Outlook for Euclid Data Processing
Tom Kitching
[Euclid: OU Level 3 Cosmology Lead; WLWG Cosmic Shear Sub-Group; Theory DM Sub-Group]
Assessment
Phase Summary
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What is Euclid?
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Summary of some of the data challenges for Euclid
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A quick summary of the current data management plan
Euclid is still in the planning stage
Slides only have information from Assessment Phase Yellow Book
http://xxx.lanl.gov/abs/0912.0914
Assessment
Phase Summary
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Euclids science goals place raw data requirements
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Data processing challenges
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Large amount of data needed : statistics for dark Univ. science
High fidelity to measure the lensing effect and control systematics
Extreme precision (high CPU) needed for weak lensing
measurements
Data processing is I/O intensive
Inhomogenous Work Flow and resources required
Data Distribution challenges
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Distributed Science Data centers
Peta-byte raw data
Giga-scale catalogues
Visualisation of final science products
Euclid
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Space-based cosmology experiment
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Proposal to ESA Cosmic Visions
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Assessment
Phase Summary
M-Class Mission
Currently in the Definition Phase (B)
Other M-Class missions in CV : Plato, Solar Orbiter
2 missions will be selected for launch in 2 slots : 2017, 2018
Euclid was ranked 1st in the Assesment Phase downselect
Single Consortium (FR, IT, GE, UK, SP, CH, NW, NL, AU, US)
Currently in activity of submitting a response to the M-class CV
AO (due on Friday!)
Assessment
Phase Summary
Assessment
Phase Summary
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Mission
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1.2m Mirror, L2 orbit
5 Years Mission Duration
Map the sky in 1 optical band, 3 NIR bands
0.16’’ optical pixels for weak lensing measurement
0.20’’ IR pixels for photometry
Low-res NIR Spectra
20,000 sqdeg to a median z=1.0
Assessment
Phase Summary
Assessment
Phase Summary
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Science Goals
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Use Weak Gravitational Lensing and Baryon Acoustic
Oscillations
 Constrain the Dark Energy Equation of state to 1%
 Test General Relativity on cosmic scales
 Map Dark Matter, and constrain its properties
 Probe the initial conditions (perfect low-z complement to CMB)
Assessment
Phase Summary
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Will generate approx 2 Peta-Bytes of raw data
Many times this will be needed in simulations
Assessment
Phase Summary
Assessment
Phase Summary
shear needs to be measured
to 10-3 accuracy
Assessment
Phase Summary
Cosmic Lensing
gi~0.2
Real data:
gi~0.03
12/19
Slide from S. Bridle
Atmosphere and Telescope
Assessment
Phase Summary
Convolution with kernel
Real data: Kernel size ~ Galaxy size
13/19
Slide from S. Bridle
Pixelisation
Assessment
Phase Summary
Sum light in each square
Real data: Pixel size ~ Kernel size /2
14/19
Slide from S. Bridle
Noise
Assessment
Phase Summary
Mostly Poisson. Some Gaussian and bad pixels.
Uncertainty on total light ~ 5 per cent
15/19
Slide from S. Bridle
Assessment
Phase Summary
shear needs to be measured
to 10-3 accuracy
Assessment
Phase Summary
3 billion galaxies
1 billion with spectra
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Assessment
Phase Summary
Data Intensive, Inhomogenous I/O and CPU Intensive
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Assessment
PS1 Example
Phase Summary
More effects (e.g. CTI), higher accuracy needed for Euclid
Assessment
Phase Summary
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PS1 Example
For PS1 redo obj detection, stacking, PSF model etc.
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WL needs very high precision, check IPP, possible incorporation
Euclid Ground Segment
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(assessment phase, slightly updated now, qualitatively the same)
Consortium
Assessment
Phase Summary
Assessment
Phase Summary
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Data Distribution
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“Data centric approach” through the Euclid Mission Archive
Data will be “distributed/federated/partially replicated” over all
participants
Still not clear *exactly* what this means for x PetaBytes of Data
and Giga-scale catalogue products
Consortium
Conclude
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Euclids science goals place raw data requirements



Large amount of data needed : statistics for dark Univ. science
High fidelity to measure the lensing effect and control systematics
Data processing challenges




Assessment
Phase Summary
Extreme precision (high CPU) needed for weak lensing
measurements
Data processing is I/O intensive
Inhomogenous Work Flow and resources required
Data Distribution challenges




Distributed Science Data centers
Peta-byte raw data
Giga-scale catalogues
Visualisation of final science products