Likelihood-based inference of growth rate with SNIa velocity and galaxy density fields

Corentin Ravoux - LPCA Clermont-Ferrand, France

with Bastien Carreres, Damiano Rosselli, Julian Bautista, Anthony Carr, Tyann Dummerchat, Alex G. Kim, David Parkinson, Benjamin Racine, Dominique Fouchez and Fabrice Feinstein

 

 

 

 

Cosmic Flows 2025

 

 

ACCEL² simulation

Arxiv: 2501.16852

Likelihood-based inference of growth rate with SNIa velocity and galaxy density fields

Corentin Ravoux - LPCA Clermont-Ferrand, France

with Bastien Carreres, Damiano Rosselli, Julian Bautista, Anthony Carr, Tyann Dummerchat, Alex G. Kim, David Parkinson, Benjamin Racine, Dominique Fouchez and Fabrice Feinstein

 

 

 

 

Cosmic Flows 2025

 

 

ACCEL² simulation

Arxiv: 2501.16852

Coco

Likelihood-based field-level inference

Corentin Ravoux                                                                                                                                                                    Cosmic Flows 2025 - February 6

ACCEL² simulation

ACCEL² simulation

Likelihood-based field-level estimator

\begin{aligned} \mathcal{L}(p) \propto & \exp\left[-\frac{1}{2} v^T(p) C_{vv}(p)^{-1} v(p)\right] \\ \end{aligned}

Velocity from SNe Ia

Field covariance matrix computed from theory and coordinates

  • Purpose: Maximizing or sampling a likelihood computed from all coordinates of the data
  • p = parameters: contains cosmology and velocity/density parameters

Parameters

Velocity example: Carreres et al. 2023

Corentin Ravoux                                                                                                                                                                    Cosmic Flows 2025 - February 6

Combining densities and peculiar velocities

\begin{aligned} \mathcal{L}(p) \propto & \exp\left[-\frac{1}{2} \right. \end{aligned}

Velocities

Density field from galaxy surveys

Full field covariance matrix

\delta
\delta
v
v
\Bigg]
-1
T
\left(C_{gg}\right)
\left(C_{gv}\right)
\left(C_{vv}\right)
\left(C_{gv}\right)

Corentin Ravoux                                                                                                                                                                    Cosmic Flows 2025 - February 6

Computing the field covariance

Corentin Ravoux                                                                                                                                                                    Cosmic Flows 2025 - February 6

General covariance calculation

  • Objective: Improve/Extend the likelihood-based modeling
  • General form of field power spectrum
  • Algorithmically-oriented calculations (Hankel transforms)
  • Wide-angle effect accounted in the integration
C_{\mathrm{ab}}(\mathbf{r_1}, \mathbf{r_2}) = \frac{1}{(2 \pi)^3} \int_{\mathbf{k}} P_{\mathrm{ab}}(k, \mu_1, \mu_2) e^{i \mathbf{k}.\mathbf{r}} d^3 \mathbf{k}
P_{\mathrm{ab}}(k, \mu) = \sum_{i} w_{\mathrm{ab},i} F_{\mathrm{ab},i} (k, \mu) \mathcal{P}_{\mathrm{ab},i}(k)

Corentin Ravoux                                                                                                                                                                    Cosmic Flows 2025 - February 6

gg, gv, vv

flip Field Level Inference Package

  •  python package for likelihood-based field-level inference:

 

 

https://github.com/corentinravoux/flip

  • Symbolic code generation for covariance model
  • Fast generation of field covariance
  • MCMC and Minuit fitter
  • General expression for vectors (SNIa, density, log distance...)
  • Covariance model evaluation
  • Survey geometry-dependent Fisher forecast

Flip article: arxiv:2501.16852

Corentin Ravoux                                                                                                                                                                    Cosmic Flows 2025 - February 6

Contributors: Bastien Carreres, Damiano Rosselli and Alex G. Kim

Field covariance models

  • Model comparison on a regular coordinate grid

 

  • New wide-angle model developed (RC25): improved stability in integration and for wider fitting parameter ranges

Corentin Ravoux                                                                                                                                                                    Cosmic Flows 2025 - February 6

Validating flip

  • Field covariance comparison with pairV Anthony Carr & David Parkinson
  • Model validated on N-body simulations  Tyann Dummerchat

Disagreement due to different power spectrum model

Corentin Ravoux                                                                                                                                                                    Cosmic Flows 2025 - February 6

Application: Survey dependent Fisher forecast

  • Field covariance matrix accounts for survey geometry
  • Comparing ZTF simulation (Carreres et al. 2023) with Fisher forecasts
  • Error ~ 30% closer to likelihood-based than standard Fisher

Corentin Ravoux                                                                                                                                                                    Cosmic Flows 2025 - February 6

Damiano Rosselli

Howlett et al. 2017

Application: combining DESI galaxy density and ZTF SNIa velocities on simulation

ACCEL² simulation

Corentin Ravoux                                                                                                                                                                    Cosmic Flows 2025 - February 6

Abacus BGS mocks

  • 27 AbacusSummit mocks matching to the DESI-BGS clustering

 

  • ZTF SNIa Y6 simulation performed (following Carreres et al. 2023)

 

  • Objective: Assessing growth rate measurement on a DESI Y5 / ZTF Y6 simulation

Corentin Ravoux                                                                                                                                                                    Cosmic Flows 2025 - February 6

Galaxy field

  • Runs of SNsim to mimic ZTF Y6 SN Ia field

Supernovae velocities

https://github.com/bastiencarreres/snsim

  • DESI Y5 BGS footprint and density field gridding

Corentin Ravoux                                                                                                                                                                    Cosmic Flows 2025 - February 6

ZTF velocity fit

  • New likelihood-based model in flip
  • Velocity fit including Hubble diagram parameters (Carreres et al. 2023)

 

 

 

  • Variation of analysis parameters (max redshift, SNIa density...)
\hat{v}_i\left(\mathbf{p}_{\mathrm{HD}}\right)=-\frac{\ln (10) c}{5}\left(\frac{\left(1+z_i\right) c}{H\left(z_i\right) r\left(z_i\right)}-1\right)^{-1} \Delta \mu_i\left(\mathbf{p}_{\mathrm{HD}}\right)

Corentin Ravoux                                                                                                                                                                    Cosmic Flows 2025 - February 6

\begin{split} \Delta \mu_i (\mathbf{p}_{\mathrm{HD}}) &=m_{B,i} - M_0 + \alpha x_{1,i} - \beta c_i \\ &- \left( 5 \log\left[(1+ z_{\mathrm{obs},i})D(z_{\mathrm{obs},i})\right] + 25 \right) \end{split}

DESI density fit

  • Density grid with pypower
  • Mean minuit fit on 27 mocks with average error bar
  • Test varying all density gridding parameters: grid size, mesh cell size, interpolation scheme (ngc, cic, tsc, pcs), footprint (Y5 or full)

Corentin Ravoux                                                                                                                                                                    Cosmic Flows 2025 - February 6

DESIxZTF joint fit

  • ZTF Y6 SNIa velocity fit with Hubble diagram parameters and DESI Y5 BGS density field

Adding density improves fs8 constraints up to 30%

Different density configurations

Corentin Ravoux                                                                                                                                                                    Cosmic Flows 2025 - February 6

Preliminary

Preliminary

Conclusion

  • flip = Framework for likelihood-based field-level inference
  • Growth rate analysis on simulation with ZTF SNIa and DESI density field
  • Next steps: improvement to non-linear models & combination with other DESI tracers (PV, TF)

ACCEL² simulation