clax

A differentiable cosmological Boltzmann solver in JAX.

Development

This codebase was written entirely by Claude Code (Opus 4.6). The development process -- including architecture decisions, bug hunting through the CLASS C source code, and numerical validation -- is documented in CHANGELOG.md and CLAUDE.md.

Overview

clax solves the coupled Einstein-Boltzmann equations for cosmological perturbations from first principles: background cosmology, hydrogen recombination, the full photon-baryon-neutrino Boltzmann hierarchy in synchronous gauge, line-of-sight integration for CMB angular power spectra, HaloFit for nonlinear matter power, gravitational lensing, and a shooting method for theta_s parametrization. The entire pipeline -- from cosmological parameters to P(k), C_l^TT/EE/TE/BB, and lensed C_l -- is end-to-end differentiable via JAX automatic differentiation.

The goal is a differentiable alternative to CLASS that enables gradient-based cosmological inference (HMC, variational methods) on CMB and large-scale structure data.

Status

Sub-0.2% unlensed C_l^TT/EE at l=20-1200. Full lensed C_l^TT/EE/TE/BB (sub-0.2% at l=10-2000). Multi-cosmology validated (10 LCDM parameter points). Full ncdm Boltzmann hierarchy. fit_cl preset for HMC / fitting (~20 s end to end on a V100 with its 5 q-bin massive-neutrino hierarchy, Oct 22, 2026); planck_cl for science-grade accuracy (~124 s on a V100 after the Oct 2026 retune; 4679 s before). One-loop EFTofLSS module (clax.ept) ports the CLASS-PT FFTLog pipeline to JAX with sub-percent accuracy on monopoles/quadrupoles. See CHANGELOG.md for details.

One-loop EFTofLSS (`clax.ept`) accuracy vs CLASS-PT

All comparisons at Planck 2018 best-fit LCDM, z=0.38, k < 0.30 h/Mpc:

Spectrum	Max error	Mean error	Notes
P_mm real-space	0.33%	0.10%	Matter-matter, IR resummed
P_gg real-space	0.35%	0.12%	Galaxy-galaxy (b1=2)
P_gm real-space	0.28%	0.09%	Galaxy-matter cross
P_mm ell=0	0.21%	0.08%	Matter monopole
P_mm ell=2	0.34%	0.11%	Matter quadrupole
P_mm ell=4	1.2%	0.45%	Matter hexadecapole
P_gg ell=0	0.42%	0.15%	Galaxy monopole
P_gg ell=2	0.89%	0.50%	Galaxy quadrupole
P_gg ell=4	1.8%	0.72%	Galaxy hexadecapole

Sub-percent on all monopoles and quadrupoles. Hexadecapole within 2% (limited by the small signal amplitude and zero-crossings — see project memory project_ept_hexadecapole_floor).

The clax.ept module additionally feeds CMB lensing C_l^phiphi corrections via compute_cl_pp(... nonlinear="ept", pt_mpk=...) (pt_mpk is the solve that reaches today, perturbations_solve_mpk).

Accuracy comparison against CLASS v3.3.4

All comparisons at Planck 2018 best-fit LCDM. GPU: H100-80GB.

Unlensed C_l angular power spectra

planck_cl preset as it was when this table was measured (before the Oct 2026 retune): k_max=1.0, 60 k/decade (300 modes), l_max=50, full ncdm Psi_l(q) hierarchy (5 q-bins x 18 multipoles), source-interpolated to 10000 fine k-points. The retuned preset (l_max 17/17/17, rtol 1e-4 / atol 1e-6, max_steps 8192, Rodas5, fast path at n_k_fine 20000) gives the same spectra to ~0.1% at l <= 1000 (CHANGELOG, Oct 12, 2026).

Multipole l	C_l^TT error	C_l^EE error	C_l^TE error
20	-0.08%	-0.21%	-0.3% †
30	-0.05%	-0.11%	-0.5% †
50	-0.05%	-0.05%	+0.8% †
100	-0.02%	+0.02%	-0.03%
200	-0.05%	-0.04%	-0.05%
300	-0.06%	-0.02%	-0.04%
500	-0.15%	-0.15%	-0.01%
700	-0.23%	-0.11%	+0.08%
1000	-0.57%	-0.26%	+1.7%
1200	-0.07%	+0.03%	—

Bold = sub-percent. TT sub-0.1% at l=20-300 and l=1200. EE sub-0.3% from l=20 to l=1200.

