Galaxy clustering analysis with SimBIG and the wavelet scattering transform
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Bibliographic record
Abstract
The non-Gaussian spatial distribution of galaxies traces the large-scale structure of the Universe and therefore constitutes a prime observable to constrain cosmological parameters. We conduct Bayesian inference of the <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" display="inline"><a:mi mathvariant="normal">Λ</a:mi><a:mi>CDM</a:mi></a:math> parameters <d:math xmlns:d="http://www.w3.org/1998/Math/MathML" display="inline"><d:msub><d:mi mathvariant="normal">Ω</d:mi><d:mi>m</d:mi></d:msub></d:math>, <g:math xmlns:g="http://www.w3.org/1998/Math/MathML" display="inline"><g:msub><g:mi mathvariant="normal">Ω</g:mi><g:mi>b</g:mi></g:msub></g:math>, <j:math xmlns:j="http://www.w3.org/1998/Math/MathML" display="inline"><j:mi>h</j:mi></j:math>, <l:math xmlns:l="http://www.w3.org/1998/Math/MathML" display="inline"><l:msub><l:mi>n</l:mi><l:mi>s</l:mi></l:msub></l:math>, and <n:math xmlns:n="http://www.w3.org/1998/Math/MathML" display="inline"><n:msub><n:mi>σ</n:mi><n:mn>8</n:mn></n:msub></n:math> from the Baryon Oscillation Spectroscopic Survey CMASS galaxy sample by combining the wavelet scattering transform (WST) with a simulation-based inference approach enabled by the SimBIG forward model. We design a set of reduced WST statistics that leverage symmetries of redshift-space data. Posterior distributions are estimated with a conditional normalizing flow trained on 20,000 simulated SimBIG galaxy catalogs with survey realism. We assess the accuracy of the posterior estimates using simulation-based calibration and quantify generalization and robustness to the change of forward model using a suite of 2000 test simulations. When probing scales down to <p:math xmlns:p="http://www.w3.org/1998/Math/MathML" display="inline"><p:msub><p:mi>k</p:mi><p:mi>max</p:mi></p:msub><p:mo>=</p:mo><p:mn>0.5</p:mn><p:mtext> </p:mtext><p:mtext> </p:mtext><p:mi>h</p:mi><p:mo stretchy="false">/</p:mo><p:mi>Mpc</p:mi></p:math>, we are able to derive accurate posterior estimates that are robust to the change of forward model for all parameters, except <s:math xmlns:s="http://www.w3.org/1998/Math/MathML" display="inline"><s:msub><s:mi>σ</s:mi><s:mn>8</s:mn></s:msub></s:math>. We mitigate the robustness issues with <u:math xmlns:u="http://www.w3.org/1998/Math/MathML" display="inline"><u:msub><u:mi>σ</u:mi><u:mn>8</u:mn></u:msub></u:math> by removing the WST coefficients that probe scales smaller than <w:math xmlns:w="http://www.w3.org/1998/Math/MathML" display="inline"><w:mi>k</w:mi><w:mo>∼</w:mo><w:mn>0.3</w:mn><w:mtext> </w:mtext><w:mtext> </w:mtext><w:mi>h</w:mi><w:mo stretchy="false">/</w:mo><w:mi>Mpc</w:mi></w:math>. Applied to the Baryon Oscillation Spectroscopic Survey CMASS sample, our WST analysis yields seemingly improved constraints obtained from a standard perturbation-theory-based power spectrum analysis with <z:math xmlns:z="http://www.w3.org/1998/Math/MathML" display="inline"><z:msub><z:mi>k</z:mi><z:mi>max</z:mi></z:msub><z:mo>=</z:mo><z:mn>0.25</z:mn><z:mtext> </z:mtext><z:mtext> </z:mtext><z:mi>h</z:mi><z:mo stretchy="false">/</z:mo><z:mi>Mpc</z:mi></z:math> for all parameters except <cb:math xmlns:cb="http://www.w3.org/1998/Math/MathML" display="inline"><cb:mi>h</cb:mi></cb:math>. However, we still raise concerns on these results. The observational predictions significantly vary across different normalizing flow architectures, which we interpret as a form of model misspecification. This highlights a key challenge for forward modeling approaches when using summary statistics that are sensitive to detailed model-specific or observational imprints on galaxy clustering. Published by the American Physical Society 2024
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it