DESI 2024: Survey overview and cosmological constraints from DR1 BAO and Full Shape measurements
Arnaud de Mattia
CEA Saclay
CPPM
November 2024
Thanks to our sponsors and
72 Participating Institutions!
DESI 3D Map
Physics program
- Galaxy and quasar clustering
- Lyman-alpha forest
- Clusters and cross-correlations
- Galaxy and quasar physics
- Milky Way Survey
- Transients and low-z
DESI 3D Map
Physics program
- Galaxy and quasar clustering
- Lyman-alpha forest
- Clusters and cross-correlations
- Galaxy and quasar physics
- Milky Way Survey
- Transients and low-z
DESI: a stage IV survey
10 years = \(10 \times \)
DESI Y5 galaxy samples
Bright Galaxies: 14M (SDSS: 600k)
0 < z < 0.4
LRG: 8M (SDSS: 1M)
0.4 < z < 0.8
ELG: 16M (SDSS: 200k)
0.6 < z < 1.6
QSO: 3M (SDSS: 500k)
Lya \(1.8 < z\)
Tracers \(0.8 < z < 2.1\)
Y5 \(\sim 40\)M galaxy redshifts!
\(z = 0.4\)
\(z = 0.8\)
\(z = 0\)
\(z = 1.6\)
\(z = 2.0\)
\(z = 3.0\)
From images to redshifts
imaging surveys (2014 - 2019) + WISE (IR)
target selection
spectroscopic observations
spectra and redshift measurements
Mayall Telescope
focal plane 5000 fibers
wide-field corrector
6 lenses, FoV \(\sim 8~\mathrm{deg}^{2}\)
Kitt Peak, AZ
4 m mirror
Mayall Telescope
focal plane 5000 fibers
fiber view camera
ten 3-channel spectrographs
49 m, 10-cable fiber run
Kitt Peak, AZ
Focal plane: 5000 robotic positioners
Focal plane: 5000 robotic positioners
Exposure time (dark): 1000 s
Configuration of the focal plane
CCD readout
Go to next pointing
140 s
0.1 mm
Spectroscopic pipeline
wavelength
fiber number
\(z = 2.1\) QSO
\(z = 0.9\) ELG
Ly\(\alpha\)
CIV
CIII
[OII] doublet at \(2727 \AA\) up to \(z = 1.6\)
[OII]
Ly\(\alpha\) at \(1216 \AA\) down to \(z = 2.0\)
DESI data release 1 (DR1)
Observations from May 14th 2021 to June 12th 2022
Final survey
- dark time (LRG, ELG, QSO): 7 layers
- bright time (BGS): 5 layers
- 14,000 \(\mathrm{deg}^2\)
Release of DESI DR1 (BAO) results
April 4th 2024
First batch of DESI DR1 cosmological analyses
https://data.desi.lbl.gov/doc/papers/
• DESI 2024 I: First year data release
• DESI 2024 II: Sample definitions and two-point clustering statistics
• DESI 2024 III: BAO from Galaxies and Quasars
• DESI 2024 IV: BAO from the Lyman-Forest
• DESI 2024 V: Full Shape measurements from Galaxies and Quasars
• DESI 2024 VI: Cosmological constraints from BAO measurements
• DESI 2024 VII: Constraints from the Full Shape measurements
Release of DESI DR1 (FS) results
November 17th 2024
Second batch of DESI DR1 cosmological analyses
https://data.desi.lbl.gov/doc/papers/
• DESI 2024 I: First year data release
• DESI 2024 II: Sample definitions and two-point clustering statistics
• DESI 2024 III: BAO from Galaxies and Quasars
• DESI 2024 IV: BAO from the Lyman-Forest
• DESI 2024 V: Full Shape measurements from Galaxies and Quasars
• DESI 2024 VI: Cosmological constraints from BAO measurements
• DESI 2024 VII: Constraints from the Full Shape measurements
Clustering analysis
galaxy catalog
galaxy power spectrum (or correlation function)
cosmological constraints
compression = "we measure specific features"
"variance of the density field as a function of scale"
Full Shape (baseline)
BAO
ShapeFit (alternative Full Shape)
Baryon acoustic oscillations
Sound waves in primordial plasma
At recombination (\(z \sim 1100\))
- plasma changes to optically thin
- baryons decouple from photons
- sound wave stalls
spherical shell in the distribution of galaxies, of radius the distance that sound waves travelled
= sound horizon scale at the drag epoch \( r_\mathrm{d} \sim 150 \; \mathrm{Mpc} \sim 100 \; \mathrm{Mpc}/h \)
standard ruler
BAO measurements
- transverse to the line-of-sight: \(D_\mathrm{M}(z) / r_\mathrm{d}\)
- along the line-of-sight: \(D_\mathrm{H}(z) / r_\mathrm{d} = c / (H(z) r_\mathrm{d}) \)
z
\theta_\mathrm{BAO} = r_\mathrm{d} / D_\mathrm{M}(z)
transverse comoving distance
sound horizon \(r_d\)
BAO measurements
- transverse to the line-of-sight: \(D_\mathrm{M}(z) / r_\mathrm{d}\)
- along the line-of-sight: \(D_\mathrm{H}(z) / r_\mathrm{d} = c / (H(z) r_\mathrm{d}) \)
z
\Delta z_\mathrm{BAO} = r_\mathrm{d} / D_\mathrm{H}(z)
Hubble distance
sound horizon \(r_d\)
- transverse to the line-of-sight: \(D_\mathrm{M}(z) / r_\mathrm{d}\)
- along the line-of-sight: \(D_\mathrm{H}(z) / r_\mathrm{d} = c / (H(z) r_\mathrm{d}) \)
At multiple redshifts \(z\)
BAO measurements
z_1
z_2
z_3
Probes the expansion history, hence the energy content
Absolute size at \(z = 0\): \(H_0 r_d\)
Release of DESI DR1 (BAO) results
April 4th 2024
First batch of DESI DR1 cosmological analyses
https://data.desi.lbl.gov/doc/papers/
• DESI 2024 I: First year data release
• DESI 2024 II: Sample definitions and two-point clustering statistics
• DESI 2024 III: BAO from Galaxies and Quasars
• DESI 2024 IV: BAO from the Lyman-Forest
• DESI 2024 V: Full Shape measurements from Galaxies and Quasars
• DESI 2024 VI: Cosmological constraints from BAO measurements
• DESI 2024 VII: Constraints from the Full Shape measurements
Correlation functions
BAO peak
Excess probability to find 2 galaxies separated by a distance s
Power spectra
BAO wiggles
Fourier transform of the correlation function
Some fits: correlation function
isotropic measurement
anisotropic measurement
\propto (D_{\mathrm{M}}^{2}(z) D_\mathrm{H}(z))^{1/3} / r_\mathrm{d}
\propto D_{\mathrm{M}}(z) / D_\mathrm{H}(z)
Density field reconstruction
Non-linear structure growth and peculiar velocities blur and shrink (slightly) the ruler
Reconstruction: estimate Zeldovich displacements from observed field and moves galaxies back \(\rightarrow\) refurbishes the ruler (improves precision and accuracy)
reconstruction
Density field reconstruction
DR1 BAO analysis: what's new?
