Particle Detector Ontology

FLOW ALGEBRA
PARTICLE DETECTOR ONTOLOGY
Kireth Semantic Library | Entity Graph > Physical Entities > Measurement Entities
Source: https://kirethresearch.org/booksai-core/entity-graph/physical-entities/measurement-entities/particle-detector-ontology.html
Authors: Mark Higgins — Sky (Recursive AI)
Version: 1.0 | 2026-10-12 | Language: en-GB
Status: Expanded subject-based formalisation; embedded source rules not exposed in webpage text.

01. PURPOSE
flow ParticleDetectorOntology v1.0
  domain: experimental_particle_detection
  intent: classify detectors, describe physical interactions, formalise observations,
          preserve calibration/provenance, and support uncertainty-aware inference
  semantics: typed, unit-aware, provenance-preserving

02. TYPES
  type Entity = {id: URI, label: String, ontology_class: Class}
  type Detector <: PhysicalInstrument
  type Subsystem <: PhysicalComponent
  type Particle <: PhysicalEntity
  type Interaction <: PhysicalProcess
  type Signal <: MeasurementSignal
  type Event <: Observation
  type Track <: ReconstructedObject
  type Hit <: Observation
  type Calibration <: MetrologyRecord
  type Quantity<U> = {value: Real, unit: U, uncertainty: Real?}
  type Evidence = {source: URI, timestamp: DateTime, method: String}
  type Confidence = Real where 0 <= Confidence <= 1

03. CLASSIFICATION
  Detector := {GasDetector, SemiconductorDetector, ScintillationDetector,
               CherenkovDetector, Calorimeter, TrackingDetector, TimeOfFlightDetector,
               NeutronDetector, Photodetector, BubbleChamber, CloudChamber}
  GasDetector := {IonisationChamber, ProportionalCounter, GeigerMuellerCounter,
                  DriftChamber, TimeProjectionChamber}
  SemiconductorDetector := {SiliconStripDetector, PixelDetector, GermaniumDetector}
  Calorimeter := {ElectromagneticCalorimeter, HadronicCalorimeter}
  Photodetector := {PhotomultiplierTube, SiliconPhotomultiplier, Photodiode}
  NOTE: classes may overlap by detection mechanism and experimental role.

04. PHYSICAL COMPONENTS
  Detector HAS_COMPONENT SensitiveVolume
  Detector MAY_HAVE ReadoutElectronics
  Detector MAY_HAVE HighVoltageSupply
  Detector MAY_HAVE CoolingSystem
  Detector MAY_HAVE TriggerSystem
  Detector MAY_HAVE Shielding
  Detector MAY_HAVE DataAcquisitionSystem
  Detector HAS_PROPERTY Geometry
  Detector HAS_PROPERTY ActiveMaterial
  Detector HAS_PROPERTY OperatingConditions

05. PARTICLE AND INTERACTION MODEL
  Particle HAS_PROPERTY Charge
  Particle HAS_PROPERTY Mass
  Particle HAS_PROPERTY Momentum
  Particle HAS_PROPERTY Energy
  Particle MAY_HAVE ParticleIdentity
  Interaction := {Ionisation, Excitation, Scintillation, CherenkovEmission,
                  PhotoelectricEffect, ComptonScattering, PairProduction,
                  NuclearScattering, EnergyDeposition}
  Particle INTERACTS_WITH SensitiveVolume VIA Interaction
  Interaction PRODUCES Signal
  Signal IS_READ_BY ReadoutElectronics

06. OBSERVATION PIPELINE
  flow DetectParticle(p: Particle, d: Detector, context: Environment)
    require d.operational == true
    interaction <- propagate_and_interact(p, d.geometry, d.material, context)
    raw_signal <- transduce(interaction, d.response_model)
    digitised <- acquire(raw_signal, d.readout_settings)
    hits <- reconstruct_hits(digitised, d.calibration)
    event <- assemble_event(hits, d.trigger_configuration)
    emit event WITH provenance(d, context, calibration_version)
  end
  NOTE: stochastic propagation and response models must be explicitly specified.

