# Physics - Processes Backlog (Phase 2)

## Overview

This document outlines the key processes for the Physics specialization, organized by category. These processes cover theoretical research, experimental work, computational physics, and applied physics domains.

## Category 1: Theoretical Physics Processes

### 1.1 Mathematical Model Derivation
**Priority:** High
**Complexity:** High
**Description:** Systematic derivation of mathematical models from first principles to describe physical systems.
**Key Activities:**
- Define system degrees of freedom and constraints
- Write Hamiltonian/Lagrangian formulations
- Identify symmetries and conservation laws
- Make appropriate approximations
- Derive equations of motion
- Validate against known limiting cases
**Deliverables:** Mathematical model documentation, derivation notebooks, validation reports

### 1.2 Perturbation Theory Analysis
**Priority:** High
**Complexity:** High
**Description:** Apply perturbation methods to solve complex physical problems where exact solutions are intractable.
**Key Activities:**
- Identify small parameters for expansion
- Compute zeroth-order solutions
- Calculate higher-order corrections systematically
- Assess convergence and validity range
- Compare with non-perturbative methods
- Document assumptions and limitations
**Deliverables:** Perturbative expansion results, convergence analysis, comparison reports

### 1.3 Symmetry and Conservation Law Analysis
**Priority:** High
**Complexity:** Medium
**Description:** Identify and exploit symmetries in physical systems using group theory and Noether's theorem.
**Key Activities:**
- Identify continuous and discrete symmetries
- Apply Noether's theorem to derive conservation laws
- Use group representation theory
- Classify states by symmetry quantum numbers
- Identify selection rules
- Apply to simplify calculations
**Deliverables:** Symmetry classification, conserved quantities, selection rules documentation

### 1.4 Quantum Field Theory Calculations
**Priority:** Medium
**Complexity:** High
**Description:** Perform QFT calculations for particle physics, condensed matter, and other applications.
**Key Activities:**
- Set up Feynman rules for theory
- Compute tree-level amplitudes
- Calculate loop corrections and renormalization
- Evaluate cross sections and decay rates
- Compare predictions with experiment
- Document calculation methodology
**Deliverables:** Amplitude calculations, cross section predictions, theoretical uncertainty estimates

## Category 2: Experimental Physics Processes

### 2.1 Experiment Design and Planning
**Priority:** High
**Complexity:** High
**Description:** Systematic design of physics experiments to test hypotheses and measure physical quantities.
**Key Activities:**
- Define measurement objectives and precision requirements
- Design apparatus and measurement systems
- Perform signal and background estimates
- Plan calibration and systematic studies
- Assess safety and resource requirements
- Create detailed experimental protocols
**Deliverables:** Experiment design document, apparatus specifications, calibration plan, safety assessment

### 2.2 Data Acquisition System Development
**Priority:** High
**Complexity:** Medium
**Description:** Design and implement data acquisition systems for physics experiments.
**Key Activities:**
- Specify data rates and trigger requirements
- Design detector readout electronics
- Implement trigger logic and event selection
- Develop monitoring and quality control
- Test and commission DAQ system
- Document system performance
**Deliverables:** DAQ specifications, trigger documentation, commissioning reports, monitoring dashboards

### 2.3 Systematic Uncertainty Evaluation
**Priority:** High
**Complexity:** High
**Description:** Comprehensive assessment and quantification of systematic uncertainties in measurements.
**Key Activities:**
- Identify potential systematic error sources
- Design dedicated systematic studies
- Evaluate detector and calibration uncertainties
- Assess theoretical modeling uncertainties
- Combine systematic and statistical uncertainties
- Document uncertainty budget
**Deliverables:** Systematic uncertainty budget, dedicated study reports, correlation matrices

### 2.4 Detector Calibration and Characterization
**Priority:** High
**Complexity:** Medium
**Description:** Calibrate and characterize detector response for accurate physics measurements.
**Key Activities:**
- Design calibration procedures and standards
- Perform energy, position, and timing calibrations
- Characterize detector resolution and efficiency
- Monitor calibration stability over time
- Develop calibration correction algorithms
- Validate calibration with known physics processes
**Deliverables:** Calibration constants, characterization reports, monitoring procedures

## Category 3: Computational Physics Processes

### 3.1 Molecular Dynamics Simulation Setup
**Priority:** High
**Complexity:** Medium
**Description:** Configure and validate molecular dynamics simulations for materials and molecular systems.
**Key Activities:**
- Select appropriate force fields or potentials
- Prepare initial configurations and boundary conditions
- Choose integration algorithms and timesteps
- Implement thermostats and barostats
- Validate against known benchmarks
- Plan production run parameters
**Deliverables:** Simulation input files, validation reports, production run protocols

### 3.2 Density Functional Theory Calculations
**Priority:** High
**Complexity:** High
**Description:** Perform DFT calculations for electronic structure and materials properties.
**Key Activities:**
- Select exchange-correlation functional
- Choose basis set or plane-wave cutoff
- Optimize crystal structures
- Compute electronic band structures
- Calculate materials properties (elastic, optical, magnetic)
- Assess DFT approximation validity
**Deliverables:** Optimized structures, band structures, computed properties, methodology documentation