- Biggest ever spectroscopic BAO dataset (\(N_\mathrm{tracer}\) and \(V\))
5.7 million unique redshifts
Effective volume \(V_\mathrm{eff} = 18 \; \mathrm{Gpc}^{3}\)
\(3 \times \) bigger than SDSS!
- Biggest ever spectroscopic BAO dataset (\(N_\mathrm{tracer}\) and \(V\))
- Blind analysis to mitigate observer / confirmation biases (catalog-level blinding)
- Theory developments in BAO fitting code
- New and improved reconstruction methods
- New combined tracer method used for overlapping galaxy samples (LRG and ELG in \(0.8 < z < 1.1\))
- Unified BAO pipeline applied to all (discrete) tracer / redshift bins consistently
DR1 BAO analysis: what's new?
Tests of systematic errors
Considered many possible sources of systematic errors using simulations and data:
- observational effects (imaging systematics, fiber collisions)
- BAO reconstruction (2 algorithms compared)
- covariance matrix construction
- incomplete theory modelling
- choice of fiducial cosmology
- galaxy-halo (HOD) model uncertainties
no systematics detected
systematics << statistics
Maximum effect: \(\sigma_\mathrm{stat. + syst.} < 1.05 \sigma_\mathrm{stat.}\)
Release of DESI DR1 (BAO) results
April 4th 2024
First batch of DESI DR1 cosmological analyses
https://data.desi.lbl.gov/doc/papers/
• DESI 2024 I: First year data release
• DESI 2024 II: Sample definitions and two-point clustering statistics
• DESI 2024 III: BAO from Galaxies and Quasars
• DESI 2024 IV: BAO from the Lyman-Forest
• DESI 2024 V: Full Shape measurements from Galaxies and Quasars
• DESI 2024 VI: Cosmological constraints from BAO measurements
• DESI 2024 VII: Constraints from the Full Shape measurements
Ly\(\alpha\) forest
Absorption in QSO spectra by neutral hydrogen in the intergalactic medium: \(\lambda_\mathrm{abs} = (1 + z_\mathrm{HI}) \times 1215.17 \; \AA \)
Transmitted flux fraction \(F = e^{-\tau}\) probes the fluctuation in neutral hydrogen density, \(\tau \propto n_\mathrm{HI} \)
credit: Andrew Pontzen
Ly\(\alpha\) correlation functions in DESI DR1
Ly\(\alpha\) - Ly\(\alpha\)
Ly\(\alpha\) - QSO
QSO
QSO
HI cloud
HI cloud
HI cloud
QSO
DR1 Ly\(\alpha\) BAO analysis: what's new?
- Biggest ever Ly\(\alpha\) dataset (\(N_\mathrm{tracer}\))
>420,000 Ly\(\alpha\) QSO at z > 2.1
\(2 \times \) more than SDSS!
- Biggest ever Ly\(\alpha\) dataset (\(N_\mathrm{tracer}\))
- First blind analysis to mitigate observer / confirmation biases (correlation function-level blinding)
DR1 Ly\(\alpha\) BAO analysis: what's new?
- Biggest ever Ly\(\alpha\) dataset (\(N_\mathrm{tracer}\))
- First blind analysis to mitigate observer / confirmation biases (correlation function-level blinding)
- Modelling of the correlation function: cosmological signal, and many contaminants!
- Very stable results, systematic uncertainty neglected
DR1 Ly\(\alpha\) BAO analysis: what's new?
- transverse to the line-of-sight: \(D_\mathrm{M}(z) / r_\mathrm{d}\)
- along the line-of-sight: \(D_\mathrm{H}(z) / r_\mathrm{d} = c / (H(z) r_\mathrm{d}) \)
- low S/N, isotropic average: \( D_\mathrm{V}(z) / r_\mathrm{d} = (z D_{\mathrm{M}}^{2}(z) D_\mathrm{H}(z))^{1/3} / r_\mathrm{d}\)
BAO measurements
Let's factor out the \(h\) terms:
- \(\color{blue}{[D_\mathrm{M}(z) h] (\Omega_\mathrm{m}, f_\mathrm{DE}, \Omega_\mathrm{K}, ...)} \color{black}{/} \color{orange}{[r_\mathrm{d}(\Omega_\mathrm{m} h^{2}, \Omega_\mathrm{b} h^{2}) h]} \)
- \( \color{blue}{[D_\mathrm{H}(z) h] (\Omega_\mathrm{m}, f_\mathrm{DE}, \Omega_\mathrm{K}, ...)} \color{black}{/} \color{orange}{[r_\mathrm{d}(\Omega_\mathrm{m} h^{2}, \Omega_\mathrm{b} h^{2}) h]} \)
BAO measurements at different \(z\) constrain:
- energy content \( \color{blue}{(\Omega_\mathrm{m}, f_\mathrm{DE}, ...)} \)
- constant-over-\(z\) product \(\color{orange}{r_\mathrm{d} h}\) i.e. \(\color{orange}{H_{0} r_\mathrm{d}}\)
DESI DR1 BAO
DESI DR1 BAO measurements
DESI DR1 BAO
DESI DR1 BAO measurements
DESI DR1 BAO
DESI DR1 BAO measurements
DESI DR1 BAO
DESI DR1 BAO measurements
DESI DR1 BAO
DESI DR1 BAO measurements
DESI DR1 BAO
DESI DR1 BAO measurements
Consistent with each other,
and complementary
\underbrace{\begin{align*}
\Omega_\mathrm{m} &= 0.295 \pm 0.015 & \mathbf{(5.1\%)} \\
H_{0} r_\mathrm{d} &= (101.8 \pm 1.3) \, [100 \, \mathrm{km} \, \mathrm{s}^{-1}] & \mathbf{(1.3\%)}
