| 1. Domain | 1.1 Scope of the Domain | Boundaries | The range of phenomena the science includes and excludes. | Includes the study of galaxies beyond the Milky Way, their formation, evolution, interactions, active galactic nuclei, large scale structure, galaxy clusters, and intergalactic medium behavior. Excludes stellar scale processes inside single galaxies and excludes primordial cosmology beyond the galactic formation context. |
| | Scale | The spatial, temporal, or organizational level at which the science operates (e.g., quantum, cellular, social, cosmic). | Operates from kiloparsec scales of individual galaxies to megaparsec and gigaparsec scales of clusters, filaments, and large scale structure. Time scales range from millions to billions of years. |
| 1.2 Ontological Commitments | Entities | The kinds of things assumed to exist within the domain (particles, organisms, agents, fields, etc.). | External galaxies, galaxy clusters, dark matter halos, intergalactic gas, active galactic nuclei, jets, starburst galaxies, merging galaxies, cosmic filaments, and large scale gravitational structures. |
| | Properties | The fundamental attributes these entities possess (mass, charge, genotype, preference, etc.). | Luminosity, mass, star formation rate, metallicity, redshift, morphology, velocity dispersion, gas content, cluster mass, and activity level of central black holes. |
| | Categories | The basic ontological types used to classify domain elements (substances, processes, relations, structures). | Galaxy types, cluster types, large scale structures, intergalactic medium phases, dynamical processes, and interaction or merger classes. |
| 1.3 State-Variables | Variables | The measurable or definable properties that describe system conditions. | Redshift, luminosity, gas density, temperature, star formation rate, metallicity, velocity fields, halo mass, and clustering strength. |
| | Parameterization | How variables encode and represent the system’s state. | States encoded by luminosity distance, spectral energy distributions, cluster scaling relations, halo occupation models, redshift distributions, and galaxy population parameters. |
| 1.4 Admissible Idealizations | Simplifications | Conceptual reductions used to make the domain tractable (point masses, rational agents, perfect gases). | Treating galaxies as point masses, assuming simple halo profiles, approximating intergalactic medium as uniform, ignoring substructure, treating mergers as symmetric, and using simplified star formation laws. |
| | Validity Conditions | The limits and contexts in which idealizations hold or break down. | Valid when spatial resolution is limited, when substructure does not dominate dynamics, when large scale averages are appropriate, and when extreme activity phases or strong asymmetries are absent. |
| 1.5 Domain Assumptions | Structural Assumptions | Background ontological stances such as determinism, continuity, randomness, discreteness. | Assumes gravitational evolution shapes large scale structure, galaxy populations follow statistical trends, dark matter halos define galaxy environments, and intergalactic gas follows thermodynamic and hydrodynamic rules. |
| | Implicit Commitments | Unstated but necessary assumptions that shape the field’s conceptual structure. | Assumes redshift reliably traces cosmic distance, luminosity functions represent galaxy populations, cluster scaling laws reflect gravitational physics, and halo models connect galaxies to dark matter. |
| 1.6 Internal Coherence Requirements | Consistency | The demand that domain concepts do not contradict one another. | Requires consistency between galaxy evolution models, cluster dynamics, dark matter halo theory, large scale structure statistics, and observational redshift surveys. |
| | Compatibility | The requirement that entities, variables, and assumptions fit together into a unified descriptive framework. | Entities, variables, and assumptions must integrate into a unified framework linking galaxy formation, cluster behavior, and large scale structure evolution. |
| 2. Evidence Layer | 2.1 Observable Phenomena | Observables | The aspects of the domain that can produce detectable signals accessible to measurement. | Detectable signals include galaxy spectra, redshifts, multi band luminosities, star formation indicators, cluster X ray emission, radio jets, gravitational lensing patterns, large scale clustering, and intergalactic absorption features. |
| | Detection Limits | The boundaries of what can be resolved or sensed by current instruments or methods. | Limited by telescope sensitivity, redshift reach, angular resolution, dust extinction, instrument noise, confusion at large distances, and the faintness of distant galaxies or diffuse intergalactic gas. |
| 2.2 Measurement Systems | Units | Standardized quantifications (meters, seconds, volts, decibels, dollars, etc.) necessary for consistent comparison. | Uses parsecs, megaparsecs, gigaparsecs, years, seconds, magnitudes, redshift values, flux units, kelvins, kilometers per second, and luminosity units. |