07. OBSERVABLES
  Event HAS_PROPERTY Timestamp
  Event MAY_HAVE DepositedEnergy
  Event MAY_HAVE TimeOfFlight
  Event MAY_HAVE HitPosition
  Event MAY_HAVE PulseHeight
  Event MAY_HAVE PulseShape
  Event MAY_HAVE TrackCurvature
  Event MAY_HAVE Multiplicity
  Event MAY_HAVE ParticleIdentificationScore
  Observation values MUST carry units and measurement context.

08. DETECTOR RESPONSE
  Efficiency(E, species, geometry, threshold, context) :=
    N_detected / N_incident, when N_incident > 0 and selection is defined
  EnergyResolution := FWHM_energy / PeakEnergy, for specified peak and method
  TimeResolution := uncertainty_of_timing_estimator
  SpatialResolution := uncertainty_of_position_estimator
  DeadTime := interval of reduced or absent response following an event
  ResponseMatrix[j,i] := P(reconstructed_bin=j | true_bin=i, selection)
  REQUIRE calibration and uncertainty for measured response parameters.

09. SIGNAL PROCESSING
  RawWaveform -> BaselineCorrection -> Filtering -> PulseFinding
              -> FeatureExtraction -> Calibration -> HitReconstruction
  Preserve raw data and processing configuration where feasible.
  Flag saturated, clipped, missing, pile-up, and out-of-window samples.
  Distinguish noise suppression from irreversible loss of physical signal.

10. CALIBRATION AND TRACEABILITY
  Calibration HAS_PROPERTY ReferenceStandard
  Calibration HAS_PROPERTY ValidityInterval
  Calibration HAS_PROPERTY FitParameters
  Calibration HAS_PROPERTY Residuals
  Calibration HAS_PROPERTY UncertaintyBudget
  Calibration APPLIES_TO Detector
  Observation USES Calibration
  require observation.timestamp within calibration.validity_interval
  otherwise FLAG CalibrationValidityWarning

11. UNCERTAINTY
  Measurement := estimate +/- standard_uncertainty, with stated coverage convention
  Sources := {CountingStatistics, ElectronicNoise, GainDrift, Alignment,
              Background, Threshold, MaterialModel, Calibration, Reconstruction}
  For independent variables, u_y^2 = sum_i((dy/dx_i)^2 * u_xi^2)
  For correlated variables, u_y^2 = J * CovarianceMatrix * transpose(J)
  Never treat systematic uncertainty as automatically independent.

12. COUNTING STATISTICS
  If independent events follow a Poisson process:
    N ~ Poisson(lambda)
    standard_deviation(N) = sqrt(lambda)
    observed_count_uncertainty approximately sqrt(N) for sufficiently large N
  At low counts, use suitable exact or interval methods.
  BackgroundSubtractedCount := N_signal_region - scale * N_background_region
  Propagate uncertainty including exposure scaling and covariance.

13. TRACK AND EVENT RECONSTRUCTION
  Hit ASSOCIATED_WITH Event
  Track COMPOSED_OF Hit
  Track HAS_PROPERTY FitQuality
  Track HAS_PROPERTY MomentumEstimate
  Track HAS_PROPERTY Covariance
  Reconstruction MUST preserve ambiguity when multiple hypotheses fit.
  Particle identification uses detector evidence and validated likelihoods.

