{"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/learning-to-concentrate-multi-tracer","title":"Learning to Concentrate: Multi-tracer Forecasts on Local Primordial Non-Gaussianity with Machine-Learned Bias","arxiv_id":"2303.08901","date":"2023-03-15","proceeding":null,"authors":["James M Sullivan","Tijan Prijon","Uros Seljak"],"abstract":"Local primordial non-Gaussianity (LPNG) is predicted by many non-minimal models of inflation, and creates a scale-dependent contribution to the power spectrum of large-scale structure (LSS) tracers, whose amplitude is characterized by $b_{\\phi}$. Knowledge of $b_{\\phi}$ for the observed tracer population is therefore crucial for learning about inflation from LSS. Recently, it has been shown that the relationship between linear bias $b_1$ and $b_{\\phi}$ for simulated halos exhibits significant secondary dependence on halo concentration. We leverage this fact to forecast multi-tracer constraints on $f_{NL}^{\\mathrm{loc}}$. We train a machine learning model on observable properties of simulated Illustris-TNG galaxies to predict $b_{\\phi}$ for samples constructed to approximate DESI emission line galaxies (ELGs) and luminous red galaxies (LRGs). We find $\\sigma(f_{NL}^{\\mathrm{loc}}) = 2.3$, and $\\sigma(f_{NL}^{\\mathrm{loc}}) = 3.7$, respectively. These forecasted errors are roughly factors of 3, and 35\\% improvements over the single-tracer case for each sample, respectively. When considering both ELGs and LRGs in their overlap region, we forecast $\\sigma(f_{NL}^{\\mathrm{loc}}) = 1.5$ is attainable with our learned model, more than a factor of 3 improvement over the single-tracer case, while the ideal split by $b_{\\phi}$ could reach $\\sigma(f_{NL}^{\\mathrm{loc}}) <1$. We also perform multi-tracer forecasts for upcoming spectroscopic surveys targeting LPNG (MegaMapper, SPHEREx) and show that splitting tracer samples by $b_{\\phi}$ can lead to an order-of-magnitude reduction in projected $\\sigma(f_{NL}^{\\mathrm{loc}})$ for these surveys.","url_abs":"https://arxiv.org/abs/2303.08901v2","url_pdf":"https://arxiv.org/pdf/2303.08901v2.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":"learning-to-concentrate-multi-tracer","repo_url":"https://github.com/jmsull/ml_fnl_forecast","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}