*chto@uchicago.edu

Synergies between Roman and Rubin: Weak Lensing point of view

Chun-Hao To*

Roman High Latitude Imaging Survey Project Infrastructure Team (HLIS-PIT)

  • We build pipelines to enable Roman's 3x2pt + cluster science.
    • Shear catalogs and calibrations (such as m, photo-z, ...). 
    • Lens and cluster catalogs and calibrations (such as weights, photo-z, ...). 
    • Associated products for the above, such as coadd images, PSF, theory pipeline...
  • See https://roman-hlis-cosmology.caltech.edu/page/products  for expected products and timeline. 
  • Wavelength coverage

    •  

Synergies between Roman and Rubin

https://rtn-011.lsst.io/

  • Wavelength coverage
    • Roman will help Rubin Photo-z

Synergies between Roman and Rubin

  • Wavelength coverage
    • Rubin will help Roman Photo-z

Synergies between Roman and Rubin

  • Wavelength coverage
    • Both Roman and Rubin need spec-z for photo-z. 

Synergies between Roman and Rubin

Link

Cosmos DDF

XMM DDF

20k-30k spectra with H depth of ~24.5 (AB), subsampled to have flatter mag distribution.

Contact:
Brett Andrews, Jeff Newman,
Dan Master.

  • Wavelength coverage
    • Photo-z. 

Synergies between Roman and Rubin

  • Wavelength coverage
    • Photo-z.
  • Resolution: 
    • Rubin: PSF ~0.7" FWHM 
    • Roman: ~0.1" FWHM 

Synergies between Roman and Rubin

We are expecting to get to this depth in COSMOS DDF ~ summer 2027

Link

Performance: Rubin

Rubin Y5

Roman Medium Tier

Rubin detection

Roman detection

  • Wavelength coverage

    • Photo-z.

  • Resolution:

    • Deblending.

    • Star-galaxy separation.

Synergies between Roman and Rubin

See also:

  • Roman Core Community Surveys White Paper: Coordinating Roman and Rubin for Cosmic Probes of Dark Matter with Resolved Stellar Populations (Bechtol+23).

  • OpenUniverse2024: A shared, simulated view of the sky for the next generation of cosmological surveys (Openuniverse+ 24).

  • Forced photometry using Rubin coadd images based on Roman detections. 

One step toward pixel-level synergies between Roman and Rubin

What are the unique challenges for Roman photometry? 

  • Ground-based telescope (LSST/DES):

     Sky background ~ 2000 e/p/s vs Read noise ~ 8.8 e/p
           Poisson shot noise.
  • Space-based telescope (Roman/JWST):

    Zodiacal+thermal ~ 0.76 e/p/s vs Read noise ~ 8.5 e/p
          readout noise is no longer negligible, which can be correlated across pixels (1/f noise). 

Bagley+ 2022

Snowballs (Cosmic Ray)

JWST

1/f

WISP
(Stray light)

See also, Rauscher+22, Betti+ 24

Laliotis+ 25

ImDeStripe: method subtracting 1/f noise using multiple overlapping observations 

Even with this, correlation remains

Generated with remaining noise powerspectrum  

Impact on flux uncertainty

MonteCarlo Simulation 

Out-of-the-box Flux Uncertainty.

Photometry measurement

  • (DES) Fitvd
  • (LSST) LSST-DM
  • SourceXTractor++
     
  • (COSMOS-2020) The Farmer​
    • Detection:
      SEP 
    • Photometry:
      Tractor: Multi-object Model fitting (MOF)

Tae-Hyeon Shin is working on this

+ Lots of other things:
Such as Mosaic / divide skys into chunks, etc...

Weaver+23

The Farmer: 

  • Detection:
    SEP 
  • Photometry:
    Tractor (Multi-object Model fitting)

Weaver+23

+ Lots of other things:
Such as Mosaic / divide skys into chunks, etc...

