Growth rate from SNIa peculiar velocity and galaxy density field

Corentin Ravoux - CNRS/LPCA Clermont-Ferrand

 

 

 

 

 

Synergistic power of combined cosmological observables - 29 October 2025

 

 

ACCEL² simulation

with Bastien Carreres, Julian Bautista, Damiano Rosselli, Alex Kim and many others

Dark Energy

  • What is the nature of dark energy ?
  • Cosmological constant model (Λ) not very satisfying

Recent DESI DR2 BAO + CMB + SNIa results: stronger hints for varying dark energy

DESI DR2 BAO 2025

Corentin Ravoux                                                                                                                                                                           LPNHE workshop - Oct. 2025

Dark Energy

  • What is the nature of dark energy ?
  • Cosmological constant model (Λ) not very satisfying
  • Other models :
    • Fifth force, additional field ?
    • Modified gravity theories ?
  • Our current objective: Probing large-scale structure formation to constrain dark energy/modified gravity.

Shankaranarayanan et al. 2022

Corentin Ravoux                                                                                                                                                                           LPNHE workshop - Oct. 2025

Cosmic web

  • Matter perturbations in the primordial Universe grew to form the cosmic web
  • Static description: matter density contrast (δ) follows a linear power spectrum at large-scales
  • Dynamic description: Cosmic web peculiar velocities (   ) caused by gravitation

Walther et al. 2021

Chabanier et al. 2019

v_p

Corentin Ravoux                                                                                                                                                                           LPNHE workshop - Oct. 2025

fσ8 parameter

  • Linear perturbation theory:
\nabla \cdot \mathbf{v}(\mathbf{r}) \propto-a H (f \sigma_8) \hat{\delta}(\mathbf{r})

Cosmic web velocities

Normalized matter overdensity

Corentin Ravoux                                                                                                                                                                           LPNHE workshop - Oct. 2025

fσ8 parameter

  • Linear perturbation theory:
\nabla \cdot \mathbf{v}(\mathbf{r}) \propto-a H (f \sigma_8) \hat{\delta}(\mathbf{r})

Cosmic web velocities

Normalized matter overdensity

Logarithmic growth rate of linear perturbations

f=\frac{d \log \delta_{+}}{d \log a} \simeq \Omega_{\mathrm{m}}^{0.55}

8 Mpc/h

Amplitude of the matter perturbations in spheres of 8 Mpc/h comoving radius (matter power spectrum amplitude)

in GR

Corentin Ravoux                                                                                                                                                                           LPNHE workshop - Oct. 2025

Measuring fσ8

Peculiar velocities (PV)

Redshift Space Distortions (RSD)

  • Direct estimation of velocities

 

 

  • Need an estimate of redshift (      ) and distance (decoupling       from     )
  • Compute statistics from PV
1 + z_{\mathrm{obs}} = (1 + z_{\mathrm{cos}})(1 + v_{\mathrm{p}}/c)
  • Impact of PV on measurements
  • Example: galaxy auto-correlation, void cross-correlation, ...
z_{\mathrm{obs}}
z_{\mathrm{cos}}
v_{\mathrm{p}}

Ravoux et al. 2022

He et al. 2018

Corentin Ravoux                                                                                                                                                                           LPNHE workshop - Oct. 2025

fσ8 constraints

  • Constrain modified gravity models
  • RSD very effective for high redshift
  • PV for low redshift
  • Improvement with method combination

from Julian Bautista

Corentin Ravoux                                                                                                                                                                           LPNHE workshop - Oct. 2025

fσ8 measurement with peculiar velocities

ACCEL² simulation

ACCEL² simulation

Corentin Ravoux                                                                                                                                                                           LPNHE workshop - Oct. 2025

Hubble diagram

Perfect standard candle in an expanding Universe

from Bastien Carreres

Standard candle = object with fixed luminosity

Corentin Ravoux                                                                                                                                                                           LPNHE workshop - Oct. 2025

