Modeling and simulated optimization of the AstroPIC photonic coronagraph

The AstroPIC project is currently developing a coronagraph intended to detect exo-Earths and characterize their atmospheres that uses a photonic integrated circuit (PIC) to separate star and planet light. By adopting a PIC-centric architecture, we take advantage of the theoretical ability to perform arbitrary linear operations on any finitely-sampled electric field to isolate specific modes from pupil electric fields that are either dominated by starlight or by system aberrations and remove those modes from the final science data. In order to understand the interactions between real-world impacts on PIC performance on design decisions, it is necessary to model the coronagraph numerically. We discuss the framework we are using for simulating AstroPIC in the lab, and use that framework to examine reconfiguring the tunable circuit mesh to recover high-contrast on the order of 10<sup>−10</sup> in broadband light, and in the presence of biased directional couplers. Using the same mesh configuration as the AstroPIC Cycle 1 laboratory prototype, we find phase shifter settings that create a single-channel 10<sup>−10</sup> dark zone in a &Delta;&lambda;/&lambda; = 0.025 band. Separately, in monochromatic light, we optimize these settings to recover a fourth-order dark zone when simulating directional coupler biases on the order of several percent.

Authors

Institutions

Publication Details

Published
2025-09-18
DOI
https://doi.org/10.1117/12.3065826
Primary Topic
Adaptive optics and wavefront sensing
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Modeling and simulated optimization of the AstroPIC photonic coronagraph

Dan Sirbu, Rachel Morgan, Eduardo Bendek, Kevin Fogarty et al.
Adaptive optics and wavefront sensing
article

Modeling and simulated optimization of the AstroPIC photonic coronagraph

Dan Sirbu, Rachel Morgan, Eduardo Bendek, Kevin Fogarty, Ruslan Belikov
article en

Abstract

The AstroPIC project is currently developing a coronagraph intended to detect exo-Earths and characterize their atmospheres that uses a photonic integrated circuit (PIC) to separate star and planet light. By adopting a PIC-centric architecture, we take advantage of the theoretical ability to perform arbitrary linear operations on any finitely-sampled electric field to isolate specific modes from pupil electric fields that are either dominated by starlight or by system aberrations and remove those modes from the final science data. In order to understand the interactions between real-world impacts on PIC performance on design decisions, it is necessary to model the coronagraph numerically. We discuss the framework we are using for simulating AstroPIC in the lab, and use that framework to examine reconfiguring the tunable circuit mesh to recover high-contrast on the order of 10<sup>−10</sup> in broadband light, and in the presence of biased directional couplers. Using the same mesh configuration as the AstroPIC Cycle 1 laboratory prototype, we find phase shifter settings that create a single-channel 10<sup>−10</sup> dark zone in a &Delta;&lambda;/&lambda; = 0.025 band. Separately, in monochromatic light, we optimize these settings to recover a fourth-order dark zone when simulating directional coupler biases on the order of several percent.

Ames Research Center (US), Search for Extraterrestrial Intelligence (US)
National Aeronautics and Space Administration, Space Technology Mission Directorate
Openalex Percentile: Top 80%
Adaptive optics and wavefront sensing
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.