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Jones Ray

ScholarPulse 日报 2026-07-13

2026-07-13 学术简报:2 篇。CAMB v2通过创新的超球面贝塞尔函数处理和多项优化,实现了高精度宇宙学功率谱预测,满足未来CMB和大尺度结构调查的数值误差要求。

今日速览

序号标题来源日期主题推荐等级
1CAMB v2: cosmological power spectra for high-precision surveysarXiv2026-07-16AI-Agent高
2Ground-Side Mission Plan Compilation with Policy-as-Code Guardrails for Cloud-Native Satellite PlatformsarXiv2026-07-16MCP高

重点论文与技术动态

1. CAMB v2: cosmological power spectra for high-precision surveys

一句话结论

CAMB v2通过创新的超球面贝塞尔函数处理和多项优化,实现了高精度宇宙学功率谱预测,满足未来CMB和大尺度结构调查的数值误差要求。

核心内容

方法与数据

价值判断

摘要 Upcoming cosmic microwave background (CMB) and large-scale-structure surveys require theoretical power spectra with numerical errors well below their observational uncertainties over the scales that carry most of the constraining power. We describe a substantial update to CAMB designed to provide fast, high-precision predictions for two key outputs: the lensed CMB and matter power spectra. The central development is a new treatment of the hyperspherical Bessel functions used for line-of-sight integration in non-flat cosmologies. A leading-order Olver construction maps the curved radial equation onto the flat spherical Bessel equation by matching their actions through the turning point. The resulting approximation is exact in the flat limit, remains smooth through the turning point, and reduces near flatness to a simple rescaling of the flat Bessel argument and amplitude. We also describe updated integrators, a recalibrated fast recombination model, stabilized parameterized post-Friedmann dark-energy evolution, and improvements to CMB lensing accuracy. Numerical convergence is assessed by comparing unboosted default results against more converged calculations. The defaults meet conservative $10^{-3}$ pointwise convergence targets over the main lensed-CMB and quasi-linear matter-power ranges relevant for future surveys. Errors measured in typical runs are substantially smaller than this. We also describe the convention dependence of the nominally linear matter power spectrum when a homogeneous calculation attempts to represent the effects of reionization heating. Essentially all of the new algorithms and code were developed with LLMs or AI agents under human supervision.

2. Ground-Side Mission Plan Compilation with Policy-as-Code Guardrails for Cloud-Native Satellite Platforms

一句话结论

本文提出Satellite Mission Compiler,一个四阶段管道,将人类编写的卫星任务计划编译为云原生卫星运行时可消费的容器工作流工件,确保结构合规与策略安全。

核心内容

方法与数据

价值判断

摘要 Onboard cloud-native runtimes for satellites are emerging on multiple tracks (ORCHIDE, Axiom Space's AxDCU-1, Kepler's Jetson nodes), but each assumes that the workflow artifacts it executes arrive from the ground. ORCHIDE's architecture document D3.1 states explicitly that "only the Deferred Phase is part of the ORCHIDE scope," and no open-source ground-side toolchain has been released by the consortium. We present Satellite Mission Compiler, a four-stage pipeline that addresses this gap: it takes a human-authored mission plan, checks it against machine-checkable structural and policy rules, and compiles it into the container-workflow artifacts that cloud-native satellite runtimes consume. The pipeline parses the plan against a Pydantic schema derived from public ORCHIDE materials, evaluates it against an OPA/Rego policy package of ten deny rules with documented provenance, compiles it into a typed WorkflowIntent intermediate representation, and renders it as Argo Workflow DAGs and Kueue Job manifests with Dynamic Resource Allocation (DRA) support. We classify pre-uplink loss events into four severity tiers tied to specific schema and policy checks, and anchor the layered-validation design in the safety reading of defense-in-depth (NASA-STD-8739.8B) rather than the security reading (NIST SP 800-53). The implementation is validated by golden translation evaluations, argo lint, an in-process baseline that reproduces OPA's decisions, and live single-node cluster submission, including a DRA-backed GPU admission cascade on Kueue v0.17.3 (re-validated on v0.18.3) and, on v0.18.3, a unified GPU+CPU device-class quota with a scheduler-level accelerator fallback. Six Model Context Protocol (MCP) tools expose the pipeline to AI agents. The compiler is released under EUPL-1.2 (DOI 10.5281/zenodo.21228150).