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arxiv: 2605.23841 · v1 · pith:YALMXX53new · submitted 2026-05-22 · 🌌 astro-ph.CO · astro-ph.IM

cloelike: A Python Library for Cosmological Likelihood Inference in the Euclid Era

Marco Bonici , Guadalupe Ca\~nas-Herrera , Pedro Carrilho , Santiago Casas , Chiara Moretti , Andrea Pezzotta (cloe-org maintainers) , Zahra Baghkhani , Carmelita Carbone
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Martin Crocce Jip de Buck Klara Bertmann Nastassia Grimm Martin K\"archer Felicitas Keil Davide Sciotti Peter L. Taylor Nicolas Tessore Isaac Tutusaus Casper Vedder (cloe-org contributors)
This is my paper

Pith reviewed 2026-05-25 02:56 UTC · model grok-4.3

classification 🌌 astro-ph.CO astro-ph.IM
keywords Python packagecosmological likelihoodsEuclid missionweak lensinggalaxy clusteringlarge-scale structureGaussian likelihoodjoint probes
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The pith

cloelike supplies modular Gaussian likelihood classes for Euclid's main large-scale structure observables.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces cloelike as a Python package that delivers composable Gaussian likelihoods for weak lensing, photometric galaxy clustering, galaxy-galaxy lensing, full-shape spectra, and BAO measurements targeted by the Euclid mission. It forms the core of the CLOE ecosystem and connects directly to cloelib for predictions and euclidlib for official data products. The library supports all joint probe combinations such as 3x2pt and 2x2pt analyses in both harmonic and real space. A sympathetic reader would care because the package is already in use for internal Euclid Consortium work and is released openly to enable consistent, reproducible cosmological inference.

Core claim

cloelike implements Gaussian likelihood classes for harmonic angular power spectra and real-space two-point correlation functions covering WL, GCph, and GGL in joint combinations, plus spectroscopic full-shape power spectrum multipoles and BAO, with direct interfaces to theoretical prediction and official data libraries.

What carries the argument

Modular, composable Gaussian likelihood classes that handle the listed observables and their joint combinations.

If this is right

  • Joint analyses of multiple probes such as 3x2pt become directly usable without custom likelihood code.
  • Official Euclid data products can be read and analyzed through the provided interfaces.
  • Internal and external teams can adopt the same likelihood definitions for consistent results.
  • Reproducibility improves because the package is openly released for community inspection.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same modular structure could be extended to additional observables or non-Gaussian likelihood forms by later developers.
  • Other large-scale structure surveys with similar probes might adopt or adapt the classes for their own pipelines.
  • Standardized likelihoods reduce the chance that differences in analysis codes produce spurious tensions between results.

Load-bearing premise

The implemented likelihood classes correctly reproduce the intended Gaussian forms and interface without error with the prediction and data libraries.

What would settle it

Running cloelike on a fixed cosmological model and data vector and comparing the resulting likelihood values to an independent manual implementation of the same Gaussian forms would reveal any mismatch.

read the original abstract

cloelike is a Python package providing modular, composable Gaussian likelihood classes for the main cosmological large-scale structure observables targeted by the ESA Euclid space mission. It is a core component of the CLOE (Cosmology Likelihood for Observables in Euclid) ecosystem and interfaces directly with cloelib for theoretical predictions and euclidlib for reading official Euclid data products. The package implements Gaussian likelihoods covering harmonic angular power spectra and real-space two-point correlation functions for weak lensing (WL), photometric galaxy clustering (GCph), and Galaxy-Galaxy Lensing (GGL) in all joint probe combinations (3x2pt, 2x2pt), as well as spectroscopic full-shape power spectrum multipoles, and baryonic Acoustic oscillations (BAO). cloelike is actively used in internal Euclid Consortium analyses and is openly released to support community validation and reproducibility.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript introduces cloelike, a Python package that supplies modular, composable Gaussian likelihood classes for the primary large-scale structure observables targeted by the ESA Euclid mission (WL, GCph, GGL, full-shape spectra, and BAO) in joint combinations. It forms part of the CLOE ecosystem, interfaces with cloelib for theory predictions and euclidlib for data products, and is stated to be in active internal Euclid Consortium use with an open release for community validation.

Significance. If the implementation is correct and documented with verification, the library would provide a reusable, standardized component for Euclid-era cosmological inference, supporting reproducibility across analyses that combine multiple probes. The open release and stated internal adoption are positive features for community uptake.

major comments (2)
  1. [Implementation and validation sections] Implementation and validation sections: The manuscript asserts that the package implements correct Gaussian likelihoods for the listed observables and interfaces, yet provides no numerical tests, comparisons to analytic expectations, or cross-checks against existing codes. This verification is load-bearing for the central claim that the classes reproduce the intended forms without error.
  2. [Usage and interface description] Usage and interface description: No concrete code examples or API signatures are shown that would allow a reader to confirm the claimed modularity and composability for 3x2pt, 2x2pt, and spectroscopic combinations.
minor comments (1)
  1. [Abstract and introduction] The abstract and introduction repeat the list of observables without a concise table summarizing which likelihood classes cover which probe combinations.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments, which highlight important aspects for strengthening the manuscript. We address each major comment below and will incorporate revisions to address the concerns raised.

read point-by-point responses

  1. Referee: [Implementation and validation sections] Implementation and validation sections: The manuscript asserts that the package implements correct Gaussian likelihoods for the listed observables and interfaces, yet provides no numerical tests, comparisons to analytic expectations, or cross-checks against existing codes. This verification is load-bearing for the central claim that the classes reproduce the intended forms without error.

    Authors: We agree that the current version of the manuscript does not include explicit numerical validation or cross-checks. In the revised manuscript we will add a dedicated validation section containing comparisons against analytic expectations for simplified cases, unit tests for the Gaussian likelihood forms, and cross-checks with independent implementations where relevant to demonstrate correctness of the implemented classes. revision: yes

  2. Referee: [Usage and interface description] Usage and interface description: No concrete code examples or API signatures are shown that would allow a reader to confirm the claimed modularity and composability for 3x2pt, 2x2pt, and spectroscopic combinations.

    Authors: We acknowledge that the manuscript currently lacks concrete usage examples. The revised version will include code snippets illustrating the API signatures and usage patterns for constructing joint likelihoods in 3x2pt, 2x2pt, and spectroscopic configurations, thereby demonstrating the modularity and composability of the classes. revision: yes

Circularity Check

0 steps flagged

Software library description contains no derivations or predictions

full rationale

The paper is a software release description for the cloelike Python package. It details modular Gaussian likelihood classes for Euclid observables (WL, GCph, GGL, full-shape spectra, BAO) and their interfaces with cloelib and euclidlib, but presents no equations, derivations, fitted parameters, predictions, or uniqueness theorems. The central claim is the existence and design of the library itself, which is self-contained as a code artifact and does not reduce to any input by construction or self-citation chain. No load-bearing steps exist that match the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is a software library announcement with no scientific derivation, fitted parameters, or postulated entities.

pith-pipeline@v0.9.0 · 5770 in / 998 out tokens · 16170 ms · 2026-05-25T02:56:44.257775+00:00 · methodology

discussion (0)

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