An exploration of dark energy and beyond the standard model with analytical and numerical tools for the large scale structures of the universe

Santiago Casas

Postdoctoral Researcher

TTK, RWTH Aachen University

@santiagocasas @sant87casas

santiagocasas.github.io

Dark Energy & Modified Gravity

The Horndeski Lagrangian

PhD thesis: Non-linear structure formation in models of Dark Energy and Modified Gravity, http://archiv.ub.uni-heidelberg.de/volltextserver/23120/

Beyond Einstein's GR

Scalar-Tensor theories

Effect on LSS

N-Body simulations

CONCEPT: Python and Neutrinos

GADGET2/CoDECS

Credit: Yun Ling, My bachelor student,

Jeppe Dakin (U. Zurich)

Credit: Dr. Marco Baldi, Master thesis co-supervisor

Approximate methods: COLA

ABACUS: Fits for GCspectro with 1loopEFT

Other codes tested:

gevolution

flowpm

GNQ: Growing Neutrino Quintessence (developed in Heidelberg)

Credit: Winther, SC, Koyama, et al (2019)

Credit: SC, Führer, Ayaita, Weber, Wetterich

Credit: Rademacher, Moradinezhad, Lesgourges, SC

My Journey with Euclid

Credits: www.esa.int/Science_Exploration/Space_Science/Euclid, www.euclid-ec.org, ESA/NASA/SpaceX, Euclid Consortium

Euclid consortium scientist visits Cannes. Credits: ThalesAlenia Space

Euclid preparation: VII. Forecast validation for Euclid cosmological probes,,Blanchard et al. arXiv:1910.09273

★ Awardees of the Euclid STAR Prize Team 2019

EC Builder Status achieved 2023

Early Release Observations. ECICOM, ECEPO: Social media manager of instagram: @euclidconsortium

Euclid Launch: 1st July 2023

Large forecasting projects

The Fingertip Galaxy

WEAK Lensing and Galaxy Clusteering

SC, Lesgourgues, Schöneberg, et al., Euclid: Validation of the MontePython forecasting tools, 2303.09451

SC, Kunz, Martinelli, Pettorino, Phys.Dark Univ. 18 1703.01271

3x2pt Photometric Cosmic Shear + Clustering

Spectroscopic Galaxy Clustering: BAO+RSD+DM+LSS+Galaxy bias

Excellent complimentarity

https://www.esa.int/Science_Exploration/Space_Science/Euclid/Euclid_test_images_tease_of_riches_to_come

FISHER Matrix FORECASTS

Code: CosmicFish

S.Casas, M.Martinelli and M.Raveri

Soon to be released: Full pythonic version

https://github.com/santiagocasas/cosmicfishpie

Cosmomathica/Fishermathica

Python, Numpy-intensive

Couples to CAMB, CLASS, HiCLASS, MGCAMB, input4cast files

GC, WL, 21cm-IM, 3x2pt, CMB

Euclid, DESI, Rubin LSST, SKAO

Wolfram Language,

Cosmomathica-FORTRAN link for CAMB

GC, WL

Euclid, SKA1

Used to produce and validate IST:F forecasts 2015-2019

Hessian of a Gaussian Likelihood. Used to approximate posterior distributions at the maximum (fiducial value)

First code to be validated against MontePython MCMC forecasts

SC, Lesgourgues, Schöneberg, et al., Euclid: Validation of the MontePython forecasting tools, 2303.09451

Euclid preparation: VII. Forecast validation for Euclid cosmological probes,,Blanchard et al. arXiv:1910.09273

SC, Kunz, Martinelli, Pettorino, Phys.Dark Univ. 18 1703.01271

https://github.com/santiagocasas/cosmomathica

Euclid theory working group

SC, I. Tutusaus : Work Package lead of WP6, Forecasting and statistics

SC: Member of WP1,2,3,4,6,7,11 : From theory and non-linearities to likelihood

SC: Co-coordinator of the KeyPaper-Theory-1 project

\rm{d}s^2 = -(1+2\Psi) \rm{d}t^2 + a^2(1-2\Phi) \rm{d}x^2

G_{\rm eff}=\left(1+ \frac{2\beta^2(\phi_0)}{Z(\phi_0)}e^{-m(\phi_0)r}\right) G_N

