1 multi-wavelength, multi-epoch heritage of stripe 82 | princeton, nj | march 17, 2014 name of...

1MULTI-WAVELENGTH, MULTI-EPOCH HERITAGE OF STRIPE 82 | PRINCETON, NJ | MARCH 17, 2014Name of Meeting • Location • Date - Change in Slide Master

LSST Reprocessed Co-adds of SDSS Stripe 82Yusra AlSayyad University of Washington

MULTI-WAVELENGTH, MULTI-EPOCH HERITAGE OF STRIPE82MARCH 17, 2014

Andrew Becker, Andrew Connolly, Željko Ivezić,Simon Krughoff,Russell Owen,(U. Washington)

Jim Bosch, Robert Lupton, Paul Price, (Princeton),Mario Jurićand the LSST Data Management Team

3MULTI-WAVELENGTH, MULTI-EPOCH HERITAGE OF STRIPE 82 | PRINCETON, NJ | MARCH 17, 2014

CORE DATA PROCESSING SOFTWARE PLAN REVIEW | SEATTLE, WA | SEPTEMBER 19-20, 2013

Outline

− How do we aggregate information from multi-epoch surveys, with the goal of extracting highest S/N possible. • Detect on co-add• Measure on single-epoch exposures (forced photometry)

− Summer 2013 co-add construction details• Co-addition process• Datasets produced• Preliminary quality assessment



Three ways combine info from multi-epoch surveys

1. Catalog co-add: Detect and measure on single-epoch exposures and take a weighted mean or median of the measurements.


Light-curves derived from detecting on single-epoch images misses information

Time

Flux

5σ detectionlimit

MeasuredMean Flux




1. Catalog Co-add: Detect and measure on single-epoch exposures and take a weighted mean or median of the measurements.

2. Image Co-add: Detect and measure on co-added image

Lose measurements under detection threshold



Measurement on Co-adds misses information

1. Poor-seeing and bright-sky images do not increase your co-add SNR.

2. Co-add PSFs are hard to characterize. 3. Impossible to distinguish between galaxies and high proper

motion stars.

PSF 3P

SF

2Psf 1

Image 2Image 1 Image 3COADD

Lang+ 09

Individual exposures: objects are undetected or marginally detected

Moving point-source and galaxy models are indistinguishable on the coadd

Solved by PSF-Matching: (Kaiser 04) (Huff+11)




1. Catalog Co-add: Detect and measure on single-epoch images and take a weighted mean or median of the measurements.

2. Image Co-add: Detect and measure on co-added image

3. Detect on co-added image. Measure on single-epoch exposures.

Misses measurements under detection threshold

Requires PSF-matching. Can’t characterize objects.



Summer 2013 Stripe 82 Co-addition



Summer 2013 Stripe 82 Co-add Process

ucoadd

gcoadd

rcoadd

icoadd

zcoadd

Catalog

catalog

catalog

catalog40M rows

catalog

Detect and Measure

Detect and Measure

Detect and Measure

Detect and Measure

Detect and Measure

Detect and Measure with: • StackFit/CoaddPSF• Deblender• 3σ detection threshold

• Measure flux at positions in i-band co-added catalog

Details next

Forced Photometry

Catalog15 B rows

~80 epochs per band

Force photometry at positions


Steps to make a deep co-add

− Define the co-add projection and geometry

For each run:1. Resample to defined output geometry2. Scale to co-add zeropoint


2. Scale to a co-add zeropoint

− Zeropoint varies spatially and temporally

− Photometric self-calibration calculated before co-adds are generated

− Used 1-D model of zeropoint (per camcol) as multiplicative scale factor. Figure: Realization of a 0.5 mag cloud based on a structure

function of observed clouds and some realistic assumptions about cloud-size and velocity. (image credit: Lynne Jones)

RA & time

Dec



− Define the co-add projection and geometry

For each run:1. Resample to defined output geometry2. Scale to co-add zeropoint 3. Match sky-levels/backgrounds



Background-subtracted coadd Single-epoch visit

5 arcmin



Background-subtracted coadd Background-matched coadd

5 arcmin


3. Match Backgrounds

− Goal: Estimate the difference in sky level between successive runs• Leave in common-mode (astrophysical) background

- Wings of galaxies, diffuse nebulosity • Remove time-dependent backgrounds

- Atmospheric: changes in sky, airglow, scattered moonlight

− One realization of the sky survives, but we can subtract this at higher S/N

− (see Huff+ 11)


