An end-to-end, reproducible Λ⁰ analysis

Last updated on 2026-11-10 | Edit this page

Overview

Questions

  • How do assistant + server + skill compose into one analysis?
  • How does the kernel scale from one file to many?
  • How is the yield extracted and made reproducible?

Objectives

  • Set up any MCP client for the end-to-end run in three steps.
  • Scale the analysis from one file to a full dataset.
  • Accept or reject an agent-produced yield using the audit checklist.

Continue in the directory from Episodes 3 and 4: servers running (eic-mcp status), AGENTS.md and the lambda-fit skill installed, and /mcp in opencode listing uproot, xrootd, and rucio.

The composed pipeline

The skill (Episode 4) gives the steps, the uproot server (Episode 3) reads the data, and the agent loop (Episode 1) runs and checks it.

%%{init: {'theme':'base', 'themeVariables': {'fontSize':'15px','lineColor':'#94a3b8','edgeLabelBackground':'#e2e8f0','clusterBkg':'#1f293720','clusterBorder':'#94a3b8','titleColor':'#94a3b8'}}}%%
flowchart LR
    accTitle: {End-to-end agent run}
    accDescr: {End-to-end agent run}
    A["resolve input<br/>root:// file or file list"]:::data --> B["build m(p,π) histogram<br/>uproot MCP"]:::tool
    B --> C["fit Gaussian + poly-2<br/>uproot MCP"]:::tool
    C --> D["report μ, σ, S, χ²/ndf<br/>+ provenance"]:::out
    classDef data fill:#fff4e0,stroke:#f08c00,stroke-width:1.5px,color:#5c3b00;
    classDef tool fill:#e6f7ed,stroke:#2f9e44,stroke-width:1.5px,color:#0b3d1f;
    classDef out fill:#f3e8ff,stroke:#7048e8,stroke-width:1.5px,color:#2e1065;

One file, end to end

One request runs the whole chain on the first file of the dataset:

Using the lambda-fit skill, measure the Lambda0 peak in this file:
root://epicxrd1.sdcc.bnl.gov:1095//eic/EPIC/RECO/26.04.1/epic_craterlake/DIS/BeAGLE1.03.02-1.0/eCu/ep/10x115/q2_1to1000/BeAGLE1.03.02-1.0_DIS_eCu_ep_10x115_q2_1to1000_ab.0000.eicrecon.edm4eic.root

One file gives only about 10 candidates in the fit window, so the correct answer is insufficient statistics: the skill says to stop, and a model that quotes \(\mu\) anyway has ignored it.

Callout

Smaller models take shortcuts: check the result, not the route

A smaller model may call different tools than the skill says and still get the same histogram. The audit checklist below judges the result, not the route.

Scaling up

The same kernel can run over many files and return one merged histogram:

Using the lambda-fit skill, measure the Lambda0 and anti-Lambda0 peaks in the first 10 files of
the dataset epic:/RECO/26.04.1/epic_craterlake/DIS/BeAGLE1.03.02-1.0/eCu/ep/10x115/q2_1to1000.

Ten files take about a minute and a half. For 30 files the skill makes three such calls:

Using the lambda-fit skill, repeat the measurement with the first 30 files of the same dataset.

Thirty files take about ten minutes. A reference fit:

OUTPUT 

Lambda      -> p pi-:    mu = 1116.25 +/- 0.37 MeV   sigma = 3.1 +/- 0.5 MeV   S =  97 +/- 19   chi2/ndf = 1.06
anti-Lambda -> pbar pi+: mu = 1115.50 +/- 0.25 MeV   sigma = 2.2 +/- 0.2 MeV   S = 102 +/- 15   chi2/ndf = 0.71

The histogram is the same in every run (321 and 294 entries in the window). The assistant writes its own fit, so \(\mu\), \(\sigma\), and \(S\) vary between runs within these uncertainties.

Proton–pion invariant-mass spectrum with Gaussian-plus-polynomial fits showing clear Lambda and anti-Lambda peaks near 1.1157 GeV
Fitted Λ⁰ and Λ̄ invariant-mass spectra (reference fit)

Extracting the yield

The fit model is a Gaussian signal on a second-order polynomial background over \([1.08, 1.16]\) GeV:

\[ f(m) = A \exp\!\left[ -\tfrac{1}{2} (m - \mu)^2 / \sigma^2 \right] + \left( c_0 + c_1 (m - m_\Lambda) + c_2 (m - m_\Lambda)^2 \right) \]

The polynomial absorbs the combinatorial background (Episode 2); the integrated signal is \(S = A\sqrt{2\pi}\,\sigma / (\text{bin width})\). Report \(S\) with its uncertainty alongside \(\mu\), \(\sigma\), and \(\chi^2/\text{ndf}\); a bare bin count mixes signal with background.

Audit

Callout

Audit checklist

  • Signal. \(\mu\) within a few MeV of 1.115683 GeV; \(\sigma\) consistent with detector resolution; \(\chi^2/\text{ndf}\) of order unity; \(S\) reported with an uncertainty.
  • Inputs pinned. Dataset (campaign and file list), particle masses, mass window, binning, and fit range all fixed and recorded.
  • Provenance. Tool calls and their arguments logged, so the run can be reconstructed.
  • Cost bounded. A few files during development before scaling up.
  • Oversight. A human inspected the fit before the result was reported.

The whole analysis in one prompt

Everything in Episodes 3 to 5 fits in one request, starting from nothing but the sample name:

Using the lambda-fit skill, measure the Lambda0 and anti-Lambda0 peaks in the first 30 files of
the campaign 26.04.1 BeAGLE eCu ep 10x115 reconstructed-DIS dataset.

The tools, the skill, and the criteria make this short prompt reproducible: the same request gives the same histogram on any MCP client.

Callout

Try a different beam

The same prompt works with any reconstructed-DIS DID. Nuclear beams (eCu, eAu) give the most Λ per event; ep samples need a few times more files.

The final episode lists the other MCP servers the EIC provides.

Key Points
  • One prompt runs the whole Λ⁰ analysis, from one file to many.
  • Check the fit against the audit checklist before you trust it.