Spitzer: IRAC HPP

	Spitzer Documentation & Tools
Overview IRAC IRS MIPS Data Archive Data Analysis & Tools Index of Documentation Helpdesk Spitzer FAQs	IRAC High Precision Photometry > Removing Correlated Noise > Correlated Noise Home There are several techniques in the literature for removing correlated noise from IRAC high precision photometry datasets. Please refer to the following papers as starting points for a discussion of those techniques. BiLinearly Interpolated Subpixel Sensitivity Gain Mapping Gaussian Process Models Independent Component Analysis MCMC evaluation Nearest Neighbors Pixel Level Decorrelation Kernel Regression using a calibration pixel mapping dataset (clicking this link will download a large file). See Krick et al. 2016 for a description of this technique Polynomial IRAC HPP Home Correlated Noise ↧ ↥ Spacecraft Motion Repeatable Pointing ↧ ↥ PCRS Peak-Up Results Effective Use of PCRS Peak-Up Catalog Mapping Intrapixel Gain ↧ ↥ Centroiding Noise Pixel Optimal Aperture Size Residual Nonlinearity Staring Mode Darks Superdarks IRAC Photometric Stability Persistent Images Removing Correlated Noise Repeatability and Accuracy Measuring Noise Reduction ↧ ↥ RMS vs. Binning Scale Frametimes longer than 2 seconds Tools ↧ ↥ Contributed Software Planning Observations Data Simulator Archival Observations Data Features ↧ ↥ Updated Distortion Correction Header Keywords Skydark Change in the Middle of an Observation Background Modulation Noise Pixel Spikes Timing 58th Frame in Subarray Datasets Corrupted Header Position Jump Wheel Speed Change Pointing History File Change Effect of CR on centroiding algorithm Pattern Noise Sample Datasets Meetings ↧ ↥ IRAC IAU 2015 ↧ ↥ Poster Data Challenge

IRAC High Precision Photometry