Hubble diagram

Peculiar velocities

from Bastien Carreres

Perfect standard candle in an expanding Universe

Corentin Ravoux                                                                                                                                                                           LPNHE workshop - Oct. 2025

Hubble diagram

Noise sources (intrinsic scatter, local environment, instruments...)

from Bastien Carreres

Standardizable candle in an expanding Universe

Peculiar velocities

Corentin Ravoux                                                                                                                                                                           LPNHE workshop - Oct. 2025

Hubble diagram

from Bastien Carreres

High redshifts = dark energy  constraints (w, or w0,wa)

Low redshifts + calibration of the candle luminosity = H0 constraints

Hubble residuals = fσ8 constraints

Corentin Ravoux                                                                                                                                                                           LPNHE workshop - Oct. 2025

Type Ia supernovae

  • Distance modulus:
\mu_{\mathrm{obs},i}=m_{\mathrm{B},i}-M_{\mathrm{B},i}

Betoule et al. 2014

  • SNe Ia standardization = Tripp relation
M_{\mathrm{B},i} = M_{0} - \alpha x_{1,i} + \beta c_i + (\mathrm{opt})

Light-curve stretch

SN color

Parameters common for all SN Ia

"Redder is fainter"

"Luminous fade slowly"

  • SNe Ia:  Thermonuclear explosion a white dwarf reaching Chandrasekhar mass
\sigma_{D} / D \sim 7 \%

Corentin Ravoux                                                                                                                                                                           LPNHE workshop - Oct. 2025

Type Ia supernovae

  • Hubble residuals:
\begin{align*} \Delta \mu_i &= \mu_{\mathrm{obs},i} - \mu_{\mathrm{model}}(z_{\mathrm{obs},i}) \\ &= \mu_{\mathrm{obs},i} - 5 \log\left({\frac{d_{\mathrm{L}}(z_{\mathrm{obs},i})}{10 \mathrm{pc}}}\right) \end{align*}
  • Velocity estimator:
\hat{v}_i=-\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

Carreres et al. 2023

Corentin Ravoux                                                                                                                                                                           LPNHE workshop - Oct. 2025

Likelihood-based field-level inference

ACCEL² simulation

ACCEL² simulation

Carreres et al. 2023

Ravoux et al. 2025

Corentin Ravoux                                                                                                                                                                           LPNHE workshop - Oct. 2025

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

Parameters

Velocity example:

  • Purpose: Maximizing or sampling a likelihood computed from all coordinates of the data
  • p = fitted parameters: simultaneously cosmology (fσ8) and field parameters (e.g., Hubble diagram parameters α, β, M0 for SN)

Corentin Ravoux                                                                                                                                                                           LPNHE workshop - Oct. 2025

Computing the field covariance

Corentin Ravoux                                                                                                                                                                           LPNHE workshop - Oct. 2025

Combining densities (RSD) 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_{vg}\right)

Corentin Ravoux                                                                                                                                                                           LPNHE workshop - Oct. 2025

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_a}, \mathbf{r_b}) = \frac{1}{(2 \pi)^3} \int_{\mathbf{k}} P_{\mathrm{ab}}(k, \mu_{\mathrm{a}}, \mu_{\mathrm{b}}) e^{i \mathbf{k}.\mathbf{r}} d^3 \mathbf{k}
P_{\mathrm{ab}}(k, \mu_{\mathrm{a}}, \mu_{\mathrm{b}}) = \sum_{n} w_{\mathrm{ab},n} F_{\mathrm{ab},n} (k, \mu_{\mathrm{a}}, \mu_{\mathrm{b}}) \mathcal{P}_{\mathrm{ab},n}(k)

gg, gv, vv

Parameters to fit

Terms to integrate analytically

Power spectra terms to integrate numerically

Corentin Ravoux                                                                                                                                                                           LPNHE workshop - Oct. 2025

flip Field Level Inference Package

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

 

