# FLIP

Directly imaging exoplanets requires subtracting the star's glare from the image, and standard practice builds that model from reference-star images, so telescope pointing errors and thermal drifts between reference and science observations limit sensitivity. FLIP implements a physics-based instrument model in PyTorch and uses automatic differentiation to optimize millions of parameters in stages, pre-calibrating on a reference star and then tuning on the science image to compensate those instabilities. On JWST/NIRCam coronagraph data, FLIP achieves 2 to 15 times better sensitivity than the standard KLIP method and makes the first detection of 51 Eridani b at 4.3 micrometers in F430M at signal-to-noise 6.3.

Directly imaging exoplanets requires subtracting the star's glare from the image, and standard practice builds that model from reference-star images, so telescope pointing errors and thermal drifts between reference and science observations limit sensitivity. FLIP implements a physics-based instrument model in PyTorch and uses automatic differentiation to optimize millions of parameters in stages, pre-calibrating on a reference star and then tuning on the science image to compensate those instabilities. On JWST/NIRCam coronagraph data, FLIP achieves 2 to 15 times better sensitivity than the standard KLIP method and makes the first detection of 51 Eridani b at 4.3 micrometers in F430M at signal-to-noise 6.3.