6 “Deconstruction–Injection” Amplitude and Shape of Cosmic Microwave Background μ / y Micro-Distortions

Home ／ Appendix-Prediction and Falsification

This chapter follows the publication template for the falsification program. It uses plain language, avoids equations, and preserves the fixed four-block logic while expanding operational details for publication. For general readers: a μ-type distortion is a gentle, early-time smoothing shift from a perfect blackbody; a y-type distortion is a subtle lift or dip produced by later, hot-gas reprocessing.

I. One-Sentence Goal

Propose a falsification workflow that deconstructs the observed micro-distortions of the Cosmic Microwave Background (CMB) into a smooth baseline term that should vary monotonically with large-scale environment plus standard μ-type and y-type components, then injects environment-forward, text-only forecasts of amplitude and shape ranking across sky patches. Under blinding, we compare forecasts with absolute-spectrum measurements. If the “smooth term + standard templates” repeatedly match observations across platforms and seasons, with a void → filament/node monotonic trend, the result supports the path contribution proposed by Energy Filament Theory (EFT). Chance-level performance or platform-specific shapes argues against it.

II. What to Measure

• All-sky average, deconstructed amplitudes: After standard foreground and zero-level handling, report three plain-language quantities—smooth baseline strength, μ-type share, and y-type share—with intervals rather than formulas, plus a brief note on broadband shape (for example, relatively higher at low/mid/high frequencies).
• Environmental gradients across sky patches: In predefined high-latitude, low-foreground tiles, compare void corridors versus filament/node corridors. Ask whether the smooth baseline is systematically higher in filament/node sightlines, while μ / y template shares remain stable or show predictable small adjustments.
• Cross-platform and multi-epoch robustness: For independent pipelines on ground, balloon, and space platforms, check agreement in amplitudes, shapes, and rank order. In multi-epoch repeats, look for slow, same-direction drifts without shape reversals.

III. How to Do It

1. Data and frequency coverage:
   Use centimeter-to-submillimeter absolute spectrometers and differential spectrometers. Favor high-latitude, low-foreground regions and build a shared set of sky tiles across platforms. For every tile, attach a foreground-confidence grade and horizon/sidelobe risk tag.
2. Deconstruction–injection with blinding:
   • Template library: Prepare three text-defined templates: a smooth baseline (frequency-independent or only gently varying, representing a path-like common contribution), a standard μ-type, and a standard y-type. Add a catch-all foreground-residual template to absorb known, hard-to-remove Galactic leftovers.
   • Forward forecasts: An independent prediction team, using only environment templates (void fraction, filament strength, distance to nodes, cluster thermal pressure proxies, and so on), assigns each tile a ranked expectation for the three templates (strong/medium/weak) with coarse intervals, forming a sealed injection card.
   • Blinded deconstruction: Processing teams—without access to the injection cards—complete absolute calibration, foreground subtraction, and zero-level control on each platform, then fit each tile with the fixed template library, outputting strong/medium/weak with simple intervals per template and a short broadband-shape note (for example, “slightly higher at low frequencies” or “mid-band plateau”).
   • Arbitration: A third party aligns injection cards with deconstruction outputs, computing hit, wrong-sign, and null rates, stratified by target vs. control and by environment grade.
3. Positive/negative controls and artifact removal:
   • Positive controls: In filament/node corridors, the smooth baseline should exceed that in void corridors; μ / y shares should keep a consistent rank across platforms. In cluster-rich tiles, y-type may show a mild uplift without erasing the smooth baseline’s monotonic trend.
   • Negative controls: Randomize environment templates or swap injection cards among tiles; hit rates should fall to chance. If any platform’s “smooth baseline” tracks calibration states or horizon coupling, classify it as an instrumental effect.
   • Separating foregrounds/pipelines: If a tile’s shape co-varies strongly with Galactic templates, or flips when changing foreground solutions, classify it as foreground residual, not a positive for this chapter.

IV. Systematics and Safeguards (Three Items)

• Absolute calibration and zero-level drift: Blackbody emissivity, reflections, and thermal control can fake a smooth term. Safeguard: dual internal references, up/down switching, thermal-state inversion checks, and cross-platform agreement as a necessary condition.
• Beam chromaticity and sidelobes/horizon pickup: Frequency-dependent beams or far sidelobes import ground/atmosphere signals. Safeguard: on-sky beam mapping, strong baffling, zenith drift scans, and a requirement that altitude scaling vanish for any accepted signal.
• Imperfect foreground separation: Residual synchrotron, free–free, or dust can pollute micro-distortions. Safeguard: analyze multiple foreground solutions, down-weight or hold out low-confidence tiles, and stratify statistics by foreground-confidence grade.

V. Execution and Transparency

Pre-register the tile list, environment labels, template definitions, injection rules, and metrics. Keep hold-out tiles within each environment class for final confirmation. Repeat across seasons, solar-activity phases, and across platforms. Publicly release the injection cards, calibration logs, foreground versions, and plain-language deconstruction summaries so outside groups can replicate the tests. This chapter forms a closed loop with the chapters on the radio-background floor, the time history of precise micro-distortions, and cluster-scale residuals; cross-reference among these chapters is required.

VI. Pass/Fail Criteria

1. Support (passes):
   • In two or more environment classes, injection-card rankings of smooth baseline strength and μ / y shares achieve a hit rate significantly above chance, with a void → filament/node monotonic increase.
   • Results replicate across ground, balloon, and space platforms with consistent shape notes and no platform-specific flips or rescalings.
   • Conclusions remain stable when foreground solutions and calibration configurations change, indicating the gain is not driven by foreground co-variance or calibration state.
2. Refutation (fails):
   • Hit rates stay near chance, or one platform/pipeline dominates the apparent success.
   • Shapes flip with calibration state, altitude, or foreground version, or track Galactic templates tightly.
   • Differences between target vs. control and across environment grades are insignificant, weakening any path-linked interpretation.