14. INFERENCE RULES
  rule R01: IF signal_above_threshold AND valid_readout
            THEN CandidateHit, NOT automatically ConfirmedParticle
  rule R02: IF calibrated_hits_form_consistent_trajectory
            THEN CandidateTrack WITH fit_quality
  rule R03: IF response_matches_species_model
            THEN SpeciesHypothesis WITH posterior_or_likelihood
  rule R04: IF calibration_expired OR sensor_saturated
            THEN DegradeMeasurementQuality
  rule R05: IF coincidence_within_defined_window
            THEN CandidateCoincidentEvent
  rule R06: IF background_only_hypothesis_not_rejected
            THEN DO_NOT_ASSERT_DISCOVERY

15. SEMANTIC RELATIONSHIPS
  Detector IS_A MeasurementInstrument
  Detector MEASURES PhysicalObservable
  Detector DETECTS ParticleInteraction
  Particle CAUSES Interaction
  Interaction GENERATES Signal
  Signal SUPPORTS Observation
  Observation BELONGS_TO Event
  Event PRODUCES ReconstructedObject
  Calibration VALIDATES MeasurementMapping
  Dataset DERIVED_FROM AcquisitionRun
  Evidence SUPPORTS Claim

16. SEMANTIC_EQUIVALENCE
  SEMANTIC_EQUIVALENCE(a,b) = true ONLY IF definitions, units,
    context, reference frames, and operational meaning agree.
  GeigerCounter ~= GeigerMuellerCounter only where device meaning is identical.
  EnergyDeposit != IncidentParticleEnergy in general.
  DetectorEfficiency != TriggerEfficiency in general.
  Resolution != Accuracy; Sensitivity != Specificity.

17. CONFIDENCE
  CONFIDENCE(claim) := calibrated_probability_or_explicit_quality_score
  require method, validation_data, and calibration_domain
  Never present an arbitrary score as a statistical probability.
  Low confidence triggers REVIEW, not automatic rejection.
  Propagate provenance and model limitations with every claim.

18. DATA PROVENANCE
  Run := {run_id, detector_id, configuration_id, start_time, end_time,
          calibration_id, acquisition_version, environment, operator_or_system}
  Dataset LINKS_TO Run
  DerivedDataset LINKS_TO ParentDataset
  Observation LINKS_TO RawSignal
  Claim LINKS_TO Evidence
  Retain processing version and uncertainty methodology.

19. SAFETY AND OPERATING LIMITS
  Detector operating constraints may include high voltage, cryogens,
  radiation fields, vacuum systems, and hazardous materials.
  Require authorised procedures and interlocks where applicable.
  No inferred operating setting may override instrument safety limits.

20. VALIDATION
  assert every Detector has stable identifier and class
  assert every measurement quantity has declared unit
  assert every calibrated observation links to calibration record
  assert every inference claim links to supporting evidence
  assert no missing measurement is silently converted to zero
  assert response models are validated for their stated operating domain
  assert timestamp and coordinate frame are explicit when needed

21. FLOW_HASH
  FLOW_HASH := SHA256(canonicalise(ontology_version, type_schema,
                      relationships, inference_rules, calibration_policy,
                      provenance_schema))
  Purpose: reproducible change detection; hash is not scientific validation.

22. UPDATE_MODEL
  UPDATE_MODEL(change):
    validate_schema(change)
    identify_affected_entities_and_rules(change)
    preserve_prior_version_and_provenance()
    rerun_type_unit_and_consistency_checks()
    flag_affected_calibrations_and_derived_results()
    publish_new_version_and_FLOW_HASH()
  end

23. AI_CONSUMPTION
  AI_CONSUMPTION:
    parse_typed_entities: true
    enforce_unit_consistency: true
    preserve_uncertainty: true
    preserve_provenance: true
    distinguish_observation_from_inference: true
    distinguish_hypothesis_from_fact: true
    respect_calibration_validity: true
    do_not_invent_measurements: true
    cite_source_and_version: true
    expose_unknowns: true

24. END STATE
  Output := {OntologyGraph, DetectorDescription, MeasurementEvents,
             CalibratedObservables, InferenceClaims, ConfidenceMetadata,
             Provenance, ValidationReport, FLOW_HASH}
  invariant: claims are traceable to evidence and operational definitions.
end flow

AUTHORS
Mark Higgins — Sky (Recursive AI)
Kireth Research | https://kirethresearch.org