{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/rm-s-scriptsize-im-big-galaxy-clustering","title":"Galaxy Clustering Analysis with SimBIG and the Wavelet Scattering Transform","arxiv_id":"2310.15250","date":"2023-10-23","proceeding":null,"authors":["Bruno Régaldo-Saint Blancard","ChangHoon Hahn","Shirley Ho","Jiamin Hou","Pablo Lemos","Elena Massara","Chirag Modi","Azadeh Moradinezhad Dizgah","Liam Parker","Yuling Yao","Michael Eickenberg"],"abstract":"The non-Gaussisan 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 $\\Lambda$CDM parameters $\\Omega_m$, $\\Omega_b$, $h$, $n_s$, and $\\sigma_8$ from the BOSS CMASS galaxy sample by combining the wavelet scattering transform (WST) with a simulation-based inference approach enabled by the ${\\rm S{\\scriptsize IM}BIG}$ 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 ${\\rm S{\\scriptsize IM}BIG}$ 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 2,000 test simulations. When probing scales down to $k_{\\rm max}=0.5~h/\\text{Mpc}$, we are able to derive accurate posterior estimates that are robust to the change of forward model for all parameters, except $\\sigma_8$. We mitigate the robustness issues with $\\sigma_8$ by removing the WST coefficients that probe scales smaller than $k \\sim 0.3~h/\\text{Mpc}$. Applied to the BOSS CMASS sample, our WST analysis yields seemingly improved constraints obtained from a standard PT-based power spectrum analysis with $k_{\\rm max}=0.25~h/\\text{Mpc}$ for all parameters except $h$. 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.","url_abs":"https://arxiv.org/abs/2310.15250v2","url_pdf":"https://arxiv.org/pdf/2310.15250v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"rm-s-scriptsize-im-big-galaxy-clustering","repo_url":"https://github.com/bregaldo/galactic_wavelets","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"BSD-3-Clause"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2310.15250","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.15250"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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