\end{align*}}_{\textstyle \text{\color{black}{DESI}}}
DESI DR1 BAO
DESI DR1 BAO measurements
Consistency with other probes
DESI DR1 BAO consistent with:
Consistency with other probes
DESI DR1 BAO consistent with:
Consistency with other probes
DESI DR1 BAO consistent with:
- SDSS eBOSS Collaboration, 2020
- primary CMB: Planck Collaboration, 2018 and CMB lensing: Planck PR4 + ACT DR6 lensing ACT Collaboration, 2023, Carron, Mirmelstein, Lewis, 2022
- BAO constrains \( r_\mathrm{d}(\blue{\Omega_\mathrm{m}} h^{2}, \orange{\Omega_\mathrm{b} h^{2}}) h \)
- \( \blue{\Omega_\mathrm{m}} \) constrained by BAO at different \(z\)
- \(\orange{\Omega_\mathrm{b}h^2}\) can be constrained by light element abundance from Big Bang Nucleosynthesis (BBN): Schöneberg 2024
\(\implies\) constraints on \(h\) i.e. \(H_0 = 100 h \; \mathrm{km} / \mathrm{s} / \mathrm{Mpc}\)
Hubble constant
\underbrace{
H_0 = (68.53 \pm 0.80) \, {\rm km\,s^{-1}\,Mpc^{-1}}
}_{\textstyle \color{darkblue}{\text{DESI} + \text{BBN}}}
Hubble constant
\underbrace{
H_0 = (68.52 \pm 0.62) \, {\rm km\,s^{-1}\,Mpc^{-1}}
}_{\textstyle \color{darkblue}{\text{DESI} + \theta_\ast + \text{BBN}}}
\(\theta_\ast\) CMB angular acoustic scale
- Consistency with SDSS
\underbrace{
H_0 = (68.53 \pm 0.80) \, {\rm km\,s^{-1}\,Mpc^{-1}}
}_{\textstyle \color{darkblue}{\text{DESI} + \text{BBN}}}
Hubble constant
\underbrace{
H_0 = (68.52 \pm 0.62) \, {\rm km\,s^{-1}\,Mpc^{-1}}
}_{\textstyle \color{darkblue}{\text{DESI} + \theta_\ast + \text{BBN}}}
- Consistency with SDSS
- In agreement with CMB
- In \(3.7 \sigma\) tension with SH0ES
\underbrace{
H_0 = (68.53 \pm 0.80) \, {\rm km\,s^{-1}\,Mpc^{-1}}
}_{\textstyle \color{darkblue}{\text{DESI} + \text{BBN}}}
Hubble constant
\underbrace{
H_0 = (68.52 \pm 0.62) \, {\rm km\,s^{-1}\,Mpc^{-1}}
}_{\textstyle \color{darkblue}{\text{DESI} + \theta_\ast + \text{BBN}}}
Release of DESI DR1 (FS) results
November 17th 2024
Second batch of DESI DR1 cosmological analyses
https://data.desi.lbl.gov/doc/papers/
• DESI 2024 I: First year data release
• DESI 2024 II: Sample definitions and two-point clustering statistics
• DESI 2024 III: BAO from Galaxies and Quasars
• DESI 2024 IV: BAO from the Lyman-Forest
• DESI 2024 V: Full Shape measurements from Galaxies and Quasars
• DESI 2024 VI: Cosmological constraints from BAO measurements
• DESI 2024 VII: Constraints from the Full Shape measurements
Full Shape measurements
clustering
We fit the "full shape" (FS) of the galaxy power spectrum multipoles
Full Shape measurements
RSD
observed redshift = Hubble flow and peculiar velocities (RSD = "redshift space distortions")
We fit the "full shape" (FS) of the galaxy power spectrum multipoles
v_\mathrm{pec} \propto f\sigma_8
shape
(\( \Omega_\mathrm{cdm} h^2, \Omega_\mathrm{b} h^2, n_\mathrm{s}, \sum m_\nu \))
growth of structure \(f\sigma_8\) sensitive to the theory of gravity and dark energy
Full Shape models
Three power spectrum Effective Field Theory models considered:
- velocileptors Maus et al. 2024
- folps Noriega et al. 2024
- pybird Lai et al. 2024
V = 200 \; [\mathrm{Gpc}/h]^3
credit: Mark Maus, Hernan Noriega, Yan Lai
Comparison paper:
Full Shape models
P_{s, g}(k, \mu) = \blue{P^\mathrm{PT}(k, \mu)} + \orange{(b + f\mu^2) (b \alpha_0 + f\alpha_2 \mu^2 + f\alpha_4 \mu^4) k^2 P_{s, b_1^2}(k)} + \purple{\mathrm{SN}_0 + \mathrm{SN}_2 k^2 \mu^2 + \mathrm{SN}_4 k^4 \mu^4}
perturbation theory term
linear and quasi-linear physics
counter-terms contribution
truncation of perturbative series
stochastic-terms contribution
small-scale galaxy physics
The Effective Field Theory in a nutshell
- perturbation theory model + counter-terms and stochastic terms
- dependence on cosmology into \(P_\mathrm{lin}\), \(f\) and Alcock-Paczynski parameters (\(\mathrm{R.A.}, \mathrm{Dec.}, z \Rightarrow \mathrm{distance}\))
- Biggest ever spectroscopic dataset (\(N_\mathrm{tracer}\) and \(V\))
- Blind analysis to mitigate observer / confirmation biases (catalog-level blinding)
- Effective Field Theory models
- Full-Modelling (\(\Omega_\mathrm{cosmo}\)) and updated compression approach (ShapeFit)
- Improvements in the treatment of observational systematics (e.g. fiber assignment)
- Unified Full Shape pipeline applied to all (discrete) tracer / redshift bins consistently
DR1 Full Shape analysis: what's new?