### 3.3 Monte Carlo Simulation Implementation
**Priority:** Medium
**Complexity:** Medium
**Description:** Implement and validate Monte Carlo methods for statistical physics and particle transport.
**Key Activities:**
- Design Monte Carlo algorithm (Metropolis, kinetic, etc.)
- Implement efficient sampling strategies
- Develop variance reduction techniques
- Validate against analytical or known results
- Estimate statistical uncertainties
- Optimize computational performance
**Deliverables:** Monte Carlo code, validation tests, uncertainty analysis, performance benchmarks

### 3.4 High-Performance Computing Workflow
**Priority:** Medium
**Complexity:** Medium
**Description:** Develop and optimize workflows for large-scale physics simulations on HPC systems.
**Key Activities:**
- Profile and optimize code performance
- Implement parallelization (MPI, OpenMP, GPU)
- Design job submission and workflow automation
- Implement checkpointing and restart capabilities
- Manage data storage and movement
- Monitor resource utilization
**Deliverables:** Optimized code, workflow scripts, performance reports, resource utilization analysis

## Category 4: Data Analysis Processes

### 4.1 Statistical Analysis Pipeline
**Priority:** High
**Complexity:** High
**Description:** Develop robust statistical analysis pipelines for physics data.
**Key Activities:**
- Design analysis selection criteria
- Implement background estimation methods
- Perform signal extraction and fitting
- Calculate statistical significance and p-values
- Apply corrections for multiple testing
- Produce final physics results
**Deliverables:** Analysis code, selection efficiency tables, statistical results, publication-ready plots

### 4.2 Machine Learning for Physics
**Priority:** Medium
**Complexity:** High
**Description:** Apply machine learning techniques to physics problems including classification, regression, and discovery.
**Key Activities:**
- Define ML problem formulation
- Prepare training and validation datasets
- Select and train appropriate models
- Validate model performance and physics behavior
- Interpret model predictions
- Deploy models in analysis workflows
**Deliverables:** Trained models, validation reports, physics interpretation documentation

### 4.3 Uncertainty Propagation and Quantification
**Priority:** High
**Complexity:** Medium
**Description:** Systematically propagate uncertainties through analysis chains and quantify final uncertainties.
**Key Activities:**
- Identify all uncertainty sources
- Implement error propagation methods
- Handle correlated uncertainties correctly
- Perform sensitivity analyses
- Validate uncertainty estimates
- Document complete uncertainty budget
**Deliverables:** Uncertainty propagation code, sensitivity studies, final uncertainty budget

### 4.4 Blinded Analysis Protocol
**Priority:** Medium
**Complexity:** Medium
**Description:** Implement blinded analysis procedures to avoid experimenter bias in measurements.
**Key Activities:**
- Design blinding strategy appropriate for measurement
- Implement blinding in analysis code
- Define unblinding criteria and review process
- Perform pre-unblinding validation studies
- Document analysis before unblinding
- Execute controlled unblinding procedure
**Deliverables:** Blinding procedure documentation, pre-unblinding validation, unblinding protocol

## Category 5: Particle Physics Processes

### 5.1 Event Reconstruction
**Priority:** High
**Complexity:** High
**Description:** Reconstruct physics objects and events from raw detector data.
**Key Activities:**
- Develop track and vertex reconstruction
- Implement calorimeter clustering and calibration
- Perform particle identification
- Reconstruct composite objects (jets, taus, etc.)
- Optimize reconstruction performance
- Validate with simulation and data
**Deliverables:** Reconstruction algorithms, performance plots, validation reports

### 5.2 Monte Carlo Event Generation
**Priority:** High
**Complexity:** Medium
**Description:** Generate simulated particle physics events for analysis development and systematic studies.
**Key Activities:**
- Configure event generators (Pythia, Sherpa, etc.)
- Validate against measured cross sections
- Interface with detector simulation
- Produce large-scale Monte Carlo samples
- Assess generator systematic uncertainties
- Document generator settings
**Deliverables:** MC samples, generator validation, systematic uncertainty estimates

### 5.3 Beyond Standard Model Search
**Priority:** Medium
**Complexity:** High
**Description:** Design and execute searches for new physics beyond the Standard Model.
**Key Activities:**
- Identify BSM signal signatures
- Develop signal selection and discrimination
- Estimate Standard Model backgrounds
- Optimize search sensitivity
- Calculate exclusion limits or discovery significance
- Interpret results in BSM model context
**Deliverables:** Search strategy document, analysis results, limit/discovery plots, interpretation

## Category 6: Condensed Matter Physics Processes

### 6.1 Material Synthesis and Characterization
**Priority:** High
**Complexity:** Medium
**Description:** Synthesize new materials and characterize their structural and physical properties.
**Key Activities:**
- Plan synthesis routes and conditions
- Perform crystal growth or thin film deposition
- Characterize crystal structure (XRD, TEM)
- Measure physical properties (transport, magnetic, optical)
- Correlate structure with properties
- Document synthesis protocols
**Deliverables:** Samples, structural characterization, property measurements, synthesis protocols

