High Frame-rate Imaging Based Photometry, Photometric Reduction of Data from Electron-multiplying Charge Coupled Devices (EMCCDs)

TL;DR

This paper develops a novel photometric reduction method for EMCCD data, enabling stable, high-precision photometry in crowded fields by modeling and correcting unique noise sources.

Contribution

It introduces a probabilistic model and correction algorithm tailored for EMCCD noise characteristics, improving photometric stability in crowded astronomical observations.

Findings

Achieved 1% photometric stability in crowded fields

Developed a probabilistic noise correction model for EMCCD data

Enhanced resolution and photometry for faint stars in dense fields

Abstract

The EMCCD is a type of CCD that delivers fast readout times and negligible readout noise, making it an ideal detector for high frame rate applications which improve resolution, like lucky imaging or shift-and-add. This improvement in resolution can potentially improve the photometry of faint stars in extremely crowded fields significantly by alleviating crowding. Alleviating crowding is a prerequisite for observing gravitational microlensing in main sequence stars towards the galactic bulge. However, the photometric stability of this device has not been assessed. The EMCCD has sources of noise not found in conventional CCDs, and new methods for handling these must be developed. We aim to investigate how the normal photometric reduction steps from conventional CCDs should be adjusted to be applicable to EMCCD data. One complication is that a bias frame cannot be obtained conventionally, as the output from an EMCCD is not normally distributed. Also, the readout process generates spurious charges in any CCD, but in EMCCD data, these charges are visible as opposed to the conventional CCD. Furthermore we aim to eliminate the photon waste associated with lucky imaging by combining this method with shift-and-add. A simple probabilistic model for the dark output of an EMCCD is developed. Fitting this model with the expectation-maximization algorithm allows us to estimate the bias, readout noise, amplification, and spurious charge rate per pixel and thus correct for these phenomena. To investigate the stability of the photometry, corrected frames of a crowded field are reduced with a PSF fitting photometry package, where a lucky image is used as a reference. We find that it is possible to develop an algorithm that elegantly reduces EMCCD data and produces stable photometry at the 1% level in an extremely crowded field.