 

https://github.com/corentinravoux/flip

  • Fast generation of field covariance
  • Symbolic code generation for covariance model
  • MCMC and Minuit to fit fσ8
  • General expression for vectors (SNIa, density, log distance, TF, FP...)
  • Survey geometry-dependent Fisher forecast

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

Flip article: arxiv:2501.16852

Corentin Ravoux                                                                                                                                                                           LPNHE workshop - Oct. 2025

Field covariance models

  • Model comparison on a regular coordinate grid (Adams & Blake 2017, Adams & Blake 2020, Lai et al. 2022)

 

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

Ravoux et al. 2025

Corentin Ravoux                                                                                                                                                                           LPNHE workshop - Oct. 2025

Combining DESI galaxy density and ZTF SNIa velocities on simulation

ACCEL² simulation

ACCEL² simulation

 

 

Ravoux et al. in prep

Corentin Ravoux                                                                                                                                                                           LPNHE workshop - Oct. 2025

ACCEL² simulation

Zwicky Transient Facility (ZTF)

  • ZTF = high-cadence photometric telescop in the Palomar observatory
  • Very large field of view (47 deg²)
  • Observing 3/4 of the sky every nights with 3 filters (g, r, i)
  • Transient sky: Supernovae, gamma ray burst, tidal distruptive events, comets, asteroids

  • Dedicated spectroscopic telescope measuring transient spectra (SEDmachine)

Corentin Ravoux                                                                                                                                                                           LPNHE workshop - Oct. 2025

SN Ia simulation and analysis

SN Ia simulator

Survey parameters

SN Ia model

N-body host simulation

Realistic SN Ia light curves

Quality cuts

(Detection, spec-z selection, light curve quality...)

Light curve fit (SALT2)

Velocity estimation and simultaneous fit of fσ8 and velocity parameters

f\sigma_8

Simulation

Analysis

Corentin Ravoux                                                                                                                                                                           LPNHE workshop - Oct. 2025

snsim

https://github.com/bastiencarreres/snsim

  • Survey parameters: Cadence (ZTF, LSST), CCD gain, zero point, sky noise
  • SN Ia model: Rate, SED, color, stretch, intrinsic scattering distributions, light-curve parameters
  • Host catalog: Use velocities and associated clustering from N-body simulation

Flux

Phase

Phase

Phase

Carreres et al. 2023

Main contributors: Bastien Carreres, Damiano Rosselli

Corentin Ravoux                                                                                                                                                                           LPNHE workshop - Oct. 2025

DESI

  • 4m multi-object spectrograph at Kitt Peak Observatory

DESI collaboration 2022

Corentin Ravoux                                                                                                                                                                           LPNHE workshop - Oct. 2025

DESI

  • 5 μm positioning in few sec. for 5000 targets
  • All fibers connected to 10 spectrographs

Silber et al. 2022

Corentin Ravoux                                                                                                                                                                           LPNHE workshop - Oct. 2025

DESI

  • BGS: Bright Galaxy Survey
  • LRG: Luminous Red Galaxy
  • ELG: Emission Line Galaxy
  • QSO: Quasar
    • z > 2.1: with a Lyman-α forest
    • z < 2.1: as tracers

63 million redshifts in 8 years

over 17,000 deg²

Primary science: BAO and RSD measurements

21

12

24

5

Corentin Ravoux                                                                                                                                                                           LPNHE workshop - Oct. 2025

DESI x ZTF simulation

  • 27 simulations from AbacusSummit (Maksimova et al. 2019)
  • Density: DESI Bright Galaxy Survey (BGS) clustering matching for DR3 (J. Bautista, A. Smith)
  • Velocity: ZTF SNIa Y6 simulation performed (following Carreres et al. 2023)

from Julian Bautista

Corentin Ravoux                                                                                                                                                                           LPNHE workshop - Oct. 2025

Galaxy field

Supernovae velocities

  • Runs of SNsim to mimic ZTF SNIa Y6 data set (following Carreres et al. 2023)
  • AbacusSummit mocks matching DESI DR3 (5 years) BGS clustering and footprint

Ravoux et al. in prep.