Groups of galaxies too close to each other cannot all receive a fiber
\(0.05^\circ \simeq\) positioner patrol diameter
Fiber assignment
Fiber assignment
Impacts power spectrum measurements (altMTL vs complete)
Impacts power spectrum measurements (altMTL vs complete)
Solution: \(\theta\)-cut = remove all pairs \(< 0.05^\circ\)
Fiber assignment
New window matrix \(W^\mathrm{cut}\); \(\langle P_o(k) \rangle = W^\mathrm{cut}(k, k^\prime) P_t(k^\prime)\)
Fiber collisions
New window matrix \(W^\mathrm{cut}\); \(\langle P_o(k) \rangle = W^\mathrm{cut}(k, k^\prime) P_t(k^\prime)\)
Very non diagonal: let's "rotate" it
Fiber collisions
Systematic effects
-
Theoretical modelling (Maus et al. 2024ab, Lai et al. 2024, Noriega et al. 2024, Ramirez et al. 2024)
-
Galaxy-halo connection (Findlay et al. 2024)
-
Fiducial cosmology (Gsponer et al. 2024)
-
Fibre assignment (Pinon et al. 2024)
-
Inhomogeneities in the target selection (Zhao et al. 2024)
-
Spectroscopic redshift failures/uncertainties (Yu et al. 2024, Krowleski et al. 2024)
-
Covariance matrix: mock-based vs analytic (Forero-Sanchez et al. 2024, Alves et al. 2024, Rashkovetskyi et al. 2024)
Total systematic error = ⅖ of DR1 statistical uncertainty
no systematics detected
Full Shape (+ BAO) measurements
-
Observable: power spectrum monopole and quadrupole, post-reconstruction BAO
-
Model: Effective Field Theory
-
Covariance: mock-based
-
Systematic error: at the data vector level
-
Fitting range: \(0.02 < k\, [h/\mathrm{Mpc}] < 0.2\)
-
Fitting parameters:
-
7 nuisance parameters
-
5 \(\Lambda\)CDM parameters
-
External information
\(\Omega_\mathrm{b} h^2\): BBN from Schöneberg 2024
\(n_\mathrm{s} \sim \mathcal{G}(0.9649, 0.042^2)\): "\(n_{\mathrm{s}10}\)" \(10\times\) wider than Planck posterior
Full Shape + BAO measurements
\(\omega_\mathrm{b}\): BBN, \(n_\mathrm{s} \sim \mathcal{G}(0.9649, 0.042^2)\)
Full Shape + BAO measurements
\(\omega_\mathrm{b}\): BBN, \(n_\mathrm{s} \sim \mathcal{G}(0.9649, 0.042^2)\)
Full Shape + BAO measurements
\(\omega_\mathrm{b}\): BBN, \(n_\mathrm{s} \sim \mathcal{G}(0.9649, 0.042^2)\)
Full Shape + BAO measurements
\(\omega_\mathrm{b}\): BBN, \(n_\mathrm{s} \sim \mathcal{G}(0.9649, 0.042^2)\)
Full Shape + BAO measurements
\(\omega_\mathrm{b}\): BBN, \(n_\mathrm{s} \sim \mathcal{G}(0.9649, 0.042^2)\)
Full Shape + BAO measurements
\(\omega_\mathrm{b}\): BBN, \(n_\mathrm{s} \sim \mathcal{G}(0.9649, 0.042^2)\)
Full Shape + BAO measurements
\underbrace{\begin{align*}
\Omega_\mathrm{m} &= 0.2962\pm 0.0095 & \mathbf{(3.2\%)} \\
\sigma_8 &= 0.842\pm 0.034 & \mathbf{(4.0\%)} \\
H_0 &= (68.56\pm 0.75) \, {\rm km\,s^{-1}\,Mpc^{-1}} & \mathbf{(1.1\%)}
\end{align*}}_{\textstyle \text{DESI + BBN + $n_{s10}$}}
\(\omega_\mathrm{b}\): BBN, \(n_\mathrm{s} \sim \mathcal{G}(0.9649, 0.042^2)\)
\(S_8\) constraints
\underbrace{
S_8 = 0.836 \pm 0.035
}_{\textstyle \text{\color{blue}{DESI + BBN + $n_{s10}$}}}
\(S_8 = \sigma_8 (\Omega_\mathrm{m} / 0.3)^{0.5}\) best constrained by weak lensing surveys
\(S_8\) constraints
- Consistency with SDSS
- In agreement with CMB
\(S_8 = \sigma_8 (\Omega_\mathrm{m} / 0.3)^{0.5}\) best constrained by weak lensing surveys
\underbrace{
S_8 = 0.836 \pm 0.035
}_{\textstyle \text{\color{blue}{DESI + BBN + $n_{s10}$}}}
\(S_8\) constraints
- Consistency with SDSS
- In agreement with CMB
- Weak lensing prefers lower \(S_8\), but still consistent
\(S_8 = \sigma_8 (\Omega_\mathrm{m} / 0.3)^{0.5}\) best constrained by weak lensing surveys
\underbrace{
S_8 = 0.836 \pm 0.035
}_{\textstyle \text{\color{blue}{DESI + BBN + $n_{s10}$}}}
Dark Energy fluid, pressure \(p\), density \(\rho\)
Equation of State parameter \(w = p / \rho\)
Linked to the evolution of Dark Energy \(w(z) = -1 + \frac{1}{3}\frac{d \ln f_\mathrm{DE}(z)}{d \ln (1 + z)}\)