† Near a TE zero crossing (ℓ≈52 in Planck-fiducial ΛCDM; second crossing near ℓ≈400). The relative-error metric (clax − CLASS) / CLASS is ill-defined when C_l^TE → 0; the absolute residual is comparable to neighboring ℓ where the metric is well-defined. The standard Hu & White (1997) correlation criterion |C_l^TE| / √(C_l^TT · C_l^EE) < 0.02 flags these multipoles, and the lensed-TE accuracy test in tests/test_lensing.py:156 already skips them. A Gaussian likelihood weights TE zero-crossing modes by 1/Var(C_l^TE) → 0 automatically, so this metric artifact does not affect HMC inference.

Lensed C_l angular power spectra

Full spin-2 correlation function lensing with Cgl2 corrections, 12 Wigner d-functions:

Multipole l	Lensed TT err	Lensed EE err	BB ratio
50	-0.000%	+0.000%	1.000
200	+0.000%	-0.001%	1.000
500	+0.002%	-0.003%	0.999
1000	+0.006%	+0.005%	0.996
2000	-0.199%	-0.166%	0.937

Lensing algorithm sub-0.2% at all l=10-2000 for TT and EE (tested with CLASS unlensed+pp as input).

Lensing potential C_l^phiphi

clax.compute_cl_pp(pt, params, bg, th, l_max, *, nonlinear="none", pt_mpk=None) uses the source-based Limber kernel (CLASS transfer.c + harmonic.c). pt_mpk (from perturbations_solve_mpk) is required for nonlinear="halofit" and "ept", whose P_lin lookups start at z=0, beyond the C_l solve's grid; it is ignored for "none".

With pt_k_max_cl >= 5.0 Mpc^-1 (required for Halofit's σ(R) bisection), measured residuals at the default cosmology:

Absolute |C_l^phiphi_clax / C_l^phiphi_CLASS - 1| at the default cosmology:

Multipole l	nonlinear="none"	nonlinear="halofit"	NL/lin ratio agreement
100	0.18%	0.16%	0.02%
200	0.14%	0.10%	0.05%
500	0.23%	0.15%	0.09%
1000	0.12%	0.08%	0.04%
1500	0.49%	0.61%	0.11%
2000	0.71%	0.76%	0.05%
2500	0.39%	1.16%	0.76%

Both nonlinear modes share the same source-Limber kernel, so they track each other closely — the absolute Halofit residual is dominated by the common linear-kernel systematic, with a small extra contribution from the R(k, z) accuracy that the third column isolates. Halofit injection multiplies the lensing source by sqrt(R(k, z(τ))) where R = P_NL/P_lin is computed on a 100-point z-grid via vmap(compute_pk_nonlinear) with log-log k-extension to k_max=20 Mpc^-1 (CLASS itself uses a dedicated nonlinear k-grid that extends past the perturbation k-range; the extension provides the equivalent coverage). Sub-percent absolute residual across all ℓ ≤ 2000; ~1% at ℓ=2500 where linear-kernel and R-residual systematics add constructively. Narrower k-grids (pt_k_max_cl < 5) gracefully degrade to no NL correction (R = 1, per CLASS fourier.c:1706-1716).

Multi-cosmology validation

Validated at 10 LCDM parameter variations (omega_b, omega_cdm, h, n_s, tau_reio at +/-20%):

TT: sub-0.5% at ALL l for ALL 10 cosmologies
EE: sub-0.3% at l>=100 for all cosmologies

Matter power spectrum P(k)

Single-mode compute_pk(params, prec, k) at PrecisionParams.planck_fast() (as it was before the Oct 2026 retune: 60 k/decade, l_max=50, ncdm_q_size=5, rtol=1e-6; now l_max=17, rtol=1e-4 / atol=1e-6, Rodas5) vs CLASS v3.3.4 linear pk_lin(k, z=0) at default Planck 2018 LCDM:

k [Mpc^-1]	clax / CLASS	Error
0.003	0.9983	-0.17%
0.010	1.0025	+0.25%
0.050	1.0004	+0.04%
0.100	1.0007	+0.07%
0.300	0.9997	-0.03%
1.000	0.9980	-0.20%

Median |error| ≈ 0.07%, worst-case 0.25%. Underlying perturbation evolution (δ_cdm and δ_b at synchronous gauge) matches CLASS to <0.05% — most of any remaining residual is from photon-hierarchy and IC corrections at next order.

