e.g. no AGB stars, metallicity

evolution is mostly neglected (e.g. PÉGASE, Bruzual & Charlot).

• Model QSO spectra• Line widths can be different from line to line, and object to object.• Line blue-shift (may be due to outflows) (Richards et al.).• Pure emission/absorption lines in QSO spectra are difficult to be

isolated because of iron emissions.

• Multi-wavelength, -epoch, -catalog• Cross-calibration can be tricky, e.g. how to separate systematical from

statistical errors? (Talks on the 2nd day)

Stellar Population Synthesis on Galaxy Spectra

• It is important to sample the full probability distribution of model parameters.

(Yip et al. 2007 in prep)

† True vs. estimated parameters using MCMC, S/N = 30 for the synthetic spectra.

† Estimated parameters:MCMC vs. chisq minimization.Synthetic spectra aregenerated from PEGASE.Mean parameter errors fromchisq-min can be a factor of couple inferior.

Stellar Population Synthesis on Galaxy Spectra: Parameter Degeneracy

† 1-sigma confidence contour between the estimated stellar population parameters of a galaxy, in age vs. Z,age vs. e-folding time, and age vs. E(B-V).

† Degeneracy involving intrinsic dust extinction of some galaxies can be more dominating than the infamous age-metallicity degeneracy of old stellar population.

Template Fitting: QSO & Iron contamination

• Iron has many excitation levels making the theoretical modeling complicated. • (23,000 FeII transitions in over 800 fine-structure levels, Sigut and Pradhan 1995)

• One approach is to fit iron templates to QSO spectra.• Iron templates (derived from Zwicky 1 spectra)

• in the optical, Veron-Cetty and Veron 2004

• in the UV, Vestergaard and Wilkes 2001

• Iron templates (through radiative transfer)• in the UV-optical, Sigut and Pradhan 2003

† QSO spectrum with iron lines removed (black)

† Observed QSO (black)Best fit iron template + continuum (orange)Masking in fitting (blue)

Refining spectrophotometry in SDSS galaxies

• We adopt 2 methods to remove -dependent systematics between the spectra at two epochs:

- Wilhite et al. (2005) to remove -dependent systematics between the spectra at two epochs- similar to Filipenkko (1982) approach to remove atmospheric differential refraction of stars, but using observed light profiles of galaxies instead of the King profile

• Both the absolute continuum band scaling and the [OIII] absolute calibration (Peterson et al. 1998) are tested to remove -independent normalization between the spectra at two epochs.

† The calibration spectrum for plate 390, SDSS.

Refining spectrophotometry in SDSS: correct against atmospheric differential refraction using realistic galaxy light profiles

refractive index of the Earth atmospheren = n (air temp, air pressure)

Earth Atmosphere

**

R = R() – R(5000) = 206265 [n() – n(5000)] tan(z)

Refining spectrophotometry in SDSS galaxies

• The SDSS provides on average 10% spectrophotometry.• DR6 will have improved spectrophotometry (Schlegel et al.).• Spectrophotometry refinement on DR5 galaxies (JHU/Pitt) – a factor of 5

improvements in repeatability (before: 3.2% to after: 0.5%) is achieved.

† Flux S/N vs. fractional difference between twice observed spectra.

Before After

Refining spectrophotometry in SDSS galaxies

Before3% difference

After0% difference

† Observed spectra at 2 epochs (top). Difference spectrum between them (bottom).

• Set the standard. Remove discrepancies among astronomers.

• Help in error checking: converge faster to the right solutions.

• Fast searching and cross matching multiple catalogs.

• Nice tools have already been available, so we can focus on science:

– VO Services (Budavári)

• Searching tools

• Spectrum Services (Dobos et al. 2004, talk this afternoon)

• Filter Services

• Cosmological Services

• Footprint services

– Specview (Busko)

– Your work here…

.• On-going efforts at JHU

– Spectrophotometry in the SDSS galaxies have been improved by a factor of 5 for the repeated observations.

– Modeling of QSO continuum and line kinematics.– We demonstrate the importance of sampling the full probability

distribution of parameters in the stellar population synthesis modeling of galaxy spectra (i.e. MCMC in general give different results from chisq minimization).

– Degeneracies between intrinsic dust extinction and other parameters estimated from some galaxy spectra are found to be more dominating than the infamous age-metallicity degeneracy.

Conclusion

• Spectroscopic surveys are essential for understanding galaxy formation.

• They provide a platform for science discovery.• Surveys like SDSS and before have followed Moore’s Law. • Now they tend to go deeper for higher-redshifts objects.• Many interesting science have been done using spectra, but more

challenges will likely call for more innovative approaches.

Conclusion