arxiv: 2604.25557 · v1 · submitted 2026-04-28 · ✦ hep-ph · physics.comp-ph

Recognition: unknown

Jarvis-HEP: A lightweight Python framework for workflow composition and parameter scans in high-energy physics

Erdong Guo, Jin Min Yang, Paul Jackson, Pengxuan Zhu

Authors on Pith no claims yet

Pith reviewed 2026-05-07 15:59 UTC · model grok-4.3

classification ✦ hep-ph physics.comp-ph

keywords high-energy physicsworkflow compositionparameter scansPython frameworkYAML configurationcomputational workflowsphenomenology

0 comments

The pith

Jarvis-HEP is a lightweight Python framework that composes workflows and runs parameter scans in high-energy physics using YAML specifications.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

High-energy physics phenomenology routinely requires chaining multiple computational tools to compute observables, likelihoods, and constraints across parameter spaces. The paper presents Jarvis-HEP as a framework that lets users declare these chains in YAML files, automatically resolves task dependencies, plugs in external packages or internal modules, and schedules asynchronous execution. Built-in sampling backends are included for exploratory scans. A reader would care because this approach replaces ad-hoc scripting with a reusable, dependency-managed pipeline that can handle both synthetic tests and full phenomenological studies.

Core claim

The paper claims that a single lightweight Python package can unify workflow specification, dependency tracking, modular calculator integration, and asynchronous scheduling, while adding several sampling methods, so that external HEP tools and custom components run together in reproducible multi-step calculations.

What carries the argument

YAML-based workflow specification with dependency-aware execution that orchestrates modular calculators and sampling backends.

If this is right

Users define multi-tool studies declaratively in configuration files instead of writing custom glue code for each connection.
Built-in sampling backends enable immediate parameter-space exploration without separate scan scripts.
External software packages and internal components coexist in one dependency-managed workflow.
Asynchronous scheduling runs independent tasks in parallel, reducing wall-clock time for scans.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Standardized YAML workflow files could allow research groups to share complete analysis pipelines more easily than before.
Enforced dependency tracking may reduce common errors when linking different physics calculators.
The framework's value would be clearest in large scans where manual orchestration becomes error-prone.

Load-bearing premise

The described YAML specification, dependency handling, and sampling features will connect cleanly to real external high-energy physics packages and run with acceptable performance in practice.

What would settle it

A test workflow that links Jarvis-HEP to standard tools such as MadGraph or Pythia and encounters unresolved dependencies or slow execution would show the integration claim does not hold.

Figures

Figures reproduced from arXiv: 2604.25557 by Erdong Guo, Jin Min Yang, Paul Jackson, Pengxuan Zhu.

**Figure 1.** Figure 1: Conceptual overview of the Jarvis-HEP workflow. The YAML input file and packages in the yellow boxes are user-provided. Items in the green box are outputs from Jarvis-HEP. The blue box illustrates the architecture and workflow of Jarvis-HEP. The "Worker Factory" manages multiple computing layers (L1, L2, and L3), each containing one or more computing module pools, which handle a computing software package … view at source ↗

**Figure 2.** Figure 2: Flow chart of scan defined in bin/Example_Random.yaml, which is automatically generated by Jarvis-HEP. • initialization: To prevent program corruption from multiple calls during runtime, users should initialize the package before calculations by clearing previous outputs and restoring the input template file. • execution: The detailed execution steps are as follows: The calculator module operates as a bl… view at source ↗

**Figure 3.** Figure 3: Data visualisation using Voronoi diagram for grid, random, and Bridson sampling results for the view at source ↗

**Figure 4.** Figure 4: Similar to Fig view at source ↗

**Figure 5.** Figure 5: Scatter plot of EggBox model using the dynesty implementation of the nested sampling method. Color coded with likelihood. the evidence can be rewritten as: Z = Z 1 0 L(X)dX (18) where L(X) represents the likelihood as a function of prior volume. This transformation enables systematic shrinkage of the prior volume through ordered likelihood sampling. The nested sampling algorithm maintains a collection … view at source ↗

**Figure 6.** Figure 6: The behaviour of dynesty algorithm in EggBox model sampling task. See also view at source ↗

**Figure 7.** Figure 7: Similar to Fig view at source ↗

**Figure 8.** Figure 8: Live computing resources monitor in Jarvis-HEP. Then Jarvis-HEP starts a text user interface application, as shown in view at source ↗

read the original abstract

High-energy physics phenomenology often requires linking multiple computational tools to evaluate observables, likelihoods, and experimental constraints across nontrivial parameter spaces. In this work, we introduce Jarvis-HEP, a lightweight Python framework for workflow composition and parameter scans in high-energy physics. The framework provides YAML-based workflow specification, dependency-aware execution, modular calculator integration, and asynchronous task scheduling for multi-step computational studies. It supports both external software packages and internally implemented components within a unified workflow, and the current implementation includes several built-in sampling backends for exploratory scans. This paper describes the design and user interface of Jarvis-HEP and illustrates its use with representative synthetic and phenomenological examples.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

Jarvis-HEP describes a Python framework for chaining HEP tools via YAML and async scheduling, but supplies no benchmarks or integration tests to show it actually works in practice.