Pipeline accuracy

Module	Accuracy	Notes
Background (H, D_A, r_s)	< 0.01%	6+ significant digits
Derived epochs (z_d, r_s(z_d); z_star, tau_star, r_s(z_star))	2e-6 (20000-point grid) / 4e-5 (`fit_cl`, 3000 points) vs CLASS RECFAST, 11 cosmologies	kappa_b = 1 and the visibility peak located sub-grid; differentiable (#44)
Thermodynamics (x_e, T_b)	x_e 2e-5 (20000-point grid) / 1.1e-4 (`fit_cl`, 3000 points) over z 800-5000, and x_e 2e-5 / 7.4e-5 with T_b 6e-5 / 1.7e-4 over z 100-700, vs CLASS RECFAST (Heswitch=1), 11 cosmologies	CLASS's Saha -> ODE staging, advanced by an L-stable ROS2 step; 2.4e-4 above z = 5000 (HeIII stage)
Visibility function g(tau)	0.04%	Bisection reionization, corrected kappa
Perturbation ODE (Phi, Psi)	0.01-0.25%	Gauge-corrected at recombination
AD gradients (dP(k)/d(params))	omega_cdm: within 0.1% at five LCDM points (k = 0.1 Mpc^-1, forward and both reverse modes equal to 1e-8); omega_b: within 0.03% (k = 0.05 Mpc^-1, production reverse mode)	vs central finite differences; the finite differences themselves scatter by ~0.1% between steps. Before #55 (ADR 0004) AD was +0.6-0.8% off along omega_cdm and 4% along omega_b

Performance

Preset	Background	Thermo	Perturbations	Harmonic	Total	GPU
planck_cl (Oct 2026 retune; `scripts/benchmark_speed.py`, job 24668)	0.6s	63s	47s	13s	124s	V100
fit_cl (Oct 2026 retune, ncdm_q_size 0; same script and job)	0.6s	2.1s	8.4s	7.6s	18.6s	V100
fit_cl (Oct 22, 2026, 5 ncdm q-bins, l_max_ncdm 8, no fluid approximation, #57; `diags/diag_fitcl_qgrid.py`, job 27746, cached medians; 21.6 s on another node, job 27711)	0.5s	1.7s	8.9s	7.3s	18.5s	V100
planck_cl (before the retune)	1s	53s	401s	33s	487s	H100

fit_cl uses 20 k/decade, l_max=17, 2000 tau points, 3000 thermodynamics points, table-based Bessel (all l at once, n_k_fine 5000, l_max 1500), rtol=1e-4 / atol=1e-6, max_steps=8192, a 5 q-bin massive-neutrino hierarchy truncated at l_max_ncdm=8 and followed to today without the fluid approximation (#57, Oct 22, 2026), and the Rodas5 Rosenbrock solver with cubic Hermite dense output. Suitable for HMC/fitting at <2% TT/EE at l<=500: 0.48% TT and 1.21% EE at the massless fiducial vs CLASS RECFAST (job 27746; 0.69% / 1.25% before #53, job 24668), and with the default 0.06 eV neutrino against CLASS HyRec references on the five-point LCDM grid within 0.69% TT, 1.65% EE and 1.13% TE/sqrt(TT EE); 0.52% / 1.18% / 0.87% at 0.15 eV, 0.64% / 1.16% / 0.86% at 0.30 eV, 0.63% / 1.19% / 0.85% at 0.50 eV, flat in the neutrino mass (jobs 27746, 27711). planck_cl uses 60 k/decade, l_max=17, rtol=1e-4 / atol=1e-6, max_steps=8192, n_k_fine 20000 and Rodas5: within 0.26% TT of CLASS to l=1000 and 0.46% EE to l=500 (0.98% EE to l=1000, one sparse-l-grid multipole at l~526). All times are JIT-cached (second call onwards); benchmark_speed.py keeps planck_cl's 5 neutrino q-bins at m_ncdm=0, so its perturbation stage is ~30 s without them.

