IRAC Sensitivity Analysis Pipeline

IRAC Sensitivity Analysis Pipeline - Release Notes

Overview

This release introduces a comprehensive IDL and Python pipeline for performing sensitivity analysis on Spitzer IRAC data. The pipeline processes IRAC mosaic data to determine point source detection limits by simulating PRF placements and performing aperture photometry across multiple flux levels.

New Features

Core Pipeline Components

1. PRF Placement Engine (place_prf.pro))
   • Purpose: Places Point Response Functions into IRAC images with sub-pixel precision
   • Key Features:
   ◦ Channel-specific scaling factors for IRAC channels 1-4
   ◦ Sub-pixel shifting capability
   ◦ Automatic PRF trimming and normalization
   ◦ Multi-channel support with different scaling rules
2. Main Analysis Driver (irac_limit.pro)
   • Purpose: Performs sensitivity analysis at multiple positions around target coordinates
   • Key Features:
   ◦ 3×3 grid placement around target coordinates
   ◦ Configurable aperture photometry parameters
   ◦ Diagnostic plotting capabilities
   ◦ Support for both real data and simulated test images
3. Test Image Generator (test.pro)
   • Purpose: Creates simulated test images with known noise characteristics
   • Key Features:
   ◦ Realistic noise simulation based on image statistics
   ◦ Validation of photometry procedures
   ◦ Output of comprehensive test results
4. Pipeline Controller (main.pro)
   • Purpose: Orchestrates the entire sensitivity analysis pipeline
   • Key Features:
   ◦ Configuration file-based processing
   ◦ Batch processing of multiple targets
   ◦ Error handling and recovery
   ◦ Integration with Python analysis tools
5. Sensitivity Calculator (flux_snr5.py)
   • Purpose: Calculates 5-sigma sensitivity limits using linear regression
   • Key Features:
   ◦ Python-based statistical analysis
   ◦ Linear regression of SNR vs. flux relationships
   ◦ Diagnostic plotting capabilities
   ◦ Multi-channel sensitivity determination
6. Exposure Time Extractor (texp.pro)
   • Purpose: Extracts effective exposure times from IRAC mosaic headers
   • Key Features:
   ◦ Background annulus measurements
   ◦ Multi-channel exposure time extraction
   ◦ Configuration-driven processing