CoS_SIG_HLIS

*chto@uchicago.edu

Weak Lensing Cosmology

and Roman HLIS-PIT

Chun-Hao To*, and the HLIS PIT

Roman

Survey Year

Galaxy Density

Survey Area

18000

154

1500

5000

1400

15000

(\small{\rm{deg}^2})

(\small{\rm{arcmin}^{-2}})

154

5100

End Date

Start Date

CFHTLS

COSMOS

DLS

KIDS

DES

HSC

Euclid

Rubin

Weak lensing surveys

Weak lensing cosmology probes

Weak lensing cosmology today

Dark Energy Survey Collaboration et al. 2026 (paper1, 2)

\Lambda \text{CDM}

w_0w_a\text{CDM}

What will Roman do for WL cosmology

A deep catalog of galaxies with exquisite PSF (~0.2") and multiband coverage (0.9-2.0 ).

Many galaxies low noise

Less blending/star-galaxy separation

Great photo-z when combined with Rubin LSST

\mu m

NASA GFSC

Roman High Latitude Wide Area Survey

Roman

Survey Year

Galaxy Density

Survey Area

18000

15000

(\small{\rm{deg}^2})

(\small{\rm{arcmin}^{-2}})

5100

Start Date

Euclid

Rubin

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/ for details.

Shear and color measurement.
1. Internal and external individual-exposure calibration and point-spread function modeling.
2. Image Coaddition.
3. Shear measurement, photometry, and forced photometry on Rubin.
4. Image simulations and synthetic source injections.
Catalog and statistics.
1. Photometric redshift estimation and characterization.
2. Lens sample selection and characterization.
3. Galaxy cluster selection and characterization.
4. 2pt measurement and uncertainty quantifications.
Cosmological Parameter Inference.
1. Covariance matrix.
2. Baryon and nonlinear power spectra modeling.
3. Intrinsic alignment models.
4. Galaxy bias models.
5. Cluster systematic models.
6. 3x2pt likelihood inferences.
7. Mass mapping and higher-order statistics.

Pixel-to-cosmology pipeline (more complete)

Shear and color measurement.
1. Individual exposure calibration.
2. Image Coaddition.
3. Shear measurement.
4. Photometry and forced photometry on Rubin.
Catalog and statistics.
1. Photometric redshift estimation and characterization.
2. Galaxy sample selection.
3. Galaxy cluster sample selection.
Cosmological Parameter Inference.

Pixel-to-cosmology pipeline (less complete)

Level 1: Uncalibrated Images

1a. Individual exposure calibration

Amy Albert, Ami Choi, Nihar Dalal, Chris Hirata, Mike Jarvis, Katherine Laliotis...

1a. Individual exposure calibration

Laliotis+25

Katherine Laliotis, Chris Hirata

imDestripe

Level 2: Calibrated Images

1b. Image Coaddition

Kali Cao, Chris Hirata, Emily Macbeth, Masaya Yamamoto

The point spread function is undersampled: Pixel size ~= PSF size.
- Solution: a specialized image co-addition algorithm (IMCOM) to ensure that the output PSF is exactly Gaussian and uniform across the footprint.

Imcom PSF

Drizzle PSF

See also: Rowe+11, Hirata+24, Yamamoto+24, Cao+25

PSF Size Error

Level 3: Mosaics

1c. Shear Measurement

Metadetection: was used for DES Y6 cosmology.

Axel Guinot, Sidney Mau, Rachel Mandelbaum

\langle e \rangle = \langle \frac{\partial e}{\partial \gamma} \gamma\rangle

\langle \frac{\partial e}{\partial \gamma} \rangle = \frac{\langle e^{+}\rangle -\langle e^{-}\rangle}{\Delta \gamma}

Shape: we measured

Shear: we want

1d. Photometry and forced photometry on Rubin

Chun-Hao To, Tae-Hyeon Hsin

Slimfarmer: a multi-band, multi-object fitting pipeline.
LSST-DM: a software package for Rubin data processing.

Out of the box uncertainty

Slimfarmer uncertainty

To+ in prep.

Products: Shear and galaxy catalogs

Position

Shear

Photometry

2a. Photometric redshift estimation and characterization.

Individual photo-z: For galaxy and galaxy cluster selections (Rail-Lephare).
Ensemble photo-z: For n(z) characterization (Roman-SOMPZ).