Quick start

import jax
import clax
from clax import CosmoParams, PrecisionParams

# Background + thermodynamics
result = clax.compute(CosmoParams(h=0.6736, omega_b=0.02237))
print(result.bg.H0)            # Hubble constant [Mpc^-1] (CLASS internal units)
print(result.bg.conformal_age) # conformal age in Mpc

# Practical dense-spectrum / reusable-table workflow: solve once on an
# internal k-grid, then interpolate P(k) to the requested points.
pk_table = clax.compute_pk_table(CosmoParams(), k_eval=[1e-3, 5e-2, 1.0])
print(pk_table.pk_grid)
print(pk_table.pk(0.05))

# Exact single-k diagnostic / spot-check path
pk = clax.compute_pk(CosmoParams(), k=0.05)

# Local gradient at one k-mode
grad = jax.grad(lambda p: clax.compute_pk(p, k=0.05))(CosmoParams())
print(grad.omega_cdm)  # dP(k)/d(omega_cdm)

# Practical multi-k gradient workflow: differentiate one reusable table solve
# instead of many repeated exact single-k solves.
grad_table = jax.grad(
    lambda p: jax.numpy.sum(
        clax.compute_pk_table(p, k_eval=[1.5e-2, 3.0e-2, 9.0e-2, 2.5e-1]).pk_grid
    )
)(CosmoParams())
print(grad_table.h)  # d(sum_i P(k_i))/dh

# Angular power spectra (C_l) -- science quality
from clax.perturbations import perturbations_solve
from clax.harmonic import compute_cl_tt_interp, compute_cl_ee_interp
prec = PrecisionParams.planck_cl()
params = CosmoParams()
bg = clax.background_solve(params, prec)
th = clax.thermodynamics_solve(params, prec, bg)
pt = perturbations_solve(params, prec, bg, th)
# nonlinear lookups start at z=0, which needs the solve that reaches today (#42)
pt_mpk = clax.perturbations_solve_mpk(params, prec, bg, th)
cl_tt = compute_cl_tt_interp(pt, params, bg, [30, 100, 200])
cl_ee = compute_cl_ee_interp(pt, params, bg, [30, 100, 200])

# CMB lensing potential C_l^phiphi (linear or Halofit-corrected)
cl_pp_lin = clax.compute_cl_pp(pt, params, bg, th, l_max=2500)
cl_pp_nl  = clax.compute_cl_pp(pt, params, bg, th, l_max=2500,
                                nonlinear="halofit", pt_mpk=pt_mpk)

# Lens the unlensed CMB spectra
cl_tt_lensed, cl_ee_lensed, cl_te_lensed, cl_bb_lensed = clax.lens_cls(
    cl_tt_unlensed, cl_ee_unlensed, cl_te_unlensed, cl_bb_unlensed,
    cl_pp_nl, l_max=2500)

Installation

Requires Python >= 3.10.

pip install jax jaxlib diffrax equinox jaxtyping

# Clone and install
git clone https://github.com/smsharma/clax.git
cd clax
pip install -e .

# Run tests
pip install pytest
pytest tests/ -v
pytest tests/ -v -m "not slow"  # skip slow integration tests

Architecture

A sequential pipeline of pure functions, each returning a frozen PyTree:

CosmoParams --> background --> thermodynamics --> perturbations --> primordial
                                                                       |
                                             lensing <-- harmonic <-- transfer <-- nonlinear

Key design choices:

CosmoParams fields are JAX-traced for automatic differentiation. PrecisionParams fields are static (control array shapes, not traced).
Full Boltzmann hierarchy with smooth RSA damping post-recombination. Full ncdm Psi_l(q) phase-space hierarchy (5 q-bins x 18 multipoles). TCA (tight-coupling approximation) with CLASS-matching dual criteria for numerical stability.
Perturbation ODE solved via Diffrax with either Kvaerno5 (ESDIRK, default) or Rodas5 (8-stage Rosenbrock with cubic Hermite dense output; default in fit_cl, planck_cl and planck_fast since Oct 2026); Rodas5Batched shares time-stepping across k-modes for batched compute_pk_table solves.
compute_pk_table() / compute_pk_interpolator() are the practical dense-spectrum and reusable-table P(k) APIs, especially on GPU; exact compute_pk() remains the single-mode reference path.
Practical reverse-mode P(k) objectives should usually differentiate one table-backed multi-k solve, not many repeated exact compute_pk() calls.
Backend-aware batching over k-modes for GPU/CPU perturbation solves.
RecursiveCheckpointAdjoint for memory-efficient reverse-mode AD through the ODE solve.
Synchronous gauge throughout (matching CLASS default).
RECFAST recombination matching CLASS wrap_recfast.c (Peebles C with fudge, Gaussian K correction, CLASS's Saha -> ODE staging advanced by an L-stable second-order ROS2 step).
Source-interpolated C_l integration: source functions S(k,tau) interpolated to fine k-grid (10000 points) before line-of-sight integration, resolving the oscillatory T_l(k) transfer function robustly.
JIT-compiled: all solve functions + per-l harmonic functions cached for 2x speedup on repeated calls.

