pith:DSDUZMT5
Focal plane wavefront control with model-based reinforcement learning
A model-based reinforcement learning algorithm corrects non-common-path aberrations from focal-plane images alone.
arxiv:2604.00993 v1 · 2026-04-01 · astro-ph.IM · astro-ph.EP · cs.LG · cs.RO
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\pithnumber{DSDUZMT5HNLURD7PJMGZOAJBFL}
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Claims
Simulations show that PO4NCPA robustly compensates static and dynamic NCPAs. In static cases, it achieves near-optimal focal-plane light suppression with a coronagraph and near-optimal Strehl without one. With dynamic NCPA, it matches the performance of the modal least-squares reconstruction combined with a 1-step delay integrator.
The numerical simulations of static NCPA on a ground-based telescope and water-vapor-induced dynamic NCPA accurately capture the statistics, noise properties, and temporal behavior that will be encountered on real ELT-class instruments with a vector vortex coronagraph.
PO4NCPA applies model-based RL to focal-plane images via sequential phase diversity to correct static and dynamic NCPAs, achieving near-optimal suppression in simulations for coronagraphic and non-coronagraphic cases.
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Receipt and verification
| First computed | 2026-05-27T14:06:42.972831Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
1c874cb27d3b57488fef4b0d9701212ae7c6148cf976f067e9d5d72d006d2134
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/DSDUZMT5HNLURD7PJMGZOAJBFL \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 1c874cb27d3b57488fef4b0d9701212ae7c6148cf976f067e9d5d72d006d2134
Canonical record JSON
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