High-Precision Particle Property API
A commercial-grade API that provides programmatic, real-time access to the averaged measured properties of gauge bosons, leptons, quarks, and the Higgs boson, replacing manual table lookups with structured data queries.
Concept
A specialized data-as-a-service (DaaS) API that transforms the static Summary Tables of the Review of Particle Physics into a queryable database. Instead of researchers manually extracting values from PDFs or apps, this service provides standardized JSON/REST endpoints for the most current averaged properties of known particles and the search limits for hypothetical ones (e.g., axions, dark photons).
Why now
The Review of Particle Physics [0] has synthesized 2,717 new measurements from 869 papers, creating a massive, high-density dataset. As computational physics and simulation software become more prevalent, the need to programmatically inject the most accurate, peer-reviewed averages into simulation code—rather than hard-coding values from a printed table—becomes a critical efficiency gain for researchers.
AI assessment
A low-value utility that digitizes existing public data for a tiny, budget-constrained niche that likely prefers open-source libraries over a paid API.
- Evidence strength 5/5
- The idea is a direct digitization of the Particle Data Group's comprehensive and authoritative Summary Tables.
- Market pull 2/5
- The target user base is extremely small, and national labs typically favor open-source tools or internal databases over proprietary APIs for fundamental constants.
- Novelty & moat 1/5
- There is no technical moat; this is a simple data-entry task transforming a public PDF/website into a JSON response.
- Feasibility 5/5
- Building a REST API around a static set of tables is trivial and could be completed in a few days.
- Wedge clarity 4/5
- The focus on replacing manual table lookups for simulation parameters is a sharp, specific entry point.
- Simplicity / focus 5/5
- The product is a single, focused API with one clear purpose.
Scored by AI against a fixed rubric (evidence, market, novelty, feasibility, wedge, simplicity). A prior estimate to compare ideas before real-world signal arrives.
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Who benefits
- Computational Physicistsindividual
Reduces the risk of manual transcription errors when updating simulation constants from the Review's Summary Tables.
- National Laboratoriesorganization
Ensures consistency across multiple research teams by providing a single, authoritative source of truth for particle properties.
Research it builds on
- Review of Particle PhysicsS. Navas, C. Amsler, Th. Gutsche et al. · 2024 · 3212 citationsAll ideas from this paper →
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