Choosing an adjoint mode

PrecisionParams.ode_adjoint controls Diffrax reverse-mode AD through the ODE solves. clax currently supports:

"recursive_checkpoint": production default and recommended choice
"direct": optional alternate reverse pass for diagnostics or environment-specific benchmarking

Use "recursive_checkpoint" by default on both CPU and GPU. That includes:

laptop / local CPU development
gating gradient tests
production P(k) / C_l gradient workflows on GPU

Use "direct" only after validating it on the exact environment and problem size you care about. It is most appropriate for:

reduced or short ODE solves where memory is easy to bound
adjoint debugging, where you want to compare reverse-pass implementations
target-specific benchmarking after AD-vs-FD checks pass

Do not assume a CPU validation transfers to GPU, or vice versa. In particular, the single-mode perturbation solve's DirectAdjoint path has shown unstable density-parameter gradients on a CPU/macOS checkout even when the forward solve and thermodynamics AD were healthy.

Before switching from the default, validate on the target setup:

compare AD vs central finite differences on representative P(k) probes
include density parameters (h, omega_b, omega_cdm) plus amplitude/tilt probes
check runtime and memory, not just gradient agreement
keep recursive_checkpoint as the reference for gating tests unless the alternate mode is revalidated

Example:

from dataclasses import replace
from clax import CosmoParams, PrecisionParams, compute_pk

params = CosmoParams()
prec = PrecisionParams()  # defaults to ode_adjoint="recursive_checkpoint"
pk = compute_pk(params, prec, k=0.05)

# Optional diagnostic / benchmark mode; validate before trusting it.
prec_direct = replace(prec, ode_adjoint="direct")
pk_direct = compute_pk(params, prec_direct, k=0.05)

Source modules

Module	Description
`constants.py`	Physical constants matching CLASS
`params.py`	`CosmoParams` (traced) and `PrecisionParams` (static)
`interpolation.py`	Pytree-registered `CubicSpline`
`ode.py`	Diffrax ODE solver wrappers
`rosenbrock.py`	Rodas5 / Rodas5Batched Rosenbrock ODE solver
`background.py`	Friedmann equation, distances, growth factor
`thermodynamics.py`	RECFAST recombination, visibility function
`perturbations.py`	Full scalar + tensor Boltzmann hierarchy
`primordial.py`	Power-law scalar and tensor spectra
`bessel.py`	Spherical Bessel functions j_l(x)
`transfer.py`	Linear matter P(k) from perturbation solve
`harmonic.py`	C_l^TT/EE/TE/BB from line-of-sight integration
`lensing.py`	C_l^phiphi (source-Limber, optional Halofit / clax-pt NL) and lensed C_l (correlation-function method)
`nonlinear.py`	HaloFit (Takahashi 2012 + Bird 2012 ν correction)
`ept.py`	One-loop EFTofLSS via FFTLog (CLASS-PT algorithm in JAX); real-space and RSD multipoles, IR resummation, bias
`shooting.py`	theta_s -> H0 via Newton + `custom_vjp`

Precision presets

PrecisionParams provides six presets controlling the accuracy/speed tradeoff:

Preset	k/decade	l_max	k_max	Use case
`fit_cl()`	20	17	1.0	HMC / fitting (<2% with massive neutrinos to 0.5 eV, ~20 s V100)
`planck_fast()`	60	17	1.0	planck_cl with th_n_points 20000, l_max 1500
`fast_cl()`	15	25	0.15	Quick iteration, testing
`medium_cl()`	20	50	0.3	Moderate accuracy
`planck_cl()`	60	17	1.0	Science-grade (~100 s V100)
`science_cl()`	200	50	0.35	Sub-percent C_l

For science-grade results, use compute_cl_tt_interp / compute_cl_ee_interp which interpolate source functions to a fine k-grid (10000 points) before computing the transfer integral. This is robust regardless of the perturbation k-density.