Let's assume the CPL parameterization
w(z) = w_{0} + \frac{z}{1 + z} w_{a} \qquad \text{(CPL)}
Dynamical Dark Energy - \((w_{0}, w_{a})\)
\(\Lambda\)CDM: \((w_0, w_a) = (-1, 0)\)
Dynamical Dark Energy - \((w_{0}, w_{a})\)
DESI + CMB + SN (uncalibrated):
\(\Lambda\)CDM
Dynamical Dark Energy - \((w_{0}, w_{a})\)
- same preference for \(w_{0} > -1, w_{a} < 0\)
- similar significance for \(w_0w_a\)CDM vs \(\Lambda\)CDM
- \(20\%\) better constraints in \((w_0, w_a)\) than without FS
\(2.5\sigma\)
\(3.4\sigma\)
\(3.8\sigma\)
Sum of neutrino masses
Massive neutrinos impact:
i) the expansion history
ii) the growth of structure: \( \Delta P(k)/P(k) \propto -\sum m_\nu / \omega_\mathrm{m} \)
the \(k\)-fitting range
Sum of neutrino masses
Massive neutrinos impact:
i) the expansion history
ii) the growth of structure: \( \Delta P(k)/P(k) \propto -\sum m_\nu / \omega_\mathrm{m} \)
Taking \(n_\mathrm{s}\) prior from Planck:
\underbrace{
\sum m_\nu < 0.409 \, \mathrm{eV} \; (95\%)
}_{\textstyle \text{\color{blue}{DESI + BBN + $n_\mathrm{s10}$}}}
\underbrace{
\sum m_\nu < 0.300 \, \mathrm{eV} \; (95\%)
}_{\textstyle \text{\color{blue}{DESI + BBN + $n_\mathrm{s}$}}}
Sum of neutrino masses
Internal CMB degeneracies limiting precision on the sum of neutrino masses
Broken by DESI, especially through \(H_{0}\)
Sum of neutrino masses
Internal CMB degeneracies limiting precision on the sum of neutrino masses
Broken by DESI, especially through \(H_{0}\)
Low preferred value of \(H_{0}\) yields
\(\sum m_\nu < 0.071 \, \mathrm{eV} \; (95\%, \color{green}{\text{DESI + CMB})}\)
(\(15\%\) better than BAO-only: \(0.082 \, \mathrm{eV} \))
\(\sum m_\nu < 0.081 \, \mathrm{eV} \; (95\%, \color{green}{\text{DESI + CMB[hillipop]})}\)
In \(w_0w_a\mathrm{CDM}\), with DES-SN5YR: \(\sim 0.2\, \mathrm{eV} \; (95\%)\)
Modified gravity constraints
In general relativity, \(\green{\mu(a, k)} = \green{\Sigma(a, k)} = 1\)
To test GR, introduce \(\green{\mu_0, \Sigma_0}\)
\mu(a) = 1 + \frac{\Omega_\Lambda(a)}{\Omega_\Lambda} \green{\mu_0}, \Sigma(a) = 1 + \frac{\Omega_\Lambda(a)}{\Omega_\Lambda} \green{\Sigma_0}
Perturbed FLRW metric
\(ds^2=a(\tau)^2[-(1+2\orange{\Psi})d\tau^2+(1-2\orange{\Phi})\delta_{ij}dx^i dx^j]\)
At late times:
(mass) \(k^2\orange{\Psi} = -4\pi G a^2 \green{\mu(a,k)} \blue{\sum_i\rho_i\Delta_i}\)
(light) \(k^2(\orange{\Phi} + \orange{\Psi})=-8\pi G a^2 \green{\Sigma(a,k)} \blue{\sum_i\rho_i\Delta_i}\)
gravitational potentials
density perturbations
Modified gravity constraints
\underbrace{
\mu_0 = 0.11^{+0.45}_{-0.54}
}_{\textstyle \text{\color{blue}{DESI + BBN + $n_{\mathrm{s}10}$}}}
DESI constrains
GR
Modified gravity constraints
\(\Sigma_0\) constrained by
- CMB (ISW and lensing)
- galaxy lensing
\underbrace{
\mu_0 = 0.11^{+0.45}_{-0.54}
}_{\textstyle \text{\color{blue}{DESI + BBN + $n_{\mathrm{s}10}$}}}
DESI constrains
Modified gravity constraints
\(\Sigma_0\) constrained by
- CMB (ISW and lensing)
- galaxy lensing
\underbrace{\begin{align*}
\mu_0 &= 0.04\pm 0.22 \\
\Sigma_0 &= 0.045\pm 0.046
\end{align*}}_{\textstyle \text{\color{purple}{DESI + CMB-nl + DESY3 ($3\times 2$-pt)}}}
\underbrace{
\mu_0 = 0.11^{+0.45}_{-0.54}
}_{\textstyle \text{\color{blue}{DESI + BBN + $n_{\mathrm{s}10}$}}}
compared to CMB-nl + DESY3 (3x2pt) only: \(\sigma(\mu_0) / 2.5\), \(\sigma(\Sigma_0) / 2\)
DESI constrains
Conclusions
BAO
- compared to Planck: low \(\Omega_\mathrm{m}\), high \(H_{0}\)
- hint of dynamical dark energy (depending on SN dataset)
Adding Full Shape
- \(\sigma_8, S_8\) consistent with Planck
- modified gravity \(\mu_0\) parameter consistent with GR
- small improvements in \(w_0, w_a\) and \(\sum m_\nu\) constraints
What to do next?
DR2 data (Y3 > Y1) on disk, DR2 BAO analysis on-going... stay tuned!
DR2 analyses will include joint 2-pt, 3-pt measurements: new challenges!
