NEUTRINO RISC / MARGIN-CONFINEMENT PIPELINE
===========================================

Folder:
neutrinos_roots/

Purpose
-------
This pipeline converts sparse-observation neutrino reconstruction data
into UNNS-compatible realizability ladders for:

- STRUC-PERC-I analysis
- RISC testing
- admissibility transport analysis
- margin-confinement validation
- sparse-event coherence analysis

The objective is NOT particle-physics reconstruction itself.

The objective is:
to test whether observational representations preserve or distort
structural admissibility under sparse-event conditions.

------------------------------------------------------------
PIPELINE OVERVIEW
------------------------------------------------------------

ROOT DATA
    ↓

ROOT STRUCTURE INSPECTION
    ↓

BRANCH EXTRACTION
    ↓

SCALAR LADDER GENERATION
    ↓

NORMALIZATION + SORTING
    ↓

RAW LADDER EXPORT
    ↓

STRUC-PERC-I ANALYSIS
    ↓

Δ-LIFTING
    ↓

SECONDARY STRUC-PERC-I ANALYSIS
    ↓

RISC / FCC / MARGIN EVALUATION

------------------------------------------------------------
FILES
------------------------------------------------------------

ROOT DATASETS
-------------

deepL_performance.root
    Deep-learning embedding and classifier-space outputs.

EnerySpectrum.root
    Raw reconstructed energy distributions.

evt.root
    Sparse-event topology and event descriptors.

Fib_CDPMT.root
    Detector geometry / PMT-related structure.

TMVA_performance.root
    TMVA classifier and discrimination outputs.

variable.root
    Reconstructed observable variables:
    hit times, angular distributions, etc.

------------------------------------------------------------
SCRIPTS
------------------------------------------------------------

inspect_root_structure.py
-------------------------

Purpose:
    Lists ROOT object structure.

Outputs:
    ROOT trees and internal objects.

Usage:
    python inspect_root_structure.py

------------------------------------------------------------

inspect_branch_structure.py
---------------------------

Purpose:
    Lists all branches inside ROOT trees.

Outputs:
    Branch names suitable for extraction.

Usage:
    python inspect_branch_structure.py

------------------------------------------------------------

extract_neutrino_ladders.py
---------------------------

Purpose:
    Converts ROOT observables into ordered scalar ladders.

Main operations:
    - extract arrays
    - remove invalid values
    - normalize
    - sort
    - export

Output folder:
    raw/

Usage:
    python extract_neutrino_ladders.py

------------------------------------------------------------
RAW LADDER OUTPUTS
------------------------------------------------------------

Folder:
raw/

Contains:
    normalized scalar realizability ladders.

Examples:

    L_sig2pp.txt
    L_bkg2singleC14.txt
    L_TMVA_hSigeff.txt
    L_deepL_hSigSB.txt
    L_sig_hittime_v1.txt
    ...

Each file is:
    - normalized
    - ordered
    - STRUC-PERC-I compatible

------------------------------------------------------------
UNNS INTERPRETATION
------------------------------------------------------------

The ladders do NOT represent:
    "neutrino physics directly"

They represent:
    observational admissibility geometry.

Different representations correspond to:
    different admissibility charts.

Examples:
    raw energy space
    TMVA classifier space
    deep-learning embedding space
    event topology space

------------------------------------------------------------
PRIMARY RESEARCH QUESTIONS
------------------------------------------------------------

1. Does raw observational space appear fragmented?

2. Do lifted representations restore coherence?

3. Do deep-learning embeddings preserve GR better
   than raw energy space?

4. Do sparse-event structures form FCC-like
   admissible boundary layers?

5. Does Δ-lifting improve margin and admissibility?

6. Do different observational charts produce
   different structural verdicts?

------------------------------------------------------------
STRUC-PERC-I TARGETS
------------------------------------------------------------

For every ladder compute:

    GR
    TD
    κconn
    admissibility class
    rigidity
    margin proxy

Target classes:
    Full
    Giant
    Tail
    Hard
    FCC-like

------------------------------------------------------------
RISC DETECTION CONDITIONS
------------------------------------------------------------

Strong RISC evidence occurs if:

    Raw space:
        Tail / Hard

    Lifted or embedded space:
        Full / Giant / FCC

Interpretation:
    apparent fragmentation is representation-induced.

------------------------------------------------------------
THEORETICAL CONTEXT
------------------------------------------------------------

This corpus is used as a sparse-observation testbed for:

    - Margin-Confinement Law
    - Representation-Induced Structural Collapse (RISC)
    - admissibility transport
    - observational chart dependence
    - FCC boundary regimes

The analysis investigates whether detector-space
transformations behave as admissibility lifting operators.

------------------------------------------------------------
IMPORTANT NOTE
------------------------------------------------------------

The goal is NOT to claim:
    "UNNS explains neutrinos."

The goal is:

to test whether observational reconstruction itself
possesses admissibility geometry and representation-sensitive
structural coherence.

------------------------------------------------------------
NEXT STAGES
------------------------------------------------------------

1. Run STRUC-PERC-I on all raw ladders.

2. Generate Δ-ladders.

3. Re-run STRUC-PERC-I on lifted representations.

4. Compare:
       raw
       TMVA
       deep-learning
       topology
       Δ-space

5. Identify:
       RISC
       FCC
       admissibility recovery
       coherent sparse-event transport

------------------------------------------------------------
EXPECTED HIGH-VALUE OUTCOME
------------------------------------------------------------

If classifier or embedding spaces preserve coherence
while raw spaces fragment, then:

    detector intelligence acts as admissibility lifting.

This would become direct operational support for:
    representation-sensitive admissibility geometry.

    DATA source: https://www.scidb.cn/en/detail?dataSetId=bfe84bf2f2a94a539d0bd9dfab727cdd#p2