Cosmological parameters

Default parameters correspond to Planck 2018 best-fit LCDM:

Parameter	Default	Description
`h`	0.6736	H0 / (100 km/s/Mpc)
`omega_b`	0.02237	Physical baryon density
`omega_cdm`	0.1200	Physical CDM density
`ln10A_s`	3.0445	Log primordial amplitude (A_s = 2.1e-9)
`n_s`	0.9649	Scalar spectral index
`tau_reio`	0.0544	Reionization optical depth
`m_ncdm`	0.06 eV	Neutrino mass (single species)
`w0`, `wa`	-1.0, 0	CPL dark energy equation of state

Known limitations

Speed: fit_cl preset targets HMC / fitting; planck_cl runs in ~124 s on a V100 for science-grade work (Oct 2026 retune; 487 s on an H100 and 4679 s on a V100 before it). Further speedup requires fewer k-modes or float32 (currently infeasible with jax_enable_x64=True).
TT l=400-800: +0.10-0.18% residual from SW+Doppler source amplitude (~~0.06% excess at k~~0.03). Comparable to CAMB-CLASS inter-code variation (~0.07%).
TT l>1200: Degrades due to k-integration under-resolution (Bessel oscillation period constant in k, but log-uniform grid spacing grows). Hybrid linear/log k-grid would fix this.
EE l=20-30: ~0.2% from RECFAST visibility function bias. HyRec recombination would improve to sub-0.1%.
BB tensor modes: Lensing BB is accurate (<0.5% at l<=1000). Primordial BB matches CLASS to sub-percent at l<=200 and ~2% at l=300 with compute_cl_bb(... n_k_fine=2000) at planck_cl-style precision.

References

CLASS v3.3.4: Blas, Lesgourgues & Tram (2011). arXiv:1104.2933
CLASS-PT: Chudaykin, Ivanov, Philcox & Simonović (2020). Non-linear perturbation theory extension of CLASS. arXiv:2004.10607
EFT-with-FFT: Nguyen (2026). Pedagogical notes on FFTLog computation of one-loop PT integrals. GitHub
DISCO-DJ: Hahn, List & Porqueres (2023). Differentiable Einstein-Boltzmann solver in JAX. arXiv:2311.03291
Bolt.jl: Li, Sullivan & Millea (2023). Differentiable Boltzmann solver in Julia. Zenodo: 10.5281/zenodo.10065126
Seljak & Zaldarriaga (1996). Line-of-sight integration approach. arXiv:astro-ph/9603033
Ma & Bertschinger (1995). Cosmological perturbation theory. arXiv:astro-ph/9506072

For more on the clax.ept module, see docs/clax-pt.md, docs/CLASS-PT-summary.md, and docs/FFTLog_PT.md.

License

MIT

Name		Name	Last commit message	Last commit date
clax		clax
diags		diags
docs		docs
drafts		drafts
figures		figures
notebooks		notebooks
reference_data		reference_data
scripts		scripts
slurm		slurm
tests		tests
.gitignore		.gitignore
BENCHMARK.md		BENCHMARK.md
BENCHMARK_VALIDATION_log.md		BENCHMARK_VALIDATION_log.md
CHANGELOG.md		CHANGELOG.md
CLAUDE.md		CLAUDE.md
DESIGN.md		DESIGN.md
HPC_GUIDE.md		HPC_GUIDE.md
LICENSE		LICENSE
README.md		README.md
pyproject.toml		pyproject.toml

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One-loop EFTofLSS (`clax.ept`) accuracy vs CLASS-PT

Accuracy comparison against CLASS v3.3.4

Unlensed C_l angular power spectra

Lensed C_l angular power spectra

Lensing potential C_l^phiphi

Multi-cosmology validation

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Overview

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One-loop EFTofLSS (clax.ept) accuracy vs CLASS-PT

Accuracy comparison against CLASS v3.3.4

Unlensed C_l angular power spectra

Lensed C_l angular power spectra

Lensing potential C_l^phiphi

Multi-cosmology validation

Matter power spectrum P(k)

Pipeline accuracy

Performance

Quick start

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Choosing an adjoint mode

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Cosmological parameters

Known limitations

References

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One-loop EFTofLSS (`clax.ept`) accuracy vs CLASS-PT