Coronagraphic Data Post-processing Using Projections on Instrumental Modes

Fuente: arXiv
Saved in:
Bibliographic Details
Main Authors: Xin, Yinzi, Pueyo, Laurent, Laugier, Romain, Pogorelyuk, Leonid, Douglas, Ewan S., Pope, Benjamin J. S., Cahoy, Kerri L.
Format: Preprint
Published: 2024
Subjects:
Online Access:
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1866908294511067136
author Xin, Yinzi
Pueyo, Laurent
Laugier, Romain
Pogorelyuk, Leonid
Douglas, Ewan S.
Pope, Benjamin J. S.
Cahoy, Kerri L.
author_facet Xin, Yinzi
Pueyo, Laurent
Laugier, Romain
Pogorelyuk, Leonid
Douglas, Ewan S.
Pope, Benjamin J. S.
Cahoy, Kerri L.
contents Directly observing exoplanets with coronagraphs is impeded by the presence of speckles from aberrations in the optical path, which can be mitigated in hardware with wavefront control as well as in post-processing. This work explores using an instrument model in post-processing to separate astrophysical signals from residual aberrations in coronagraphic data. The effect of wavefront error (WFE) on the coronagraphic intensity consists of a linear contribution and a quadratic contribution. When either of the terms is much larger than the other, the instrument response can be approximated by a transfer matrix mapping WFE to detector plane intensity. From this transfer matrix, a useful projection onto instrumental modes that removes the dominant error modes can be derived. We apply this projection to synthetically generated Roman Space Telescope hybrid Lyot coronagraph (HLC) data to extract "robust observables," which can be used instead of raw data for applications such as detection testing. The projection improves planet flux ratio detection limits by about 28% in the linear regime and by over a factor of 2 in the quadratic regime, illustrating that robust observables can increase sensitivity to astrophysical signals and improve the scientific yield from coronagraphic data. While this approach does not require additional information such as observations of reference stars or modulations of a deformable mirror, it can and should be combined with these other techniques, acting as a model-informed prior in an overall post-processing strategy.
format Preprint
id arxiv_https___arxiv_org_abs_2401_04269
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Coronagraphic Data Post-processing Using Projections on Instrumental Modes
Xin, Yinzi
Pueyo, Laurent
Laugier, Romain
Pogorelyuk, Leonid
Douglas, Ewan S.
Pope, Benjamin J. S.
Cahoy, Kerri L.
Instrumentation and Methods for Astrophysics
Directly observing exoplanets with coronagraphs is impeded by the presence of speckles from aberrations in the optical path, which can be mitigated in hardware with wavefront control as well as in post-processing. This work explores using an instrument model in post-processing to separate astrophysical signals from residual aberrations in coronagraphic data. The effect of wavefront error (WFE) on the coronagraphic intensity consists of a linear contribution and a quadratic contribution. When either of the terms is much larger than the other, the instrument response can be approximated by a transfer matrix mapping WFE to detector plane intensity. From this transfer matrix, a useful projection onto instrumental modes that removes the dominant error modes can be derived. We apply this projection to synthetically generated Roman Space Telescope hybrid Lyot coronagraph (HLC) data to extract "robust observables," which can be used instead of raw data for applications such as detection testing. The projection improves planet flux ratio detection limits by about 28% in the linear regime and by over a factor of 2 in the quadratic regime, illustrating that robust observables can increase sensitivity to astrophysical signals and improve the scientific yield from coronagraphic data. While this approach does not require additional information such as observations of reference stars or modulations of a deformable mirror, it can and should be combined with these other techniques, acting as a model-informed prior in an overall post-processing strategy.
title Coronagraphic Data Post-processing Using Projections on Instrumental Modes
topic Instrumentation and Methods for Astrophysics
url https://arxiv.org/abs/2401.04269