The importance of
spectroscopic redshifts in
EMU
Minnie Yuan Mao
UTas/CASS/AAO
Outline
• ATLAS EMU Synergies
• The importance of redshift information
– Photometric
– Spectroscopic
• The need to train photometric redshifts and
statistical redshifts
• Specific case study - WATs
ATLAS
•
•
•
Australia Telescope Large Area Survey
Uses the Australia Telescope Compact
Array in Narrabri, NSW
ATLAS is wide and deep
– Wide = 7 square degrees
– Deep = ~10 µJy rms at 1.4 GHz
•
ATLAS comprises two fields so as to
mitigate cosmic variance
– CDFS – Chandra Deep Field South
– ELAIS-S1 – European Large Area ISO
Survey-South 1
http://www.atnf.csiro.au/research/deep/index.html
CDFS & ELAIS
• These fields were chosen as they have deep optical,
infrared and X-ray data.
• ~2000 radio sources so far (~30 µJy rms), expect
~16000 when we get down to a rms of 10 µJy
ATLAS  EMU
• My role in ATLAS has been to obtain
spectroscopic redshifts for the radio sources
so as to study their cosmic evolution
Cosmic Evolution
• Luminosity functions
• EMU: 10μJy rms
• Flux limit ~0.05mJy (5 x
rms)
• Cf Mauch & Sadler
(2007): ~2.8mJy
• EMU can probe the
luminosity function for
low-luminosity (>1022.5
W/Hz) out to z ~ 0.6!
4/8/2015
Minnie Mao - University of Tasmania
Flux limit of
EMU (50μJy)
The importance of redshift
information
• One of the key science goals of EMU, and
indeed one of the most pressing questions in
astronomy, is to understand how galaxies have
formed and evolved with cosmic time
• In order to do this we need redshift
information!
Surveys that EMU with cross-match with
(Norris et al. 2011)
Photometric redshifts
• Photometric redshifts (photo-z)
can be used to estimate the
distance of an objects
• The standard method is to fit the
photometry to a set of template
spectra, drawn from population
synthesis models, or using the
observed SEDs of low-redshift
galaxies
• These photometric points can be
thought about as really really lowresolution spectra
• EMU will have photometric
redshifts for ~30% of its sources
by 2013 and 70% by 2020 (Norris
et al. 2011)
Padmanabhan (2007), this figure shows the
way the spectrum of an elliptical galaxy moves
through various photometric bands as it
increases in redshift.
Problems with photo-z
• Choice of template?
– Galaxies evolve so templates constructed from low-z galaxies do not
necessarily match very well at high-z
– Using models makes it difficult to determine whether the models are good
representations of real galaxies and the application of corrections (such as for
dust) is difficult
• Best results are obtained if large training sets derived from real galaxy
spectra are used
– E.g. Salvato et al. (2008) for the COSMOS survey
– The training set must be analogous to the full dataset!
• Bell et al. (2009) note that using “state-of-the-art techniques”, typical
errors on photometric redshifts using spectroscopic training sets quite
small for red galaxies with strong 4000A breaks, but are larger for blue
galaxies, which have featureless spectra dominated by emission lines that
vary strongly from object to object
• Furthermore, EMU is a radio survey comprising a larger fraction of “odd”
objects
AAT
Anglo-Australian Telescope
Spectroscopic redshifts
• Spectrographs can measure the
spectrum of a galaxy and hence
the redshift can be derived from
doppler shifting of known
emission/absorption lines
• Olden days: a few spectra per
night
• Nowadays: hundreds of spectra
can be measured at once (e.g.
using multi-object spectrographs
like AAOmega on the AAT)
• note: still marked slower than
photometric plates.
Star-forming galaxies
• Strong Balmer lines
• Forbidden lines including [OII], [OIII] and [NII]
Elliptical galaxies
• Emission dominated by G & K stars
• The strong break at 4000Å is used to
determine photometric redshifts
A wealth of data from galaxy
spectra
• Accurate redshift
determination
• High-resolution
galaxy spectra allow
unambiguous
classification of
star-forming
galaxies and AGN
• Chemical
composition of
galaxy
BPT diagram (Baldwin et al. 1981) from
Peterson book on AGN.