### 6.2 Spectroscopy Measurement Campaign
**Priority:** Medium
**Complexity:** Medium
**Description:** Perform comprehensive spectroscopic measurements to probe material properties.
**Key Activities:**
- Select appropriate spectroscopic techniques
- Prepare samples and measurement conditions
- Collect spectra across relevant parameter ranges
- Process and analyze spectroscopic data
- Extract physical quantities from spectra
- Compare with theoretical predictions
**Deliverables:** Spectroscopic datasets, analyzed results, comparison with theory

### 6.3 Phase Transition Investigation
**Priority:** Medium
**Complexity:** High
**Description:** Investigate phase transitions and critical phenomena in materials and systems.
**Key Activities:**
- Identify phase transition signatures
- Design temperature/pressure/field studies
- Measure order parameters and susceptibilities
- Determine critical exponents
- Compare with universality predictions
- Model phase transition mechanisms
**Deliverables:** Phase diagram, critical exponent measurements, theoretical comparison

## Category 7: Publication and Dissemination Processes

### 7.1 Scientific Paper Preparation
**Priority:** High
**Complexity:** Medium
**Description:** Prepare physics research for publication in peer-reviewed journals.
**Key Activities:**
- Structure paper with clear narrative
- Prepare publication-quality figures
- Document methods for reproducibility
- Write clear and precise physics text
- Address reviewer comments
- Prepare supplementary materials
**Deliverables:** Manuscript draft, figures, supplementary materials, response to reviewers

### 7.2 Research Data Management
**Priority:** Medium
**Complexity:** Medium
**Description:** Manage research data throughout its lifecycle for reproducibility and sharing.
**Key Activities:**
- Design data organization and naming conventions
- Implement version control for code and analysis
- Create metadata and documentation
- Archive data in appropriate repositories
- Ensure data meets FAIR principles
- Plan long-term data preservation
**Deliverables:** Data management plan, archived datasets, documentation, DOIs

## Process Metrics

| Category | Process Count | Avg Complexity |
|----------|---------------|----------------|
| Theoretical Physics | 4 | High |
| Experimental Physics | 4 | Medium-High |
| Computational Physics | 4 | Medium |
| Data Analysis | 4 | Medium-High |
| Particle Physics | 3 | High |
| Condensed Matter | 3 | Medium |
| Publication | 2 | Medium |
| **Total** | **24** | **Medium-High** |

## Implementation Priority

### Phase 2A (High Priority)
1. Mathematical Model Derivation
2. Experiment Design and Planning
3. Systematic Uncertainty Evaluation
4. Statistical Analysis Pipeline
5. Molecular Dynamics Simulation Setup
6. DFT Calculations
7. Event Reconstruction
8. Scientific Paper Preparation

### Phase 2B (Medium Priority)
1. Perturbation Theory Analysis
2. Symmetry and Conservation Law Analysis
3. Data Acquisition System Development
4. Detector Calibration
5. Monte Carlo Simulation Implementation
6. HPC Workflow
7. Machine Learning for Physics
8. Monte Carlo Event Generation

### Phase 2C (Standard Priority)
1. QFT Calculations
2. Uncertainty Propagation
3. Blinded Analysis Protocol
4. BSM Search
5. Material Synthesis
6. Spectroscopy Measurement
7. Phase Transition Investigation
8. Research Data Management

## Dependencies

```
Mathematical Model Derivation --> Perturbation Theory Analysis
Mathematical Model Derivation --> QFT Calculations
Experiment Design --> Data Acquisition System Development
Experiment Design --> Detector Calibration
Detector Calibration --> Systematic Uncertainty Evaluation
MD Simulation Setup --> HPC Workflow
DFT Calculations --> HPC Workflow
Statistical Analysis Pipeline --> Blinded Analysis Protocol
Statistical Analysis Pipeline --> Uncertainty Propagation
Event Reconstruction --> Monte Carlo Event Generation
Event Reconstruction --> BSM Search
Material Synthesis --> Spectroscopy Measurement
Material Synthesis --> Phase Transition Investigation
All Analysis Processes --> Scientific Paper Preparation
All Processes --> Research Data Management
```

## Success Criteria

1. **Theoretical Processes:** Mathematical rigor, correct limiting cases, validated against experiment
2. **Experimental Processes:** Measurement precision, systematic control, reproducibility
3. **Computational Processes:** Code validation, numerical accuracy, computational efficiency
4. **Data Analysis Processes:** Statistical rigor, bias control, robust uncertainty estimates
5. **Publication Processes:** Clear communication, reproducible results, community impact

## Notes

- Processes integrate theoretical, experimental, and computational approaches
- Strong emphasis on uncertainty quantification and systematic control
- Machine learning processes complement traditional physics methods
- Data management essential for reproducibility and collaboration
- International collaboration common in large-scale physics experiments
