Digging Deeper in Imaging SurveysEric Suchyta

In Collaboration with: Eric Huff, Klaus Honscheid

Digging Deeper in Imaging Surveys APS April Meeting 2015 Eric Suchyta

Digging Deeper in Imaging Surveys APS April Meeting 2015 Eric Suchyta

What is the problem?• Big surveys want to make (sub)

percent level measurements

• Large catalogs (measured sizes, fluxes, etc.) are primary data output

• Images —> catalogs is complex, subject to measurement biases that can be difficult to fully characterize

• Blending • Proximity effects • Star-galaxy separation • Measurement software

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Digging Deeper in Imaging Surveys APS April Meeting 2015 Eric Suchyta

Digging Deeper in Imaging Surveys APS April Meeting 2015 Eric Suchyta

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Size?

• Blending • Proximity effects • Star-galaxy separation • Measurement software

What is the problem?• Big surveys want to make (sub)

percent level measurements

• Large catalogs (measured sizes, fluxes, etc.) are primary data output

• Images —> catalogs is complex, subject to measurement biases that can be difficult to fully characterize

Digging Deeper in Imaging Surveys APS April Meeting 2015 Eric Suchyta

Digging Deeper in Imaging Surveys APS April Meeting 2015 Eric Suchyta

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Brightness?

• Blending • Proximity effects • Star-galaxy separation • Measurement software

What is the problem?• Big surveys want to make (sub)

percent level measurements

• Large catalogs (measured sizes, fluxes, etc.) are primary data output

• Images —> catalogs is complex, subject to measurement biases that can be difficult to fully characterize

Digging Deeper in Imaging Surveys APS April Meeting 2015 Eric Suchyta

Digging Deeper in Imaging Surveys APS April Meeting 2015 Eric Suchyta

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Stars or galaxies?

• Blending • Proximity effects • Star-galaxy separation • Measurement software

What is the problem?• Big surveys want to make (sub)

percent level measurements

• Large catalogs (measured sizes, fluxes, etc.) are primary data output

• Images —> catalogs is complex, subject to measurement biases that can be difficult to fully characterize

Digging Deeper in Imaging Surveys APS April Meeting 2015 Eric Suchyta

Digging Deeper in Imaging Surveys APS April Meeting 2015 Eric Suchyta

What is usually done?

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Digging Deeper in Imaging Surveys APS April Meeting 2015 Eric Suchyta

Digging Deeper in Imaging Surveys APS April Meeting 2015 Eric Suchyta

What is usually done?

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FainterBrighter

Turn over as objects get too faint to see

Digging Deeper in Imaging Surveys APS April Meeting 2015 Eric Suchyta

Digging Deeper in Imaging Surveys APS April Meeting 2015 Eric Suchyta

What is usually done?

Limit the study to a range with “easy” systematics

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Use the brightest galaxies,their brightnesses shouldbe less corrupted

Brighter

Digging Deeper in Imaging Surveys APS April Meeting 2015 Eric Suchyta

Digging Deeper in Imaging Surveys APS April Meeting 2015 Eric Suchyta

What is usually done?

OR

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Model the input population and noise, then simulate, and pass the images through survey measurement software (UFIG, Bergé et al. 2013)

Limit the study to a range with “easy” systematics

Brighter

Digging Deeper in Imaging Surveys APS April Meeting 2015 Eric Suchyta

Digging Deeper in Imaging Surveys APS April Meeting 2015 Eric Suchyta

ORModel the input population and noise, then simulate, and pass the images through survey measurement software (UFIG, Bergé et al. 2013)

What is usually done?…And what are the problems?

• Statistics much reduced

• Modelling uncertainties

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Limit the study to a range with “easy” systematics

Brighter

Digging Deeper in Imaging Surveys APS April Meeting 2015 Eric Suchyta

Digging Deeper in Imaging Surveys APS April Meeting 2015 Eric Suchyta

We use a hybrid approach• Balrog software package

(https://github.com/emhuff/Balrog)

• Simulate objects into real DES images using GalSim, run DES measurement pipeline

• Automatically inherit difficult to simulate systematics

• Input distribution comes from COSMOS catalog (HST) (Jouvel et al. 2009, Mandelbaum et al., 2014) Red = Simulated

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Digging Deeper in Imaging Surveys APS April Meeting 2015 Eric Suchyta

Digging Deeper in Imaging Surveys APS April Meeting 2015 Eric Suchyta

Measure the survey likelihood function

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• Factored into is the detection probability

Digging Deeper in Imaging Surveys APS April Meeting 2015 Eric Suchyta

Digging Deeper in Imaging Surveys APS April Meeting 2015 Eric Suchyta

Visualizing the likelihood function

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Digging Deeper in Imaging Surveys APS April Meeting 2015 Eric Suchyta

Digging Deeper in Imaging Surveys APS April Meeting 2015 Eric Suchyta

Visualizing the likelihood function

• Below red line, likelihood well understood

• Above red line, likelihood gets messier

• Need to do analysis like ours if we want to use a larger range of the data

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Most DESanalyses cutting tothis region

Digging Deeper in Imaging Surveys APS April Meeting 2015 Eric Suchyta

Digging Deeper in Imaging Surveys APS April Meeting 2015 Eric Suchyta

To reconstruct the data:

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• Into last bin we incorporate non-detections, with

• Use as the likelihood function in MCMC chain

Digging Deeper in Imaging Surveys APS April Meeting 2015 Eric Suchyta

Digging Deeper in Imaging Surveys APS April Meeting 2015 Eric Suchyta

Running the reconstruction on DES Science Verification images

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Prelim

inary

• Able to reconstruct, even as the detections become incomplete

• Reconstruction- - COSMOS— DES Observed

• - Balrog Observed

Digging Deeper in Imaging Surveys APS April Meeting 2015 Eric Suchyta

Digging Deeper in Imaging Surveys APS April Meeting 2015 Eric Suchyta

Running the reconstruction on DES Science Verification images

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Prelim

inary

Galaxies

Stars

• Reconstruction- - COSMOS— DES Observed

• - Balrog Observed

• Able to reconstruct, even as the detections become incomplete

• Also recovering star/galaxy

Digging Deeper in Imaging Surveys APS April Meeting 2015 Eric Suchyta

Digging Deeper in Imaging Surveys APS April Meeting 2015 Eric Suchyta

Still to come…• More fully incorporate errors in building the likelihood into

the Markov Chain

• Make reconstruction maps and show we do not correlate with other systematics maps (e.g. background variation)

• Go beyond magnitude functions, the formalism is general

• Important: this formalism can be incorporated into any DES measurement and other surveys

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Backup Slides

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Recovering DES with Balrog

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Recovering DES with Balrog

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• Want to recover a spatially uniform galaxy distribution, consistent with cosmic variance

Prelim

inary

Reconstruct in spatial bins (HEALPixels), sum magnitude bins between 22.5 - 24.5

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Prelim

inary

• Want to recover a spatially uniform galaxy distribution, consistent with cosmic variance

Reconstruct in spatial bins (HEALPixels), sum magnitude bins between 22.5 - 24.5