CHUN-HAO TO PRO
I am a cosmologist!
*chto@uchicago.edu
Challenges and Opportunities for Roman Weak Lensing Cosmology
Chun-Hao To*
I also work on...
Lensing around dwarf galaxies
Weak lensing and cluster cosmology
Cosmological simulations
Weak lensing
Text
Weak lensing
DES+25, 26, Bocquet+ (incl. CT) 25, and many many more.
Weak lensing
An extremely powerful probe of the co-evolution of galaxies and dark matter halos.
To+19,25, Zacharegkas+26, Leauthaud+11, Zu&Mandelbaum16, Cacciato+09, Yoo+06, Thornton+13, and many many more.
LSST Y5
Roman Medium-Tier
- Space-based weak lensing experiments in infrared.
- Undersampled PSF: Pixel size and PSF size are comparable.
- Correlated noise.
- Poor spec-z coverage.
- Theory uncertainties:
Reshift evolution of IA and baryonic feedback from z=0-3.
What are the unique challenges for Roman compared to other ground based lensing survey?
Roman photometry problem
- 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.
Flux uncertainty correction
Assuming the noise is homogeneous and isotropic within a block (1.6'x1.6'), we can estimate the correlation using the correlation of the noise realization.
r(\Delta\alpha,\Delta\beta) = \left\langle \bar{n}(\alpha+\Delta\alpha,\beta+\Delta\beta) \bar{n}(\alpha,\beta) \right\rangle
where the normalized noise field is defined as:
\bar{n}(\alpha,\beta) = \sqrt{w_{\alpha,\beta}} \, n(\alpha,\beta)
Flux uncertainty correction
Photometry measurement
- Following the approach of the Farmer:
- Detection: SEP
- Photometry: Multi-object Model fitting (MOF) using the Tractor.
- With the following major improvements:
- Correlated noise correction.
- Fine-tune model decision trees for the Roman image.
- Noise from astronomical objects.
Also see 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...
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
- Space-based weak lensing experiments in infrared.
- Undersampled PSF: Pixel size and PSF size are comparable.
- Correlated noise.
- Poor spec-z coverage.
- Theory uncertainties:
Reshift evolution of IA and baryonic feedback from z=0-3.
What are the unique challenges for Roman compared to other ground based lensing survey?
- Space-based weak lensing experiments in infrared.
- Undersampled PSF: Pixel size and PSF size are comparable.
- Correlated noise.
- Poor spec-z coverage.
- Theory uncertainties:
Reshift evolution of IA and baryonic feedback from z=0-3.
What are the unique challenges for Roman compared to other ground based lensing survey?
Photo-z algorithm SOM-PZ
- An unsupervised learning algorithm that characterizes galaxies into different phenotypes (cells).
- Our ability to characterize galaxies depends on photometry.
- Two cells are used, c and chat, corresponding to the Roman deep and medium tier.
Photo-z algorithm SOM-PZ
n(z | b) = \sum_{\hat{c} \in b}\sum_{c} p(z|c) p(c|\hat{c})p(\hat{c})
Each medium-Tier galaxy has a probability of being assigned to a wide SOM cell
Photo-z algorithm SOM-PZ
n(z | b) = \sum_{\hat{c} \in b}\sum_{c} p(z|c) p(c|\hat{c})p(\hat{c})
Each medium-Tier galaxy has a probability of being assigned to a wide SOM cell
Each deep-Tier galaxies has an estimation of redshift distribution
Photo-z algorithm SOM-PZ
n(z | b) = \sum_{\hat{c} \in b}\sum_{c} p(z|c) p(c|\hat{c})p(\hat{c})
Each medium-Tier galaxy has a probability of being assigned to a wide SOM cell
Each deep-Tier galaxies has an estimation of redshift distribution
Transfer function obtained via galaxy injections
Photo-z algorithm SOM-PZ
n(z | b) = \sum_{\hat{c} \in b}\sum_{c} p(z|c) p(c|\hat{c})p(\hat{c})
Each medium-Tier galaxy has a probability of being assigned to a wide SOM cell
Each deep-Tier galaxies has an estimation of redshift distribution
Transfer function through galaxy injections
What it looks like in simulations
Challenges
19% wide and 18% deep galaxies are in cells with no spec-z samples
Challenges
19% wide and 18% deep galaxies are in cells with no spec-z samples
Our solution
deep SOM
wide SOM
- Bin assignment: majority bin assignment of spec-z + photo-z samples, for all cells
- Bin edges: spec-z + photo-z Jenks natural breaks
Our solution
deep SOM
wide SOM
- Bin assignment: majority bin assignment of spec-z + photo-z samples, for all cells
- Bin edges: spec-z + photo-z Jenks natural breaks
Original binning
New binning
Our solution
deep SOM
wide SOM
- Bin assignment: majority bin assignment of spec-z + photo-z samples, for all cells
- Bin edges: spec-z + photo-z Jenks natural breaks
Our solution
deep SOM
wide SOM
New binning
- Interpolation between cells?
