Cosmic Census - Oct 2017
Leiden Observatory
e.g. CANDELS UDS: Galametz et al. (2013)
Brammer et al. (2008)
Arnouts et al. (1999)
Ilbert et al. (2006)
Bender et al. (2001)
Bolzonella et al. (2000)
Feldmann et al. (2006)
Benitez (2000)
Total citations: ~2800
Additional rest-frame errors
Corrections to the observed zeropoints
Brammer et al. (2008)
Dust
AGB Stars?
PAH/Dust emission/AGN?
Brammer et al. (2008)
Benitez (2000)
(aka machine learning)
Euclid: ~1.5 billion galaxies
LSST: ~10 billion galaxies
Estimated time to run EAZY on all sources (on a desktop machine):
~2+ years (Euclid)
~14+ years (LSST)
Sanchez et al. (2014)
Weak Lensing requirements:
Scatter
Bias
i.e. a representative subset of your sample with spectroscopic redshifts
Neural Networks
Self-organizing Maps (SOMs)
Deep learning
Support Vector Machines (SVM)
Naive Bayes
Gaussian Processes
Generalized Linear Models
Bayesian Network
k-Nearest Neighbour
Boosted Decision Trees
Randomised Forests
Relevance vector machines
Radial basis function networks
Normalised inner product nearest neighbour
Directional neighbourhood fitting
Voronoi tesselation density estimator
Non-conditional density estimation
Neural Networks
Self-organizing Maps (SOMs)
Deep learning
Support Vector Machines (SVM)
Naive Bayes
Gaussian Processes
Generalized Linear Models
Bayesian Network
k-Nearest Neighbour
Boosted Decision Trees
Randomised Forests
Relevance vector machines
Radial basis function networks
Normalised inner product nearest neighbour
Directional neighbourhood fitting
Voronoi tesselation density estimator
Non-conditional density estimation
Dahlen et al. (2012)
7/11 submitted photo-z estimates significantly overconfident for 1-sigma errors
Wittman et al. (2016)
See also Bordoloi et al. (2010)
Under-confident
Over-confident
Dahlen et al. (2012)
= Median of all photo-z estimates
= Median of best 5 photo-z estimates
See also Carraso Kind & Brunner (2014)
Also works for diff. templates with the same code
Gory details presented in...
Duncan et al. (2017a, 1709.09183)
and Duncan et al. (2017b, in prep)
a) Zeropoint offset calculated separately for each individual template set
b) Lazy parallelisation of eazy, field split into many chunks and run in parallel.
2. Separate galaxies and AGN dominated spectra where possible - optimise magnitude priors and calibration procedure for each set
1. Photometric redshift catalogs, including:
- Primary and secondary solutions
- Calibrated uncertainty estimates
1. Photometric redshift catalogs, including:
- Primary and secondary solutions
- Calibrated uncertainty estimates
- A range of corresponding diagnostic plots for each field
2. Selection functions:
For a source with a given set of photometric properties...
a) what is the probability of a photo-z estimate existing in the HELP database
b) what is the probability of a reliable* photo-z estimate existing in the HELP database
*a very flexible definition
Incorporating targeted ML estimates can dramatically improve estimates for AGN
Producing consistent high quality photo-zs for 1300sq.deg of the sky is a challenge...but manageable
The heterogeneous nature of the datasets makes template fitting the only feasible starting point
Bayesian combination of multiple redshift estimates provides near optimal solutions across multiple fields/source types
Calibrate your photo-z errors!