3. Match Backgrounds

− Start with a reference image. • Locks the sky-level of a co-add patch to

that of its neighbors• Ensures the co-add will be seamless at

patch boundaries• Use a run with low background variation

as a reference image

Reference Image for example patch



− Define the coadd projection and geometry

For each run:1. Resample to defined output geometry2. Scale to co-add zeropoint 3. Match sky-levels/backgrounds

For ensemble of resampled & scaled visits:4. Stack co-add

- Weight whole image by inverse variance and stack with 5σ-clipped mean for eliminating:• moving objects, transients, satellite trails• Small-scale instrumental: glints

Figure: 5 sq. deg. background-matched coadd composite

(g,r,i)~55 epochs

Region: AqrGalactic lat = -35.0



Summer 2013 Stripe 82 Co-add Process

ucoadd

gcoadd

rcoadd

icoadd

zcoadd

Catalog

catalog

catalog

catalog40M rows

catalog

Detect and Measure

Detect and Measure

Detect and Measure

Detect and Measure

Detect and Measure

Detect and Measure with: • StackFit/CoaddPSF• Deblender• 3σ detection threshold

• Measure flux at positions in i-band co-added catalog

Details next

Forced Photometry

Catalog15 B rows

~80 epochs per band

Force photometry at positions



Preliminary Quality Assessment



Median zeropoint is stable

W

X

Zeropoints calibrated using the Ivezic+ 07 star catalog. (See talk on Wednesday)

Ivezic+04

Point sources



RMS approaches intrinsic width of w ~ 0.01

r mag

Median PSF flux in epochs



Forced phot. adds 0.5 mag to SNR=5 depth



Dataset is publicly available. Access?

− What’s available?• 5 sq. degree region is indexed and ready to use today.• 235 sq. degree region from previous data challenge available

- g,r,i, no deblending, forced photometry seeded on r-band

− How to access? • LSST Science Collaboration members can request a mysql account and

explore the databases hosted at NCSA• Send me a list of positions (< 10M rows) and I’ll send you an ascii or fits

file with forced photometry and aggregates

− Code is open source. − Caveat (prototype code):

• No forced galaxy photometry. • Deblender not dependable in blends containing stars < 17mag.



Summary

− Stripe 82 has proved to be a great dataset for testing LSST software.

− Result of Summer 2013 production run is deeper than best publicly-available optical Stripe82 catalog.

− Forced Photometry:• Complete light curves for even marginally detected objects • Disentangles extended and moving sources • Includes all epochs (not just the best seeing, lowest background)



Appendix



i-band Completeness



Completeness

HST SDSS



Star galaxy separation



False galaxies are unresolved blends



False stars are compact


Winter 2013 comparison to previous coadd

We go deeper than Annis et al. (2012) by 0.75 magnitudes at same

equivalent S/N.

SNR

100

10

24 25


Quasar Luminosity Function at z ~ 4


Level 2 Data Processing

Data Products Definitions DocumentSection 5.2


3. Background Matching

− For N input images, find least sq. solution of all N(N-1)/2 difference images (e.g. Huff et al. 2011) (Presented as “NN2”- algorithm by Barris et al. 2009)• Offers better relative offsets in sky-levels, but does not provide an

absolute sky-level • NN2 does not eliminate need for a reference image.

- If you choose the mean you get a patchwork quilt.- If you choose zero, you lose all astrophysical background on scales greater

than a patch• We tested both NN2 and simple ref-image. Preliminary conclusion is that

the gains are not significant. • We will re-examine on the ecliptic where the reference image may be

heavily masked due to asteroids


Level 2 Processing

DRP begins with processing, detection, and measurement on single frames, generating Sources.

These are used to photometrically and astrometrically calibrate the survey.

A series of coadds is built next, where Objects are detected.

Detections on co-adds are deblended and associated to form a master object list.

The objects are simultaneously characterized in all observed epochs (MultiFit).

Time variability is characterized by independent measurement of Forced Sources in individual epochs.


Types of coadds planned in LSST

Coadd Type

Subset of images Driver Bands Pre-served

Deep All suitable images Detect objects on static sky. Maintaining low surface brightness.

ugrizy + M ✔

Short Period

Binned by year Detect long-timescale or moving objects that would be washed out in full-depth coadds

ugrizy + M ✗

Best-Seeing

Only best seeing images (e.g. top quartile)

Assist deblending ugrizy ✗

PSF-matched

All suitable images Measure well-defined colors of galaxies

ugrizy ✗

Binned by seeing and airmass

Image Differencing Templates ugrizy ✔

1 multi-wavelength, multi-epoch heritage of stripe 82 | princeton, nj | march 17, 2014 name of...

Documents

multiepoch surveys1

singleepoch exposures

image co

singleepoch images

catalog co

sdss stripe

coadded image

lsst data management