Possible improvements to the current analyses:
- more / improved mocks
- faster cosmological inference with emulators / JAX
- simplify \(P(k)\) and associated window measurements
\(f_\mathrm{NL}^\mathrm{loc}\) - mock constraints
Chaussidon et al. 2024, in prep
DR1: \(\sigma(f_\mathrm{NL}^\mathrm{loc}) \sim 10\)
SDSS: \(\sigma(f_\mathrm{NL}^\mathrm{loc}) \sim 20\)
LRG
QSO
Successfully removes the \( > 1 \sigma\) bias
credit: Ruiyang Zhao
Fiber collisions
Combined constraints
*DES and SPT collaborations 2022
6x2pt = galaxy-galaxy, galaxy-shear, shear-shear, galaxy-CMB lensing, shear-CMB lensing, CMB lensing-CMB lensing
- Adding DESI to DESY3 6x2pt improves \(\sigma_8\) and \(\Omega_\mathrm{m}\) precision by \(\times 2\) (\(S_8\) by \(20\%\))
Combined constraints
- Adding DESI to DESY3 6x2pt improves \(\sigma_8\) and \(\Omega_\mathrm{m}\) precision by \(\times 2\) (\(S_8\) by \(20\%\))
- Adding DESI to CMB improves \(\Omega_\mathrm{m}\), \(H_{0}\) and \(S_{8}\) precision by \(30\%\)
Combined constraints
- Adding DESI to DESY3 6x2pt improves \(\sigma_8\) and \(\Omega_\mathrm{m}\) precision by \(\times 2\) (\(S_8\) by \(20\%\))
- Adding DESI to CMB improves \(\Omega_\mathrm{m}\), \(H_{0}\) and \(S_{8}\) precision by \(30\%\)
\underbrace{\begin{align*}
\Omega_\mathrm{m} &= 0.3009 \pm 0.0034 & \mathbf{(1\%)} \\
\sigma_8 &= 0.8028^{+0.0050}_{-0.0045} & \mathbf{(0.6\%)} \\
H_0 &= (68.40\pm 0.27) \, {\rm km\,s^{-1}\,Mpc^{-1}} & \mathbf{(0.4\%)}
\end{align*}}_{\textstyle \text{\color{green}{DESI + DESY3 (6$\times$2pt) + CMB-nl}}}
Dynamical Dark Energy - \((\Omega_\mathrm{m}, \sigma_{8})\)
(\(\Omega_\mathrm{m}, \sigma_{8})\) constraints remain stable when opening up to \(w_0w_a\text{CDM}\)
Dynamical Dark Energy - \((\Omega_\mathrm{m}, \sigma_{8})\)
(\(\Omega_\mathrm{m}, \sigma_{8})\) constraints remain stable when opening up to \(w_0w_a\text{CDM}\)
SN (uncalibrated):
- Pantheon+ Brout, Scolnic, Popovic et al., 2022
- Union3 Rubin, Aldering, Betoule et al. 2023
- DES-SN5YR DES Collaboration et al. 2024
Other datasets
- SDSS (for comparisons only): eBOSS Collaboration, 2020
- Primary CMB: Planck Collaboration, 2018
- CMB lensing: Planck PR4 + ACT DR6 lensing ACT Collaboration, 2023, Carron, Mirmelstein, Lewis, 2022
- BBN: Schöneberg 2024
- SN: Pantheon+ Brout, Scolnic, Popovic et al. 2022, Union3 Rubin, Aldering, Betoule et al. 2023, DES-SN5YR DES Collaboration
- DESY3 3x2pt DES collaboration 2021, 6x2pt DES and SPT collaborations 2022
DESI + CMB measurements favor a flat Universe
\Omega_\mathrm{K} = 0.0024 \pm 0.0016 \; \green{(\text{DESI} + \text{CMB})}
Spatial curvature
Dark Energy Equation of State
\underbrace{\begin{align*}
\Omega_\mathrm{m} &= 0.293 \pm 0.015 & \mathbf{(5.1\%)} \\
w &= -0.99^{+0.15}_{-0.13} & \mathbf{(15\%)}
\end{align*}}_{\textstyle \text{\color{darkblue}{DESI}}}
Constant EoS parameter \(w = p / \rho\)
\Lambda
Dark Energy Equation of State
\underbrace{\begin{align*}
\Omega_\mathrm{m} &= 0.293 \pm 0.015 & \mathbf{(5.1\%)} \\
w &= -0.99^{+0.15}_{-0.13} & \mathbf{(15\%)}
\end{align*}}_{\textstyle \text{\color{darkblue}{DESI}}}
Constant EoS parameter \(w = p / \rho\)
Dark Energy Equation of State
SNe (uncalibrated):
- Pantheon+ Brout, Scolnic, Popovic et al., 2022
\underbrace{\begin{align*}
\Omega_\mathrm{m} &= 0.293 \pm 0.015 & \mathbf{(5.1\%)} \\
w &= -0.99^{+0.15}_{-0.13} & \mathbf{(15\%)}
\end{align*}}_{\textstyle \text{\color{darkblue}{DESI}}}
Constant EoS parameter \(w = p / \rho\)
Dark Energy Equation of State
SNe (uncalibrated):
- Pantheon+ Brout, Scolnic, Popovic et al., 2022
- Union3 Rubin, Aldering, Betoule et al. 2023
\underbrace{\begin{align*}
\Omega_\mathrm{m} &= 0.293 \pm 0.015 & \mathbf{(5.1\%)} \\
w &= -0.99^{+0.15}_{-0.13} & \mathbf{(15\%)}
\end{align*}}_{\textstyle \text{\color{darkblue}{DESI}}}
Constant EoS parameter \(w = p / \rho\)
Dark Energy Equation of State
SNe (uncalibrated):
- Pantheon+ Brout, Scolnic, Popovic et al., 2022
- Union3 Rubin, Aldering, Betoule et al. 2023
- DES-SN5YR DES Collaboration et al. 2024
\underbrace{\begin{align*}
\Omega_\mathrm{m} &= 0.293 \pm 0.015 & \mathbf{(5.1\%)} \\
w &= -0.99^{+0.15}_{-0.13} & \mathbf{(15\%)}
\end{align*}}_{\textstyle \text{\color{darkblue}{DESI}}}
Constant EoS parameter \(w = p / \rho\)
Dark Energy Equation of State
\underbrace{\begin{align*}
\Omega_\mathrm{m} &= 0.3095 \pm 0.0065 & \mathbf{(2.1\%)} \\
w &= -0.997 \pm 0.025 & \mathbf{(2.5\%)}
\end{align*}}_{\textstyle \text{\color{orange}{DESI + CMB + Pantheon+}}}
Assuming a constant EoS, DESI BAO fully compatible with a cosmological constant...
\underbrace{\begin{align*}
\Omega_\mathrm{m} &= 0.293 \pm 0.015 & \mathbf{(5.1\%)} \\
w &= -0.99^{+0.15}_{-0.13} & \mathbf{(15\%)}
\end{align*}}_{\textstyle \text{\color{darkblue}{DESI}}}
Constant EoS parameter \(w = p / \rho\)
w(z) = w_{0} + \frac{z}{1 + z} w_{a} \qquad \text{(CPL)}
\left.