Corentin Ravoux                                                                                                                                                                           LPNHE workshop - Oct. 2025

Model for density & velocity fit

  • Use of the RC25 wide-angle model implemented in flip:
P_{\mathrm{vv}} = (aHf)^2 \frac{\mu_1 \mu_2}{k^2} P_{\mathrm{\theta\theta}}(k) D_u^2(k, \sigma_u)
P_{\mathrm{gg}}=\left[b^2 P_{\mathrm{mm}}(k)+b f (\mu_1^2 + \mu_2^2) P_{\mathrm{m\theta}}(k)+f^2 \mu_1^2 \mu_2^2 P_{\theta \theta}(k)\right] \exp \left[\frac{-k^2 (\mu_1^2 + \mu_2^2) \sigma_{\mathrm{g}}^2}{2}\right]
P_{\mathrm{gv}} = iaH \frac{\mu_2}{k^2} \left(bf P_{\mathrm{m\theta}}(k) + f^2 \mu_1^2 P_{\mathrm{\theta\theta}}(k) \right) \exp \left[\frac{-k^2 \mu_1^2 \sigma_{\mathrm{g}}^2}{2}\right] D_u(k, \sigma_u)
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}

Corentin Ravoux                                                                                                                                                                           LPNHE workshop - Oct. 2025

ZTF velocity fit

  • Velocity fit including Hubble diagram parameters to reproduce Carreres et al. 2023
  • Variation of analysis parameters (max redshift, SNIa density...)

Ravoux et al. in prep.

Corentin Ravoux                                                                                                                                                                           LPNHE workshop - Oct. 2025

DESI density fit

  • Density grid with pypower
  • Test varying all density gridding parameters: grid size, mesh cell size, interpolation scheme (NGC, CIC, TSC, PCS), footprint (DESI DR3 or no cut)
  • Mean minuit fit on 27 mocks with average error bar

Ravoux et al. in prep.

Corentin Ravoux                                                                                                                                                                           LPNHE workshop - Oct. 2025

DESI x ZTF joint fit

  • SNIa velocity fit with Hubble diagram parameters and density field

Adding density improves fs8 constraints up to 60%

Different density configurations

Ravoux et al. in prep.

Corentin Ravoux                                                                                                                                                                           LPNHE workshop - Oct. 2025

Summary of PV estimations on realistic simulations

ZTF + DESI

(this talk)

ZTF (Carreres et al. 2023)

LSST (Rosselli et al. 2025)

Corentin Ravoux                                                                                                                                                                           LPNHE workshop - Oct. 2025

Conclusion

  • Large modeling and simulation preparatory work for ZTF, LSST and combination with DESI density field
  • Dedicated sofware for simulation (snsim, skysurvey) and analysis (flip)
  • Next steps:
    • Application on data (Pantheon+, ZTF, DEBASS, ATLAS)
    • Simulation of all-sky combination (LSST/4HS+4MOST, ZTF+DESI)
    • Combination with other velocity tracers (DESI TF, FP)

ACCEL² simulation

A word on wide-angle in clustering

\mathbf{r_{1}}
\mathbf{r_{2}}
\mathbf{v_{1}}
\mathbf{v_{2}}
\mathbf{r} = \mathbf{r_{1}} - \mathbf{r_{2}}

Plane parallel models

Wide angle models

\mu = \mu_1 = \mu_2
\mathbf{r_{1}}
\mathbf{r_{2}}
\mathbf{v_{1}}
\mathbf{v_{2}}
\mathbf{r} = \mathbf{r_{1}} - \mathbf{r_{2}}
\theta
\phi
\mu_1 \neq \mu_2
\mu_{i} = \mathbf{k} \cdot \mathbf{r_{i}}
\theta