Brett Andrews, Dan Masters, Jeff Newman, Diogo Souza, Chun-Hao To, Boyan Yin, YuFei Zhen

19% wide and 18% deep galaxies are in cells with no spec-z samples

Self-Organizing Map (SOM)

2a. Solution 1: more spectroscopic galaxies.

Brett Andrews, Jeff Newman, Dan Masters

Cosmos DDF

XMM DDF

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

Looking for collaborators for the proposal!
email Jeff Newman (janewman@pitt.edu)

2a. Interim Approach: Augmenting spec-z training sets for SOMPZ

YuFei Zhen, Boyan Yin, Chun-Hao To

Out-of-the-box DES SOMPZ

Roman-SOMPZ

See also Souza+26 on modeling uncertainties.

True n(z)

Estimated n(z)

Zhen+ in prep.

Products: n(z) and redshift

redshift of each object

n(z) and redshift bins

2b. Galaxy sample selections.

Navin Chaurasiya, Michael Troxel

We are exploring two types of samples:

Magnitude-limit / Flux limit samples

2c. Galaxy cluster sample selections.

Dhayaa Anbajagane, Chihway Chang, Kevin Hong, HyeongHan Kim, Charlie Mpetha, Pranjal R.S., Chun-Hao To

We are exploring three types of samples:

red-sequence-based clusters (redMaPPer).
photo-z-based clusters (WaZP).
external catalogs.

Products: lens galaxies and cluster samples

3. Cosmological Parameter Inference (CPIP)

2pt measurements

External correlated and uncorrelated measurements.

CPIP

ML-based likelihood inference framework:

Extremely fast.
Detailed code comparison with CCL.
Lots of developments in intrinsic alignment, baryon, and observational systematic models.

3. Cosmological Parameter Inference (CPIP)

Summary data vectors

External correlated and uncorrelated measurements

CPIP

ML-based likelihood inference framework:

Extremely fast.
Lots of developments of IA, baryon, and observational systematic models

Shear and color measurement.
1. Individual exposure calibration.
2. Image Coaddition.
3. Shear measurement.
4. Photometry and forced photometry.
Catalog and statistics.
1. Photometric redshift estimation and characterization.
2. Galaxy sample selection.
3. Galaxy cluster sample selection.
Cosmological Parameter Inference.

Pixel-to-cosmology pipeline (recap)

Level 1: Uncalibrated Images

Level 2: Calibrated Images

Level 3: Mosaics

Products: Shear and photometry

Products: n(z) and redshift

Lens and cluster samples

Shear and color measurement.
1. Individual exposure calibration.
2. Image Coaddition.
3. Shear measurement.
4. Photometry and forced photometry.
Catalog and statistics.
1. Photometric redshift estimation and characterization.
2. Galaxy sample selection.
3. Galaxy cluster sample selection.
Cosmological Parameter Inference.

Shear and color measurement.
1. Individual exposure calibration.
2. Image Coaddition.
3. Shear measurement.
4. Photometry and forced photometry.
Catalog and statistics.
1. Photometric redshift estimation and characterization.
2. Galaxy sample selection.
3. Galaxy cluster sample selection.
Cosmological Parameter Inference.

Shear and color measurement.
1. Individual exposure calibration.
2. Image Coaddition.
3. Shear measurement.
4. Photometry and forced photometry.
Catalog and statistics.
1. Photometric redshift estimation and characterization.
2. Galaxy sample selection.
3. Galaxy cluster sample selection.
Cosmological Parameter Inference.

Cosmology constraints

Pipeline

Data

Data release

We strive to make the best weak lensing products and analysis tools for the community. For more details, see: Link.
Early data release (~early 2028):
- HLWAS observations through July 2027, most likely to be the COSMOS deep field in Y107, J129, and H158 bands.
- Subset of data, prioritizing for pipeline validation and early science explorations.
First data release (~mid 2028):
Full COSMOS and XMM deep fields.
Early science based on early data release (~late 2028).

Early Science:

LET'S CONNECT

We want to enable the community to use Roman HLWAS lensing data for studies in cosmology and astrophysics.

Webpage

roman-hlis-cosmology.caltech.edu

GitHub

Roman-HLIS-Cosmology-PIT