| | Instruments | Devices and tools (microscopes, spectrometers, sensors, surveys, detectors) used to produce measurements. | Instruments include optical telescopes, radio interferometers, infrared space telescopes, X ray observatories, spectrographs, large scale survey arrays, gravitational lensing detectors, and cosmic microwave background survey instruments. |
| 2.3 Operational Definitions | Definitions | Terms defined by specific measurement procedures, ensuring empirical clarity. | Quantities such as redshift, luminosity distance, star formation rate, cluster mass, halo mass, and metallicity are defined through specific spectroscopic, photometric, or survey based procedures. |
| | Procedures | The explicit steps required to perform a measurement in a reproducible way. | Procedures include redshift extraction from spectral lines, multi wavelength photometry, weak lensing shape measurements, cluster X ray mapping, radio flux mapping, and population fitting using template spectra. |
| 2.4 Data Acquisition | Protocols | Formal processes for gathering data under controlled or standardized conditions. | Data gathered through long exposure imaging, multi band survey cycles, repeated observations for variability, all sky mapping, spectroscopic follow up, and organized survey strategies with standardized calibration. |
| | Sampling | Rules determining which subset of the domain is measured and how representative it is. | Sampling rules include redshift binning, magnitude limited selection, color selection, cluster membership selection, and spatial sampling across large survey areas to ensure statistical completeness. |
| 2.5 Data Character & Format | Data Types | The form raw evidence takes (time series, spectra, images, counts, qualitative records). | Data appears as spectra, redshift catalogs, luminosity distributions, cluster temperature maps, radio or X ray images, weak lensing shear maps, large scale structure catalogs, and absorption line surveys. |
| | Resolution | The granularity or precision with which data is captured. | Determined by telescope aperture, detector sensitivity, spectral dispersion, integration time, array baseline for interferometry, and wavelength dependent atmospheric or instrumental effects. |
| 2.6 Reliability & Calibration | Calibration | Adjustment procedures ensuring instruments produce accurate results. | Calibration uses standard stars, spectrophotometric standards, wavelength reference lamps, atmospheric models, radio flux standards, X ray detector calibrations, and cross survey consistency checks. |
| | Error Characterization | Identification and quantification of noise, uncertainty, bias, and measurement error. | Errors arise from instrument noise, photometric uncertainties, redshift misidentification, dust extinction corrections, weak lensing shape noise, selection biases, and large scale statistical variance. |
| 3. Structural Layer | 3.1 Patterns & Regularities | Laws / Relations | Stable, repeatable patterns governing how observables behave across conditions. | Stable patterns include galaxy scaling relations, mass luminosity relations, star formation main sequence trends, metallicity dependencies, cluster scaling laws, and large scale clustering patterns following gravitational statistics. |
| | Invariants | Quantities or properties that remain constant under transformations (symmetries, conservation laws). | Invariants include conserved bulk mass in closed systems, stable large scale structure features, persistent morphology classes, symmetry properties of gravitational interactions, and statistical regularities such as luminosity functions. |
| 3.2 Causal Architecture | Mechanisms | Underlying processes or structures that produce the observed regularities. | Mechanisms arise from gravitational collapse, mergers, gas accretion, feedback from stars and active nuclei, environmental stripping, turbulence in the intergalactic gas, and dark matter driven structure growth. |
| | Pathways | Organized sequences of interactions forming a causal chain or network. | Pathways include galaxy formation from primordial fluctuations, hierarchical merging, gas inflow and outflow cycles, starburst episodes, quenching of star formation, and evolution into clusters or filaments. |
| 3.3 Theoretical Vocabulary | Concepts | Core terms that encode the domain’s structure (force, gene, equilibrium, field). | Core concepts include redshift, galaxy formation, large scale structure, merger tree, halo occupation, feedback, quenching, intergalactic medium, and clustering. |
| | Classifications | Taxonomies, categories, or typologies that organize entities and relations. | Classifies galaxies by morphology, star formation activity, mass, redshift, environment, and nuclear activity; also classifies clusters and filaments by mass and density. |
| 3.4 Formal Representations | Equations | Mathematical constructs expressing laws, relations, or mechanisms. | Uses gravitational collapse equations, halo growth rules, star formation laws, energy feedback formulas, mass accretion equations, and statistical descriptions of clustering and structure formation. |