ATLAS: Spec z vs. Phot z
• Photo-z from
Rowan-Robinson
et al. (2008)
• 5 optical bands
and 2 IR bands
• Broad agreement
but basically a
scatter plot at
z>1
Surveys that EMU with cross-match with
(Norris et al. 2011)
ECDFS: Spec z vs. Phot z
•
•
•
•
•
•
Phot-z from MUSYC survey
(Cardomone et al. 2010)
and COMBO-17 (Wolf et al.
2004)
COMBO-17 uses 17 bands
to derive phot-zs
MUSYC uses 32 bands to
derive photo-zs
These are among the best
photometric surveys
conducted to date!
Good agreement but still
basically a scatter plot at
z>1
(The mean z of EMU
sources will be z ~ 1 for SF
sources and z~1.9 for
AGNs)
Spectral Classifications
SF or AGN
• We compared our
spectral
classifications with
mid-infrared colourcolour diagrams
similar to using the
SWIRE data from
Spitzer (e.g. Lacy et
al. 2004, Richards et
al. 2006).
SF or AGN
• We compared our
spectral
classifications with
mid-infrared colourcolour diagrams
similar to using the
SWIRE data from
Spitzer (e.g. Lacy et
al. 2004, Richards et
al. 2006).
A quick look at statistical
redshifts
Mean z ~ 0.2
“Lacy Wedge”
Mean z ~ 0.6
Mean z ~ 0.4
Far-infrared Radio Correlation
Mean z ~ 0.37
Mean z ~ 0.7
Mean z ~ 0.37
Mean z ~ 0.7
Mid-way summary
• While spectroscopy provides a wealth of data
about an individual source – it is nigh impossible
to obtain spectroscopic redshifts for all 70 million
EMU sources – but a good training set of
spectroscopic redshifts is necessary to train both
photometric redshifts and statistical redshifts.
• EMU will have photometric redshifts for ~30% of
its sources by 2013, increasing to 70% by 2020
A Case Study:
• One of the science goals of EMU is to identify
clusters using diffuse radio sources
• We did this in ATLAS by searching for
overdensities around bent-radio sources
WATs in ATLAS
z = 0.38
z = 0.32
z = 0.22
z = 0.37
z = 0.22
z = 0.15
Spatial Distribution
4/8/2015
Minnie Mao - University of Tasmania
Redshift Distribution
There are a total of 309 sources with spectroscopic redshifts within a degree of the WAT
•
•
•
•
•
•
•
Peak at z~0.22!
42 galaxies in 3 ‘peak’ bins
Significant to 7σ
Velocity dispersion 870 km/s
Similar to rich, local Universe
clusters undergoing mergers
(e.g. A3667, A3376)
Inset shows 3 ‘peak’ bins
The distribution in the inset is
not Gaussian and shows
substructure, consistent with
merging systems
4/8/2015
Bin size (inset)
0.00125 = 375 km/s
Bin size
0.005 = 1500 km/s
0
0.2
Minnie Mao - University of Tasmania
0.4
z
0.6
0.8
Spectroscopic vs. Photometric
• Binsize z=0.05
• Photometric
redshifts from
Rowan-Robinson
(2008)
• Spectroscopic
redshifts from our
AAOmega
observations
Spectroscopic vs. Photometric
• Binsize z=0.05
• Photometric
redshifts from
Rowan-Robinson
(2008)
• Spectroscopic
redshifts from our
AAOmega
observations
Spectroscopic vs. Photometric
• Binsize z=0.025
• Photometric
redshifts from
Rowan-Robinson
(2008)
• Spectroscopic
redshifts from our
AAOmega
observations
Spectroscopic vs. Photometric
• Binsize z=0.025
• Photometric
redshifts from
Rowan-Robinson
(2008)
• Spectroscopic
redshifts from our
AAOmega
observations
Spectroscopic vs. Photometric
• Photometric
redshifts from
Rowan-Robinson
(2008)
• Spectroscopic
redshifts from our
AAOmega
observations
This overdensity can only be
seen when we have
spectroscopic redshifts!
Take home message
• While we won’t (ever?) get spectroscopic
redshifts for all 70 million EMU sources, it’s
imperative that we have a good sample of
spectroscopic redshifts so that we can train
photometric/statistical redshift algorithms.
• We need to get a complete spectroscopic
catalogue for a subset of EMU – We’ve
already started doing this in ATLAS!