Our solution
deep SOM
wide SOM
New binning
- Interpolation between cells?
SOM does not preserve distance
Our solution
deep SOM
wide SOM
New binning
- Interpolation between cells?
UMAP + Normalizing flow interpolation
deep SOM
Umap interp
New binning
+ UMAP interp
Impacts
If we spend many many nights on PFS to get representative spec-z.
If we use the existing spec-z for Roman, we will have to throw away 19% of the galaxy samples.
Impacts
If we spend many, many nights on PFS to get a representative spec-z.
If we try to advance our algorithms...
Why should we care?
DES+26
Text
If we can use all the scales, we could gain about 50% of the constraining power.
DES fiducial
Status:
Dalal+ (incl. CT) 26
- Simulations give a wide range of predictions.
- Data seems to prefer a stronger feedback scenario.
Status
-
Lots of data exist and have been used to constrain feedback. Here are a very incomplete list:
-
WL: Ario+22, Anbajagane+25, DES+26, Xu+25.
-
tSZxtSZ: Raghunathan+26, Chaubai+26, Efstathiou&McCarthy25.
-
tSZxothers: Sanchez+22, Dalal, CT+25, Pandey+25.
-
kSZ x others: Hadzhiyska+24, Bigwood+25, Ropper+25, Hotinli, Smith, Ferraro 25.
-
X-ray: Kovac+25, Eckert+25, Siegel+25, Zhang+26.
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FRB: Reischke & Hazstotz25, Wang+25.
-
QSO absorption: Chen & Zahedy26, Qu+23, 24, Zahedy+19.
-
Status
-
The modeling landscape:
-
Baryonification-flavored models that parametrize the gas distribution around halos at a given redshift.
Schneider+19,25, Arico+20, Anbajagane+24, Osato+23, Williams+23, Semboloni+11,13, Fedeli+14, Debackere+20, Mead+20, Giri+21, Pandey (incl. CT)+24, and many more...
-
Hydrodynamic simulations.
McCarthy+17, Schaller+25, Springel+18, Quataert&Hopkins+25, and many, many more...
-
What is missing?
- Most of the baryon probes involve galaxies.
- Clusters rely on galaxies for redshift and selections.
- kSZ stacks on galaxy samples.
- FRB needs to be cross-correlated with galaxies if DM (host) is unknown.
- Weak lensing is sensitive to a broad redshift range.
- Future weak lensing surveys (such as Roman) will be sensitive to z=0-3.
What is missing?
Tumlinson+17
Cold gas around galaxies is the fuel of star formation.
Galaxy properties and gas content should be correlated.
What we need for Roman?
A model that self-consistently describes galaxy evolution and gas properties across z=0-3.
Diffsky model
To+ in prep (with diffsky team)
Galaxies:
Jax-based galaxy model
Alarcon+23, Hearin+21
Diffsky model
To+ in prep (with diffsky team)
Galaxies:
Jax-based galaxy model
Godmax: (Pandey+ (incl. CT) 24)
Baryonification-flavored gas models implemented in Jax.
Diffbaryon
Alarcon+23, Hearin+21
Diffbaryon model
To+ in prep (with diffsky team)
Preliminary
Hydro simulation
Diffbaryon
Diffbaryon model
To+ in prep (with diffsky team)
Preliminary
Hydro simulation
Diffbaryon
Preliminary
- Diffbaryon captures
- Godmax model underestimates the intrinsic scatter.
Central
P(M_{gas}|M_{halo})
- Diffbaryon captures
Goddard seminar
By CHUN-HAO TO