\begin{align*}
w_{0} &= -0.55^{+0.39}_{-0.21} \\
w_{a} &< -1.32
\end{align*}
\right\rbrace {\text{\color{black}{DESI}}}
Dark Energy Equation of State
Varying EoS
\Lambda
Dark Energy Equation of State
\underbrace{
w_{0} = -0.45^{+0.34}_{-0.21} \qquad w_{a} = -1.79^{+0.48}_{-1.00}
}_{\textstyle \purple{\text{DESI + CMB} \; \implies \; 2.6\sigma}}
Varying EoS
w(z) = w_{0} + \frac{z}{1 + z} w_{a} \qquad \text{(CPL)}
Dark Energy Equation of State
\underbrace{
w_{0} = -0.45^{+0.34}_{-0.21} \qquad w_{a} = -1.79^{+0.48}_{-1.00}
}_{\textstyle \purple{\text{DESI + CMB} \; \implies \; 2.6\sigma}}
Varying EoS
w(z) = w_{0} + \frac{z}{1 + z} w_{a} \qquad \text{(CPL)}
Dark Energy Equation of State
\underbrace{
w_{0} = -0.45^{+0.34}_{-0.21} \qquad w_{a} = -1.79^{+0.48}_{-1.00}
}_{\textstyle \purple{\text{DESI + CMB} \; \implies \; 2.6\sigma}}
Varying EoS
w(z) = w_{0} + \frac{z}{1 + z} w_{a} \qquad \text{(CPL)}
Dark Energy Equation of State
\underbrace{
w_{0} = -0.45^{+0.34}_{-0.21} \qquad w_{a} = -1.79^{+0.48}_{-1.00}
}_{\textstyle \purple{\text{DESI + CMB} \; \implies \; 2.6\sigma}}
Varying EoS
w(z) = w_{0} + \frac{z}{1 + z} w_{a} \qquad \text{(CPL)}
Dark Energy Equation of State
Combining all DESI + CMB + SN
\underbrace{
w_{0} = -0.827 \pm 0.063 \qquad w_{a} = -0.75^{+0.29}_{-0.25}
}_{\textstyle \blue{\text{DESI + CMB + Pantheon+} \; \implies \; 2.5\sigma}}
Dark Energy Equation of State
Combining all DESI + CMB + SN
\underbrace{
w_{0} = -0.827 \pm 0.063 \qquad w_{a} = -0.75^{+0.29}_{-0.25}
}_{\textstyle \blue{\text{DESI + CMB + Pantheon+} \; \implies \; 2.5\sigma}}
\underbrace{
w_{0} = -0.64 \pm 0.11\qquad w_{a} = -1.27^{+0.40}_{-0.34}
}_{\textstyle \color{orange}{\text{DESI + CMB + Union3} \; \implies \; 3.5\sigma}}
Dark Energy Equation of State
Combining all DESI + CMB + SN
\underbrace{
w_{0} = -0.827 \pm 0.063 \qquad w_{a} = -0.75^{+0.29}_{-0.25}
}_{\textstyle \blue{\text{DESI + CMB + Pantheon+} \; \implies \; 2.5\sigma}}
\underbrace{
w_{0} = -0.64 \pm 0.11\qquad w_{a} = -1.27^{+0.40}_{-0.34}
}_{\textstyle \color{orange}{\text{DESI + CMB + Union3} \; \implies \; 3.5\sigma}}
\underbrace{
w_{0} = -0.727 \pm 0.067 \qquad w_{a} = -1.05^{+0.31}_{-0.27}
}_{\textstyle \color{green}{\text{DESI + CMB + DES-SN5YR} \; \implies \; 3.9\sigma}}
Dark Energy Equation of State
Combining all DESI + CMB + SN
\(w_{0} > -1, w_{a} < 0\) favored, level varying on the SN dataset
\underbrace{
w_{0} = -0.827 \pm 0.063 \qquad w_{a} = -0.75^{+0.29}_{-0.25}
}_{\textstyle \blue{\text{DESI + CMB + Pantheon+} \; \implies \; 2.5\sigma}}
\underbrace{
w_{0} = -0.64 \pm 0.11\qquad w_{a} = -1.27^{+0.40}_{-0.34}
}_{\textstyle \color{orange}{\text{DESI + CMB + Union3} \; \implies \; 3.5\sigma}}
\underbrace{
w_{0} = -0.727 \pm 0.067 \qquad w_{a} = -1.05^{+0.31}_{-0.27}
}_{\textstyle \color{green}{\text{DESI + CMB + DES-SN5YR} \; \implies \; 3.9\sigma}}
Sum of neutrino masses
Internal CMB degeneracies limiting precision on the sum of neutrino masses
Sum of neutrino masses
Internal CMB degeneracies limiting precision on the sum of neutrino masses
Broken by BAO, especially through \(H_{0}\)
Low preferred value of \(H_{0}\) yields
\(\sum m_\nu < 0.072 \, \mathrm{eV} \; (95\%, \color{green}{\text{DESI + CMB})}\)
Limit relaxed for extensions to \(\Lambda\mathrm{CDM}\)
\(\sum m_\nu < 0.195 \, \mathrm{eV}\) for \(w_0w_a\mathrm{CDM}\)
Neutrino mass hierarchies
\sum m_\nu < 0.113 \, \mathrm{eV} \; (95\%, \color{green}{\text{DESI + CMB})}
With \(> 0.059 \, \mathrm{eV}\) prior (NH)
Neutrino mass hierarchies
\sum m_\nu < 0.113 \, \mathrm{eV} \; (95\%, \color{green}{\text{DESI + CMB})}
\sum m_\nu < 0.145 \, \mathrm{eV} \; (95\%, \color{green}{\text{DESI + CMB})}
With \(> 0.059 \, \mathrm{eV}\) prior (NH)
With \(> 0.1 \, \mathrm{eV}\) prior (IH)
Neutrino mass hierarchies
\sum m_\nu < 0.113 \, \mathrm{eV} \; (95\%, \color{green}{\text{DESI + CMB})}
\sum m_\nu < 0.145 \, \mathrm{eV} \; (95\%, \color{green}{\text{DESI + CMB})}
With \(> 0.059 \, \mathrm{eV}\) prior (NH)
With \(> 0.1 \, \mathrm{eV}\) prior (IH)
Current constraints do not strongly favor normal over inverted hierarchy (\(\simeq 2 \sigma\))
Summary
DESI already has the most precise BAO measurements ever
Summary
DESI already has the most precise BAO measurements ever
DESI BAO is consistent (at the \(\sim 1.9\sigma\) level) with CMB in flat ΛCDM
Summary
DESI already has the most precise BAO measurements ever
DESI BAO is consistent (at the \(\sim 1.9\sigma\) level) with CMB in flat ΛCDM
In flat ΛCDM, DESI prefers "small \(\Omega_\mathrm{m}\), large \(H_0\) (though \(3.7\sigma\) away from SH0ES), small \(\sum m_\nu\)"
Summary
DESI already has the most precise BAO measurements ever
DESI BAO is consistent (at the \(\sim 1.9\sigma\) level) with CMB in flat ΛCDM
In flat ΛCDM, DESI prefers "small \(\Omega_\mathrm{m}\), large \(H_0\) (though \(3.7\sigma\) away from SH0ES), small \(\sum m_\nu\)"