Plane parallel model

  • Express power spectrum model in a general way:
P_{\mathrm{ab}}(k, \mu) = \sum_{n} w_{\mathrm{ab},n} F_{\mathrm{ab},n} (k, \mu) \mathcal{P}_{\mathrm{ab},n}(k)
  • vv "simple" example:
P_{\mathrm{vv}} = (f\sigma_8)^2 \times \left(a^2 H^2 \frac{\mu^2}{k^2}\right) \times \left(P_{\mathrm{\theta\theta}}(k) D_u^2(k, \sigma_u)\right)

Parameters to fit

Terms to integrate analytically

Power spectra terms to integrate numerically

Plane parallel model

  • Without loosing generality we can express the covariance:
C_{\mathrm{ab}}(\mathbf{r_1}, \mathbf{r_2}) = \sum_n w_{\mathrm{ab},n} \sum_{\ell} N_{\mathrm{ab},\ell}(\phi) \mathcal{H}_\ell\left[\mathcal{P}_{\mathrm{ab},n}(k)M_{\mathrm{ab},n}^{\ell}(k)\right](r)
M_{\mathrm{ab},n}^{\ell}(k) = \frac{1}{2}\int_{-1}^{1}d\mu^\prime F_{\mathrm{ab},n}(k, \mu^\prime)L_{\ell}(\mu^\prime)
N_{\mathrm{ab},\ell}(\phi) =(2\ell +1)L_\ell(\cos\left(\pi - \phi\right))

Plane parallel model

C_{\mathrm{ab}}(\mathbf{r_1}, \mathbf{r_2}) = \sum_n w_{\mathrm{ab},n} \sum_{\ell} N_{\mathrm{ab},\ell}(\phi) \mathcal{H}_\ell\left[\mathcal{P}_{\mathrm{ab},n}(k)M_{\mathrm{ab},n}^{\ell}(k)\right](r)

Linear sum of matrix with parameters to fit as coefficients

  • Without loosing generality we can express the covariance:

Plane parallel model

C_{\mathrm{ab}}(\mathbf{r_1}, \mathbf{r_2}) = \sum_n w_{\mathrm{ab},n} \sum_{\ell} N_{\mathrm{ab},\ell}(\phi) \mathcal{H}_\ell\left[\mathcal{P}_{\mathrm{ab},n}(k)M_{\mathrm{ab},n}^{\ell}(k)\right](r)

Hankel transform

\mathcal{H}_\ell\left[f(k)\right](r) = i^\ell \int_{0}^\infty\frac{k^2 dk}{2\pi^2}j_\ell(kr)f(k)
  • Most important part: Algorithmically optimized way to compute power spectrum integral, with FFTLog algorithm
  • Without loosing generality we can express the covariance:

Plane parallel model

  • Without loosing generality we can express the covariance:
C_{\mathrm{ab}}(\mathbf{r_1}, \mathbf{r_2}) = \sum_n w_{\mathrm{ab},n} \sum_{\ell} N_{\mathrm{ab},\ell}(\phi) \mathcal{H}_\ell\left[\mathcal{P}_{\mathrm{ab},n}(k)M_{\mathrm{ab},n}^{\ell}(k)\right](r)

Terms pre-computed analytically with symbolic code generaltion

  • More complicated formalism derived and implemented for wide-angle
M_{\mathrm{ab},n}^{\ell}(k) = \frac{1}{2}\int_{-1}^{1}d\mu^\prime F_{\mathrm{ab},n}(k, \mu^\prime)L_{\ell}(\mu^\prime)
N_{\mathrm{ab},\ell}(\phi) =(2\ell +1)L_\ell(\cos\left(\pi - \phi\right))

