Data for Wavelength-specific refractive-index calibration of air at 1762 nm for environmental phase compensation in quantum optical interfaces

Data for Wavelength-specific refractive-index calibration of air at 1762 nm for environmental phase compensation in quantum optical interfaces High-precision atmospheric sensing data for quantum technology and remote sensing applications Overview This dataset contains comprehensive measurements of atmospheric refractive index at 1762 nm wavelength using a quantum-enhanced interferometric technique. The data spans from December 30, 2024 to August 21, 2025, providing high-precision environmental and optical measurements for atmospheric sensing and quantum networking applications. Dataset Components 1. Raw Data (raw.zip) Contains original, unprocessed measurement files organized by date and sensor type: raw/ interferometer/ counts_ratio_data_YYYYMMDD.csv environmental/ humidity_data_YYYYMMDD.csv pressure_data_YYYYMMDD.csv temperature_data_YYYYMMDD.csv Data Specifications: Interferometer Data: Laser frequency counts ratio measurements at 1-second intervals Environmental Data: Simultaneous temperature, humidity, and pressure measurements Time Synchronization: All measurements timestamped in UTC with microsecond precision 2. Processed Data (processed.zip) Contains cleaned, synchronized, and calculated refractive index data: processed/ training_data.csv # Dec 30, 2024 - Aug 5, 2025 (model development) validation_data.csv # Aug 5, 2025 - Aug 21, 2025 (model validation) full_data.csv # Complete dataset (Dec 30, 2024 - Aug 21, 2025) Data Columns: Column Description Units/Range time UTC timestamp ISO 8601 format counts_ratio Interferometer measurement Dimensionless (2.2584867-3.0) temperature Ambient temperature °C (15-35) humidity Relative humidity % (20-45) pressure Atmospheric pressure hPa (960-1000) n_1762 Calculated refractive index at 1762 nm Dimensionless (~1.00027) Processing Steps: Time synchronization using nearest-neighbor interpolation Quality filtering (valid counts ratio range: 2.2584867-3.0) Refractive index calculation using Ciddor model for rubidium reference Environmental correction with temperature, humidity, and pressure 3. Analysis Code (code.zip) Complete Python analysis pipeline for data processing, visualization, and modeling: Main Scripts: data_loading.ipynb: Data ingestion and preprocessing functions regression_analysis.ipynb: Multivariate regression modeling with uncertainty quantification model_validation.ipynb: Comparison with Ciddor, Edlén, and Mathar atmospheric models visualization.ipynb: Publication-quality figure generation statistical_analysis.ipynb: Correlation analysis and hypothesis testing Dependencies: Python 3.9+ with NumPy, SciPy, pandas, matplotlib, statsmodels, and custom refractive index models (included) Key Features: Automated data quality assessment Multiple regression techniques (OLS, HAC, Prais-Winsten) Bootstrap confidence interval estimation Publication-ready visualization templates Model comparison and validation framework 4. Derived Results (derived.zip) Analysis outputs, figures, and intermediate results: derived/ figures/ data_preview.pdf deviation_validation.pdf kramers_kronig.pdf models/ hitran/ H2O.data H2O.header water.data water.header Scientific Context Measurement Principle The refractive index is determined using a dual-frequency interferometer comparing: Reference frequency: Rubidium transition at 384.2281145 THz Measurement frequency: 1762 nm laser at 170.126432 THz n1762 = nRb × (fRb / f1762) × (1 / R) where R is the interferometer counts ratio, and nRb is calculated using the Ciddor model with environmental corrections. Key Findings Enhanced Humidity Sensitivity 17.2% higher humidity coefficient at 1762 nm compared to standard atmospheric models Wavelength Specificity Significant differences between 1700 nm and 1762 nm models Quantum Precision Sub-ppb refractive index measurements enabled by quantum frequency references Environmental Correlations Strong temperature dependence (-8.8474×10⁻⁷ per °C) and pressure dependence (+2.5950×10⁻⁷ per Pa) Data Quality and Validation Validation Procedures Cross-model comparison: Agreement with Ciddor, Edlén, and Mathar models within 0.3% for temperature dependence Internal consistency: Training/validation split shows consistent statistical properties Physical plausibility: All measurements within expected atmospheric ranges Temporal stability: No systematic drifts or discontinuities detected Potential Applications Atmospheric Science High-precision humidity sensing near water absorption lines Temperature-pressure-humidity coupling studies Climate model validation and refinement Quantum Technologies Quantum network infrastructure planning Optical frequency standard environmental corrections Quantum sensor calibration and validation Remote Sensing Lidar atmospheric correction algorithms Satellite data validation Ground-truth measurements for atmospheric models Fundamental Physics Tests of atmospheric dispersion models Studies of wavelength-dependent refractive index Validation of quantum-enhanced measurement techniques Usage Guidelines Start with processed/full_data.csv for complete analysis Use code/ scripts for reproducible analysis Reference derived/figures/ for visualization templates Consult derived/results/ for pre-computed statistics