The Farmer    SlimFarmer

  • Remove all the irrelevant features. 
  • The following major improvements:
    • Correlated noise correction. 
    • Fine-tune model decision trees for the Roman image. 
    • Noise from astronomical objects.

+ Lots of other things:
LSST forced photometry with the same model.

Mosaic boundary and clean duplicated detections.

Astronomy 101: How to calculate flux error? 

\text{Noise} \approx \sqrt{N_{\text{galaxy}} + N_{\text{sky}} + N_{\text{readout}}^2}
  • The Farmer and LSST-DM: From the image itself
  • Fitvd: Estimate from residual map.  
  • Slimfarmer:
    • Estimated from the modeled flux at each optimization step.
    • The noise is scaled using residual map (empirical way to correct additional noise in DES)

Performance validation: DC25 Sim

RA

DEC

  • Input: OpenUniverse24.
     
  • A series of imaging process pipelines from raw data to coadded images with a uniform PSF. 

Performance: Roman

Performance: Roman

\frac{\text{model} - \text{data}}{\text{noise}}

Performance: Roman Color

Out-of-the-box uncertainty

Our method

Color Error

Performance: Crowdedness 

More Crowded

Less Crowded

Color Error

Forced photomety

  • Rubin coadd: Obtain the coadd image from the Butler, which contains WCS.
     
  • Forced photometry:
    • Take groups of Roman detections (in ra, dec). 
    • Rotate them to Rubin coadd using WCS (done in Tractor)
    • Vary their fluxes and positions with a position prior of 0.06" (0.3 of the pixel size). 
    • Jointly fit each group of galaxies. 

Performance: Forced Rubin Color

Color Error

Performance: Crowdedness 

Do we need Multi-object fitting (MOF)?

Single object fitting (SOF) gives color almost as good as MOF

Do we need Multi-object fitting (MOF)?

Single object fitting (SOF) gives significantly worse photometry than  MOF in crowded region

Forced Rubin Color

Roman

Rubin

 

Roman

Rubin

 

Roman

Rubin

 

MOF

MOF

MOF

SOF

SOF

SOF

  • Multi-object fitting (MOF): Fit overlapping objects jointly
  • Single-object fitting (SOF): Fit each object seperately 

Default

Multi-object fitting vs Single object fitting

More Crowded

Less Crowded

Multi-object fitting vs Single object fitting

More Crowded

Less Crowded

Multi-object fitting vs Single object fitting

More Crowded

Less Crowded

Implications: Photo-z

$ ceci ../yaml/test_all.yaml

Roman-SOMPZ: Built on rail-sompz

  • Uncertainty quantification.
  • Much improved structure.

One single command
 

photometry → n(z) + uncertainty

Roman-SOMPZ Team

Implications: Photo-z

  • Ignoring correlated noise has a small impact on SOMPZ.
  • LSST band is important (even with caveats of simulations)

Conclusions

  • Slimfarmer is a simple photometry pipeline that only does detection and photometry 
    • Correlated noise 
    • Multi-object fitting (MOF)
    • Forced Rubin photometry 
    • Tuned to work on Roman-like images
  • Using SlimFarmer, DC25 sim, and Roman-SOMPZ, we find:
    • MOF is only important for absolute photometry but not colors
    • Failure to account for correlated noise can underestimate flux uncertainties by several factors, but has only a small impact on SOMPZ.
    • Rubin photometry will be important for Roman weak lensing photo-z. 

Strong synergies between Roman and Rubin

  • Multiwavelength coverage:
    • Photo-z
       
  • Resolutions:
    • Deblending 
    • Star-galaxy separation. 

One step toward joint pixel processing

  • Slimfarmer is built to perform Roman photometry and Rubin forced photometry.
     
  • Correlated Noise: Failure to account for this will significantly underestimate flux uncertainties, but has minimal impact on SOMPZ.
    ​
  • Multi-object fitting is needed to obtain unbiased colors.
     
  • Rubin photometry: Essential for Roman weak lensing photo-z.