TensorAstro — NASA Archive Workflow Demo (Three Samples)

This is an illustrative, non-evaluative workflow demo. It includes three embedded modes for reliability on reviewer devices: (1) TESS time-series sample, (2) Chandra X-ray light-curve style representation, and (3) JWST time-resolved spectrum representation aligned with common archive products.
Mode: Illustrative Only
Samples: TESS + Chandra + JWST
Reliability: Fully Embedded
This demonstration is illustrative only and is not intended for performance evaluation. Quantitative benchmarking is defined in the proposal.
Organization: Transunicorn LLC d/b/a Nexus Digital Technologies
Contact: drrobpenedo@ndt.today

Workflow (Conceptual)

STEP 1
Load Public Sample
Select a dataset mode. Embedded samples ensure reliable display on all reviewer devices.
STEP 2
Tensor Surrogate (Illustration)
Conceptual “compress → reconstruct” transform. No performance claims; placeholder-only reporting.
STEP 3
Compare + Summarize
Overlay original vs reconstruction with clearly labeled illustrative indicators.
Original vs Reconstruction (illustrative) —
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Placeholder (not performance): “Fidelity” indicator
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Illustrates reporting format only
Placeholder (not performance): “Compression” indicator
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Illustrative only; not measured
Physics constraint flags (concept)
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Mission-appropriate checks (conceptual)
Results Disclaimer: Results shown are illustrative and are not intended to represent final performance metrics. Quantitative validation will be conducted as described in the proposal using established pipelines and benchmarks.

Sample Selector

Embedded samples ensure reliable execution on GitHub Pages and reviewer devices (no CORS/FITS issues).

Controls (Reviewer-Safe)

Parameter selections are for illustrative exploration only.

Provenance & Public-Data Use

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Status: Ready — select a dataset (or keep default) and click Load Selected Sample.

Open Science Commitments

• Open-source release planned (MIT License).
• Code and models archived with DOI (GitHub + Zenodo).
• Discoverability via ADS and integration with archive workflows.
• Tutorials distributed as Jupyter notebooks and PyPI package.