Impact of Baryonic Feedback on Cosmological Constraints from Weak Lensing

Andreas Tersenov

Deep CosmoStat Days, Feb 12, 2026

For which we have/assume an analytical likelihood function

t = f(x)

How to constrain cosmological parameters?

Likelihood → connects our compressed observations to the cosmological parameters

\underbrace{p\left(\theta \mid t=t_0\right)}_{\text {posterior }} \propto \underbrace{p\left(t=t_0 \mid \theta\right)}_{\text {likelihood }} \underbrace{p(\theta)}_{\text {prior }}

p\left(t=t_0 \mid \theta\right)

Credit: Justine Zeghal

2pt vs higher-order statistics

Using Power Spectra for constraining cosmological parameters misses the non-Gaussian information in the field.

t = f(x)

DES Y3 Results

Credit: Justine Zeghal

Higher Order Statistics: Peak Counts

Peaks: local maxima of the SNR field
Peaks trace regions where the value of 𝜅 is high → they are associated to massive structures

Multi-scale (wavelet) peak counts

Baryonic effects

Effects that stem from astrophysical processes involving ordinary matter (gas cooling, star formation, AGN feedback)
They modify the matter distribution by redistributing gas and stars within halos.

Suppress matter clustering on small scales
Depend on the cosmic baryon fraction and cosmological parameters.
Must be cut/modeled/marginalized over to avoid biases in cosmological inferences from WL.

Baryonic impact on LSS statistics

baryonic effects in P(k)

Credit: Giovanni Aricò

cosmoGRID:

Power Spectrum

Wavelet l1-norm: sum
of wavelet coefficients
within specific amplitude
ranges across different
wavelet scales

Wavelet peaks: local maxima of wavelet
coefficient maps

N-body sims, providing DMO & baryonified full-sky κ-maps.

Baryonic effects are incorporated using a shell-based Baryon Correction Model.

Density Estimation

Normalizing Flows (NF) are based on mapping functions

Those functions enable us to map a latent variable z∼pz(z) to a variable x∼px(x).

We can approximate distributions with NFs by learning this function

(discretize the problem into learning the parameters of a series of bijections)

Normalizing Flows for Density Estimation

f: \mathbb{R}^n \rightarrow \mathbb{R}^n

Inference method: SBI

Training objective

\mathcal{L} = - \log p_\phi ( \theta | x )

https://github.com/sachaguer/jaxili

The Scaling of Baryonic Bias with Survey Area

Determining Robust Scale Cuts

Information Content at Large Scales

Results

Weak lensing tomography

Credit: Justine Zeghal

Weak lensing tomography

Credit: Justine Zeghal

Weak lensing tomography

Credit: Justine Zeghal

Weak lensing tomography

Credit: Justine Zeghal

Weak lensing tomography

Credit: Justine Zeghal

Weak lensing tomography

Credit: Justine Zeghal

Weak lensing tomography

Credit: Justine Zeghal

BNT transform

When we observe shear, contributions come from mass at different redshifts.
BNT Transform: method to “null” contributions from unwanted redshift ranges.
It reorganizes weak-lensing data so that only specific redshift ranges contribute to the signal.
BNT aligns angular (ℓ) and physical (k) scales.
This could help mitigate baryonic effects by optimally removing sensitivity to poorly modeled small scales and controlling scale leakage.

BNT maps

no BNT

BNT

How are statistics impacted?

Power Spectrum

l1-norm

* This could help mitigate baryonic effects by optimally removing sensitivity to poorly modeled small scales and controlling scale leakage?

Take a look at the maps again..

PS without scale cuts

PS with scale cuts

BNT vs Standard contours

Peaks

l1-norm

Mass mapping and cosmological inference with higher-order statistics

Andreas Tersenov

ARGOS-TITAN-TOSCA workshop, July 8, 2025

Why this presentation may not be the best

Weak Lensing - Relation between

\kappa \,\, \mathrm{and} \,\, \gamma

From convergence to shear:
From shear to convergence:

\gamma_i = \hat{P}_i \kappa

\kappa = \hat{P}_1 \gamma_1 + \hat{P}_2 \gamma_2

\hat{P}_1(k)=\dfrac{k_1^2 - k_2^2}{k^2}, \,\, \hat{P}_2(k)=\dfrac{2k_1k_2}{k^2}

In practice...