Wide angle model

\xi_{\mathrm{ab},\ell}^{i}(r) = \sum_j N_{\mathrm{ab},\ell}^{i,j} (\theta, \phi) \left[ i^{\ell} \int_k \frac{k^2 dk}{2 \pi^2} P_{\mathrm{ab},i} (k) M_{\mathrm{ab},\ell}^{i,j} (k) j_{\ell}(k r)\right]
C_{\mathrm{ab}}(\mathbf{r_1}, \mathbf{r_2}) = \sum_i A_{\mathrm{ab},i} \sum_l \xi_{\mathrm{ab},\ell}^{i}(r)
M_{\mathrm{ab},\ell}^{i,j=(\ell_1,\ell_2)} (k) = \int_{\mu_1 = -1}^1 \int_{\mu_2 = -1}^1 \frac{1}{4} L_{\ell_1}(\mu_1)L_{\ell_2}(\mu_2) B_{\mathrm{ab},i} (k, \mu_1, \mu_2) d \mu_1 d \mu_2
N_{\mathrm{ab},\ell}^{i,j=(\ell_1,\ell_2)} (\theta, \phi) = (4 \pi)^2 \sum_{m, m_1, m_2} G_{l, l_1, l_2}^{m, m_1, m_2} Y_{\ell m}(\hat{\mathbf{r}})^* Y_{\ell_1 m_1}(\hat{\mathbf{r_1}})^* Y_{\ell_2 m_2}(\hat{\mathbf{r_2}})^*
G_{m_1, m_2, m_3}^{l_1, l_2, l_3}=\sqrt{\frac{\left(2 l_1+1\right)\left(2 l_2+1\right)\left(2 l_3+1\right)}{4 \pi}} \left(\begin{array}{ccc} l_1 & l_2 & l_3 \\ 0 & 0 & 0 \end{array}\right)\left(\begin{array}{lll} l_1 & l_2 & l_3 \\ m_1 & m_2 & m_3 \end{array}\right)
P_{\mathrm{ab}}(k, \mu_1, \mu_2) = \sum_{i} A_{\mathrm{ab},i} B_{\mathrm{ab},i} (k, \mu_1, \mu_2) P_{\mathrm{ab},i}(k)

linear sum of matrix

flip validations

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

Disagreement due to different power spectrum model

Ravoux et al. 2025

Ravoux et al. 2025

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

Howlett et al. 2017

Ravoux et al. 2025

Rubin - LSST

Telescope mount assembly

AuxTel: 1.2m atmospheric telescop

Rubin - LSST: instrument

  • Largest numerical camera in the world: 3.2 Gpixels with 2s readout time
  • 8.4 m primary mirror and 9.6 deg² field of view
  • 6 filters: (u, g, r, i, z, y)
  • 10-year photometric survey of half of the sky (~ 20 000 deg²)

ZTF results

  • SN Ia selection bias causes velocity & fσ8 bias at high redshift
  • No fσ8 error bar improvement even with correction
  • Maximum redshift usable:
z =0.06

Carreres et al. 2023

Carreres et al. 2023

ZTF results

  • fσ8 fit performed with Hubble diagram parameters
  • no fσ8 bias on the 27 simulations
  • 19 % precision measurement with only 1600 SNe Ia

Carreres et al. 2023

Carreres et al. 2023

LSST simulations

  • 10 year simulation from Uchuu-UniverseMachine (Behroozi et al. 2019)
  • Cadence and instrument properties from the observing strategy simulation v3.3

Rosselli et al. 2025

LSST simulations

  • SN host redshift efficiency (C. Ravoux, P. Gris, D. Rosselli, J. Bautista):
    • Use of DC2 LSST simulation with galaxy properties
    • Footprint and efficiency for different spectroscopic survey
    • Considering SNe variety contamination

Rosselli et al. 2025

LSST simulations

  • 33,000 SNe Ia with spectro-z at z < 0.16 (among 1M for the full sample)
  • Photo-typing with SuperNNovae (Moller et al. 2019) gives 0.02% contamination from peculiar SN

Rosselli et al. 2025

LSST results

  • Dedicated study to test BBC framework and check the impact of intrinsic scatter model and nuisance parameter (      = dispersion of velocity position)

Carreres et al. 2025

\sigma_{\mathrm{u}}

LSST results

  • Unbiased fσ8 measurement with full Hubble diagram fit over 3 redshift points
  • How can we do better ? Combine with density field

Rosselli et al. 2025