From Fit to Attribution: Support Provenance and Attributional Resolution in Cosmology

Recent work on holistic top-down confirmation has made precise why support for a model package need not transfer to its parts. A distinct downstream question remains: at what claim-resolution does a published inferential pipeline license attribution? We develop a contrast-indexed account of support provenance that identifies the highest claim-resolution carried by a documented analysis, tracking the empirical input, mediator, contrast class, decision rule, and premise carrying an inferential upgrade. Two cosmological cases yield complementary results. In the Bullet Cluster, a current dispute over dark-matter detection decomposes by claim resolution: relative to independently constrained baryonic-source alternatives, the spatial configuration licenses a coarse, functionally specified attribution—an additional gravitationally effective contribution spatially separated from the dominant hot plasma—while stronger property attributions remain open and the ontological attribution is not carried by the pipeline. In Dark Energy Spectroscopic Instrument Data Release 2, joint constraints generate additional, rule- and contrast-relative support for a phenomenological model class without by themselves identifying a physically evolving dark-energy component. The comparison yields a non-uniform result: evidential strength and attributional resolution are distinct dimensions of evidential assessment, because stronger support at one rung need not carry a more specific physical attribution at the next. Version 3.0 (September 2026): substantially revised manuscript, submitted for peer review. Versions 1.0–2.0 document an earlier, broader formulation of the project and are superseded.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-11
DOI
https://doi.org/10.5281/zenodo.22709887
Primary Topic
Computational Physics and Python Applications
Type
preprint
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preprint

From Fit to Attribution: Support Provenance and Attributional Resolution in Cosmology

Thomas Andrew Schwarz, Harald Zierhut
Zenodo (CERN European Organization for Nuclear Research)
Computational Physics and Python Applications
preprint

From Fit to Attribution: Support Provenance and Attributional Resolution in Cosmology

Thomas Andrew Schwarz, Harald Zierhut
preprint en

Abstract

Recent work on holistic top-down confirmation has made precise why support for a model package need not transfer to its parts. A distinct downstream question remains: at what claim-resolution does a published inferential pipeline license attribution? We develop a contrast-indexed account of support provenance that identifies the highest claim-resolution carried by a documented analysis, tracking the empirical input, mediator, contrast class, decision rule, and premise carrying an inferential upgrade. Two cosmological cases yield complementary results. In the Bullet Cluster, a current dispute over dark-matter detection decomposes by claim resolution: relative to independently constrained baryonic-source alternatives, the spatial configuration licenses a coarse, functionally specified attribution—an additional gravitationally effective contribution spatially separated from the dominant hot plasma—while stronger property attributions remain open and the ontological attribution is not carried by the pipeline. In Dark Energy Spectroscopic Instrument Data Release 2, joint constraints generate additional, rule- and contrast-relative support for a phenomenological model class without by themselves identifying a physically evolving dark-energy component. The comparison yields a non-uniform result: evidential strength and attributional resolution are distinct dimensions of evidential assessment, because stronger support at one rung need not carry a more specific physical attribution at the next. Version 3.0 (September 2026): substantially revised manuscript, submitted for peer review. Versions 1.0–2.0 document an earlier, broader formulation of the project and are superseded.

Zenodo (CERN European Organization for Nuclear Research)
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Computational Physics and Python Applications
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