Shear measurements are discrete, noisy, and irregularly sampled
We actually measure the reduced shear
Masks and integration over a subset of ℝ2 lead to border errors ⇒ missing data problem
Convergence is recoverable up to a constant ⇒ mass-sheet degeneracy problem

Mass mapping is an ill-posed inverse problem

Different algorithms have been introduced, with different reconstruction fidelities, in terms of RMSE

Motivating this project:

The various algorithms have different RMSE performance
In cosmology we don't care about RMSE of mass maps, but only about the resulting cosmological parameters

⇒ This should be our final benchmark!

So... does the choice of the mass-mapping algorithm have an impact on the final inferred cosmological parameters?

Or as long as you apply the same method to both observations and simulations it won't matter?

cosmoSLICS mass maps

\begin{array}{ll} \hline \hline \text{Method} & \text{RMSE} \downarrow \\ \hline \text{KS } & 1.1 \times 10^{-2} \\ \text{iKS } & 1.1 \times 10^{-2} \\ \text{MCALens} & 9.8 \times 10^{-3} \\ \hline \end{array}

For which we have/assume an analytical likelihood function

t = f(x)

How to constrain cosmological parameters?

Likelihood → connects our compressed observations to the cosmological parameters

\underbrace{p\left(\theta \mid t=t_0\right)}_{\text {posterior }} \propto \underbrace{p\left(t=t_0 \mid \theta\right)}_{\text {likelihood }} \underbrace{p(\theta)}_{\text {prior }}

p\left(t=t_0 \mid \theta\right)

2pt vs higher-order statistics

The traditional way of constraining cosmological parameters misses the non-Gaussian information in the field.

t = f(x)

DES Y3 Results

Impact of Baryonic Feedback on Cosmological Constraints from Weak Lensing

Andreas Tersenov

Deep CosmoStat Days, Feb 12, 2026

Higher Order Statistics: Peak Counts

Peaks: local maxima of the SNR field
Peaks trace regions where the value of 𝜅 is high → they are associated to massive structures

Multi-scale (wavelet) peak counts

Results

Mono-scale peaks

Multi-scale peaks

Where does this improvement come from?

Kaiser-Squires

MCALens

Baryonic effects

Effects that stem from astrophysical processes involving ordinary matter (gas cooling, star formation, AGN feedback)
They modify the matter distribution by redistributing gas and stars within halos.

Suppress matter clustering on small scales
Depend on the cosmic baryon fraction and cosmological parameters.
Must be modeled/marginalized over to avoid biases in cosmological inferences from WL.

Baryonic impact on LSS statistics

baryonic effects in P(k)

Credit: Giovanni Aricò

Project: Testing impact baryonic effects on WL HOS

Idea - Explore two things:

Information content of summary statistics as a function of scale cuts
Testing the impact of baryonic effects on posterior contours

This will show:

On what range of scales can the different statistics be used without explicit model for baryons
Answer the question: how much extra information beyond the PS these statistics can access in practice

cosmoGRID simulations

Power Spectrum

Wavelet l1-norm

Inference method: SBI

Power spectrum vs l1-norm (scale: ~10arcmin)

What about the baryonic effects? Do we have any bias?

l1-norm, scale 1 (~10arcmin)

l1-norm, scale 2 (~20arcmin)

Weak lensing tomography

BNT transform

When we observe cosmic shear, contributions come from mass at different redshifts.
This creates projection effects: large and small-scale structures get mixed up.
These effects make it harder to accurately analyze data and extract information

BNT transform

BNT Transform: A method to “null” or remove contributions from unwanted redshift ranges.
It reorganizes the weak-lensing data so that only specific redshift ranges contribute to the signal, making it easier to analyze.
It focuses on isolating lensing contributions by sorting out overlapping signals.

How are statistics impacted?

What about contours?

Scale 1 (~7arcmin)

Multiscale

Why this reduction in constraining power?

no BNT

BNT

Hope: Neural Summaries (VMIM)