read the letter

Jarvis-HEP is a new lightweight Python framework for composing workflows and running parameter scans in high-energy physics. It lets users specify jobs in YAML, track dependencies between steps, plug in external packages or internal calculators, and run tasks asynchronously with built-in sampling options for scans. The paper walks through the design and shows a few synthetic and phenomenological examples of how the pieces fit together. That covers a real practical need when people have to link MadGraph, Pythia, or likelihood tools across many parameter points. The architecture looks straightforward on paper and the modular approach plus async scheduling could save some scripting time for users who want something lighter than full workflow engines. The examples give a concrete sense of the config-to-execution flow. The main weakness is the complete lack of evidence that any of this works at scale or integrates cleanly. There are no timing numbers, no failure-rate data, no side-by-side comparisons against manual scripts or existing tools, and no tests with real external HEP packages. Without those, the claims about usable performance and smooth integration stay untested. The paper is aimed at HEP phenomenologists who already run multi-step calculations in Python and are looking for a simple config-driven way to manage them. A reader in that group might borrow the YAML pattern or the sampling backends even if they never install the package. For peer review I would send it if the journal has a software track, but only with the understanding that the authors need to add concrete benchmarks and real-package tests before it could be accepted.

Referee Report

2 major / 2 minor

Summary. The paper introduces Jarvis-HEP, a lightweight Python framework for workflow composition and parameter scans in high-energy physics. It provides YAML-based workflow specification, dependency-aware execution, modular calculator integration, and asynchronous task scheduling. The framework supports both external software packages and internal components, includes built-in sampling backends, and is illustrated with synthetic and phenomenological examples.

Significance. If the architecture delivers the claimed integration and performance without hidden friction, Jarvis-HEP could serve as a practical, specialized tool for HEP phenomenologists managing multi-tool workflows and parameter explorations, reducing reliance on ad-hoc scripting. The unified handling of external and internal components is a notable design choice that, if validated, would distinguish it from general workflow managers.

major comments (2)

[Examples section] The examples section: all provided illustrations use only synthetic or internal components; no concrete demonstration of integration with real external HEP packages (e.g., MadGraph, Pythia, or likelihood tools) is shown, leaving the central claim that the framework 'supports both external software packages and internally implemented components within a unified workflow' unverified.
[Implementation / Features] No section on benchmarks or validation: the manuscript contains no timing data, failure-rate measurements, scalability tests, or side-by-side comparisons against manual scripting or existing tools, which is load-bearing for the assertions of 'usable performance' and 'lightweight' operation for multi-step studies.

minor comments (2)

[Abstract] The abstract states that 'several built-in sampling backends' are included but does not name or briefly describe them; this information should appear in the main text or a dedicated table for immediate clarity.
[Introduction / Conclusions] A public code repository link or installation instructions are not mentioned in the provided text; for a software framework paper this is standard and should be added.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed review and constructive suggestions. We appreciate the opportunity to clarify and strengthen the manuscript. Below we respond to the major comments and indicate the planned revisions.

read point-by-point responses

Referee: [Examples section] The examples section: all provided illustrations use only synthetic or internal components; no concrete demonstration of integration with real external HEP packages (e.g., MadGraph, Pythia, or likelihood tools) is shown, leaving the central claim that the framework 'supports both external software packages and internally implemented components within a unified workflow' unverified.

Authors: We agree that demonstrating integration with actual external HEP packages would better substantiate the framework's capabilities. The current examples were chosen for self-containment and to focus on the workflow mechanics. The design includes support for external tools via subprocess calls and Python interfaces, as described in the implementation section. In the revised version, we will incorporate an additional example that integrates with a real external package, such as a wrapper for a simple MadGraph simulation or a likelihood tool, to explicitly verify the unified handling of external and internal components. revision: yes
Referee: [Implementation / Features] No section on benchmarks or validation: the manuscript contains no timing data, failure-rate measurements, scalability tests, or side-by-side comparisons against manual scripting or existing tools, which is load-bearing for the assertions of 'usable performance' and 'lightweight' operation for multi-step studies.

Authors: We acknowledge the value of empirical validation for the performance claims. The manuscript emphasizes the architectural design and user interface, supported by illustrative examples. To address this, we will add a dedicated section on validation and benchmarks in the revision. This will include timing measurements for workflow execution, error handling statistics from test cases, scalability tests with increasing task complexity, and comparisons to equivalent manual Python scripts for parameter scans. These additions will provide concrete evidence for the 'lightweight' and 'usable performance' aspects. revision: yes

Circularity Check

0 steps flagged

No circularity: software framework description with no derivations or fitted predictions

full rationale

The paper is a pure software description introducing Jarvis-HEP. It specifies YAML workflows, dependency handling, modular integration, and async scheduling, then illustrates usage with synthetic and phenomenological examples. No equations, parameter fits, predictions, or uniqueness theorems appear. All load-bearing claims are architectural statements supported by design description rather than any reduction to self-citation chains or input data. The absence of mathematical content makes every enumerated circularity pattern inapplicable by definition.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The work is a software framework announcement rather than a theoretical derivation. No free parameters, axioms, or invented physical entities are required or introduced.

pith-pipeline@v0.9.0 · 5411 in / 1237 out tokens · 34081 ms · 2026-05-07T15:59:35.001242+00:00 · methodology

discussion (0)

Reference graph

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