\newcommand{\sg}{\ensuremath{\sigma_{8}}} \newcommand{\de}{\mathrm{d}} S = \frac{c^4}{16\pi G} \int{\de^4 x \sqrt{-g} \left[R+f(R)\right]}

SC, Cardone, Sapone, et al, Euclid: Constraints on f(R) cosmologies from the spectroscopic and photometric primary probes, 2306.11053

Frusciante, Pace, Cardone, SC et al, Euclid: Constraining linearly scale-independent modifications of gravity with the spectroscopic and photometric primary probes, 2306.12368

Main tasks:

Constrain gravitational potentials

Test for screening mechanisms

Check theories of modified gravity

Requires expertise with modified Einstein-Boltzmann solvers,

Emulators, Halo-model,

Fisher and likelihoods

Euclid: Likelihood and Nonlinear TASKFORCES

GCspectro: EFT 1-loop RSD in multipoles
3x2pt photo:
- Covariance, nonlinear Super-Sample
- Galaxy Bias expansion
- Emulators for nonlinear, HMCode, Halofit, Bacco, EuclidEmu
- Baryonic Feedback: different parameters

Different emulators with systematic offsets
Euclid error bars can distinguish among them

EUCLID PRELIMINARY

Credit: P. Carrilho for IST:NL

Credit: SC, for IST:NL

Cosmological Likelihood for Observables in Euclid

Credit: SC, for IST:L

Credit: SC, for IST:NL

SC: Main developer and core member of CLOE. KeyPaper co-lead of 3x2pt in IST:NL

Preparation of DR1 analysis

Biasing when failing to analyse data with correct model of baryonic feedback

Other Surveys and CROss-correlations

Vera Rubin Observatory, LSST Project Office - http://www.lsst.org/gallery/telescope-rendering-2013

DESI telescope in Tucson, Arizona, in the Schuk Toak District on the Tohono O’odham Nation

Square Kilometer Array Observatory https://www.skao.int/

SC, Carucci, Pettorino et al (2022), Constraining gravity with synergies between radio and optical cosmological surveys, 2210.05705

CMB Stage-IV experiments: https://kipac.stanford.edu/research/projects/cmb-stage-4

Invited talk at the Manchester Optical x Radio Synergy meeting

Neutrinos and LSS with Euclid & more

Euclid Preparation: Sensitivity to Neutrino parameters. (Under internal review). Archidiacono, Lesgourgues, SC, Pamuk, et al.

EUCLID PRELIMINARY

\(M_{\nu}\) and \(N_{\rm eff}\)
Combination of different probes breaks degeneracies

For our given \(\ell_{\rm max}\) the effect of baryonic feedback is small <10%
Euclid (all) 1\(\sigma\) error: 30-60 meV

Text

future avenues of work

Merci!!

In collaboration with Johanna Schaffmeister and Sven Günther, students at RWTH

More work on uncertainty-aware emulators. https://github.com/santiagocasas/looti
Both for non-linear power spectra, as well as for baryonic feedback, MG-effects and neutrinos.
Work on 1loop-EFT for LSS in the case of GC-spectro (currently in prep. with Linde, Lesgourgues and Moradinezhad. Inclusion of consistent treatment for Neutrinos and Modified Gravity. (in collab with Linde, Pietroni, Fidler).
JAX-COSMO: Campagne, Lanusse, Zuntz, SC, et al, 2302.05163.
Implement auto-differentiability into commonly used codes in the field: CLASS, CAMB, HMCode, Looti, and Euclid codes: CLOE.
Use auto-diff emulators: CosmoPower (Spurio-Mancini, et al)
SC: Invited participant at 3-month MIAPP Differentiable physics workshop
This allows for fast, efficient variational parameter estimation, orders of magnitude faster than conventional tools. Data compression, automatic Fishers, optimization, etc....
Careful study of the effect of emulators and baryonic feedback on the Euclid 3x2pt observables. Parameter estimation biases, efficient sampling, sensible priors....
Preparation of consistent (and alternative) pipelines for Euclid data release analysis, including cross-correlations with Clusters, Voids or other non-Gaussian observables and correlations with other surveys such as CMB, radio 21cm IM, SNIa and gravitational wave sirens.