| | Models | Structured representations—mathematical, computational, or conceptual—used to predict and explain phenomena. | Includes semi analytic galaxy formation models, cosmological simulations, halo occupation models, cluster scaling models, merger models, and models of feedback from stars or active nuclei. |
| 3.5 Idealized Structures | Simplified Models | Purposeful abstractions that capture essential dynamics while omitting irrelevant detail. | Idealizations include symmetric halos, smooth gas distributions, simplified feedback rules, ignoring small scale turbulence, steady inflow or outflow approximations, and uniform environment assumptions. |
| | Limit Conditions | Regimes where specific models or approximations hold (classical vs. quantum, linear vs. nonlinear). | Valid when resolution is coarse, environment is averaged, turbulence is small, and strong interactions or extreme feedback events are absent; breaks down during major mergers or rapidly evolving core activity. |
| 3.6 Integrative Frameworks | Unifying Theories | Higher-order structures that connect disparate laws or mechanisms under a coherent whole. | Includes frameworks linking dark matter halo evolution, galaxy growth, feedback cycles, and large scale structure under a unified gravitational and hydrodynamic picture. |
| | Interdisciplinary Links | Points where the theory connects to adjacent sciences or larger explanatory systems. | Links to cosmology, plasma physics, stellar evolution, nuclear astrophysics, gravitational physics, and computational large scale simulation. |
| 4. Method Layer | 4.1 Inquiry Design | Experimental Design | Structured plans for manipulating variables to test causal claims. | Direct manipulation is impossible; instead, tests rely on selecting galaxy samples with controlled properties such as redshift, mass, environment, or activity level to infer causal relationships in growth, mergers, and feedback. |
| | Observational Design | Systematic approaches for gathering non-manipulated data (surveys, field studies, natural experiments). | Observational approaches include long baseline surveys, deep field imaging, multi wavelength scans, natural experiments such as cluster mergers, and cross correlation of galaxy properties with environment. |
| 4.2 Testing & Validation | Hypothesis Testing | Procedures for evaluating whether evidence supports or contradicts specific claims. | Hypotheses tested by comparing redshift distributions, cluster scaling relations, star formation histories, merger rates, and mass functions with predictions from theoretical and simulation based models. |
| | Replication | The requirement that results be independently reproducible under similar conditions. | Replication requires verifying findings using different surveys, instruments, wavelengths, analysis pipelines, and repeated measurements across independent galaxy samples. |
| 4.3 Inference & Evaluation | Statistical Inference | Rules for drawing conclusions from noisy or incomplete data. | Methods include fitting luminosity functions, deriving clustering statistics, estimating halo masses, reconstructing star formation histories, measuring scaling relations, and quantifying uncertainties across large datasets. |
| | Model Comparison | Criteria (fit, simplicity, predictive accuracy, robustness) used to evaluate competing models. | Models are compared based on accuracy in predicting galaxy population trends, cluster properties, large scale clustering, merger rates, and evolution of star formation across redshift. |
| 4.4 Error Management | Error Analysis | Identification and quantification of random and systematic errors. | Errors arise from photometric uncertainties, redshift errors, dust attenuation, instrumental drift, selection biases, incomplete sky coverage, and sample variance in large scale structure. |
| | Bias Control | Methods for minimizing subjective, instrumental, or procedural biases. | Bias minimized with blind catalog processing, completeness corrections, standardized calibration, multiple survey cross checks, and corrections for selection effects and redshift systematics. |
| 4.5 Adjudication & Revision | Peer Scrutiny | Collective evaluation of claims through critique, review, and debate. | Findings evaluated through cross survey validation, peer reviewed publication, conference critique, and comparison with cosmological simulations and analytic models. |
| | Theory Revision | Procedures for modifying, replacing, or discarding models based on new evidence. | Theories revised when new survey results contradict predicted galaxy abundances, cluster masses, gas distributions, or scaling relations, requiring updated feedback, merger, or halo growth models. |
| 4.6 Integrity Conditions | Transparency | Requirements to disclose methods, data, assumptions, and limitations. | Requires full disclosure of survey parameters, photometric calibration, completeness limits, selection criteria, noise estimates, reduction pipelines, and all assumptions built into analysis. |
| | Ethical Standards | Norms ensuring responsible conduct in experimentation, data handling, and publication. | Requires accurate reporting of uncertainties, avoidance of selective survey cuts, responsible use of telescope time, correct catalog handling, and adherence to community data release standards. |