Some hint of time-varying Dark Energy equation of state especially when combined with supernovae measurements
DESI data release 1 (DR1)
5.7 million unique redshifts at z < 2.1 and > 420,000 Ly\(\alpha\) QSO at z > 2.1
DESI Y5 forecasts
Survey Validation (arXiv:2306.06307)
BAO and RSD constraints at the end of the survey (\( \Delta z = 0.1 \))
Ly\(\alpha\)
DESI Y5 forecasts
Survey Validation (arXiv:2306.06307)
BAO and RSD constraints at the end of the survey (\( \Delta z = 0.1 \))
Ly\(\alpha\)
(w/ Planck)
Power spectra
Power spectra
BAO wiggles
Density field reconstruction
DESI DR1 Ly\(\alpha\) BAO analysis
- Biggest ever Ly\(\alpha\) dataset (\(N_\mathrm{tracer}\))
- First blind analysis to mitigate observer / confirmation biases (correlation function-level blinding)
- Modelling of the correlation function:
- cosmo signal
P_{\mathrm{Ly}\alpha}(k, \mu) = \blue{b^{2} (1 + \beta \mu^2)^2} \orange{P_\mathrm{lin}(k, \mu)} \green{F_\mathrm{NL}(k, \mu)}
linear bias + RSD
hydro-sim
BAO
\mu = r_\parallel / \sqrt{r_\parallel^2 + r_\perp^2}
DESI DR1 Ly\(\alpha\) BAO analysis
- Biggest ever Ly\(\alpha\) dataset (\(N_\mathrm{tracer}\))
- First blind analysis to mitigate observer / confirmation biases (correlation function-level blinding)
- Modelling of the correlation function:
- cosmo signal
- high-column density
- metal absorbers
r_\parallel \propto \left\vert\frac{1}{\lambda_{\mathrm{Ly}\alpha}} - \frac{1}{\lambda_{\mathrm{metal}}}\right\vert
SiII
DESI DR1 Ly\(\alpha\) BAO analysis
- Biggest ever Ly\(\alpha\) dataset (\(N_\mathrm{tracer}\))
- First blind analysis to mitigate observer / confirmation biases (correlation function-level blinding)
- Modelling of the correlation function:
- cosmo signal
- high-column density
- metal absorbers
- correlated noise (sky subtraction)
DESI DR1 Ly\(\alpha\) BAO analysis
- Biggest ever Ly\(\alpha\) dataset (\(N_\mathrm{tracer}\))
- First blind analysis to mitigate observer / confirmation biases (correlation function-level blinding)
- Modelling of the correlation function:
- cosmo signal
- high-column density
- metal absorbers
- correlated noise (sky subtraction)
DESI DR1 Ly\(\alpha\) BAO analysis
- Biggest ever Ly\(\alpha\) dataset (\(N_\mathrm{tracer}\))
- First blind analysis to mitigate observer / confirmation biases (correlation function-level blinding)
- Modelling of the correlation function
physical model fit
+ broadband polynomial
broadband: \(< 0.1\sigma\)
DESI DR1 Ly\(\alpha\) BAO analysis
- Biggest ever Ly\(\alpha\) dataset (\(N_\mathrm{tracer}\))
- First blind analysis to mitigate observer / confirmation biases (correlation function-level blinding)
- Modelling of the correlation function
- Covariance matrix
Correlation matrix
smoothed jackknife, validated with mocks
Tests of systematic errors
tests with same dataset (not red): shifts \(< \sigma_\mathrm{stat}/3\)
tests with varying datasets (red): shifts consistent with stat.
Tests of systematic errors
DESI DR1 BAO analysis
- Biggest ever spectroscopic BAO dataset (\(N_\mathrm{tracer}\) and \(V\))
- Blind analysis to mitigate observer / confirmation biases (catalog-level blinding)
(\mathrm{R.A.}, \mathrm{Dec.}, z) \Longrightarrow (x, y, z) \Longrightarrow (\mathrm{R.A.}^\prime, \mathrm{Dec.}^\prime, z^\prime)
fiducial cosmology
blinded cosmology (\(\Omega_\mathrm{m}, w_0, w_a\))
(random & unknown)
+ RSD blinding: change reconstructed peculiar velocities
+ \(f_\mathrm{NL}\) blinding: add clustering-dependent signal on large scales with weights
DESI DR1 BAO analysis
- Biggest ever spectroscopic BAO dataset (\(N_\mathrm{tracer}\) and \(V\))
- Blind analysis to mitigate observer / confirmation biases (catalog-level blinding)
- Theory developments in BAO fitting code
P_\mathrm{gg}(k, \mu) = \mathcal{B}(k, \mu) P_\mathrm{nw}(k) + \mathcal{C}(k, \mu) P_\mathrm{w}(k) + \orange{\mathcal{D}(k, \mu)}
Chen, Howlett et al. 2024
Some fits: Fourier space
\(\sum m_\nu\)
credit: Christophe Yèche
\(w(z)\)
DESI - SDSS consistency (\(\Omega_\mathrm{m}\))
Perfectly consistent!
Using these 2 points alone moves \(\Omega_\mathrm{m}\) by \(< 2 \sigma\)
Are SN \(\Omega_\mathrm{m}\) consistent?
Not so much in flat \(\Lambda\mathrm{CDM}\)...
(so we do not combine them in this model!)
Are SN \(\Omega_\mathrm{m}\) consistent?
Consistent in \(w_0w_a\mathrm{CDM}\)!
plik vs PR4 Planck likelihoods
Appendix B
\(w_0 - w_a\) with \(\sum m_\nu\) free
\(w_0 - w_a\) with \(\Omega_\mathrm{K}\)
Preference for \(w_{0} > -1, w_{a} < 0\) persists when curvature is left free
DE constraints driven by low-\(z\) ?
Not that much!
DESI + SDSS swaps DESI measurements with SDSS for \(z < 0.6\)
\(- 0.4 \sigma\) compared to DESI only
\(w(z)\)
Dark energy equation of state:
\(P = w \rho\)
- \(w\) = constant
BAO measurements: dark energy
BAO measurements: dark energy
Full tables
Full tables
Full tables
Full tables
Marseille_November2024
By Arnaud De Mattia
