pith:NJZK2IAY
Sensitivities of Black Hole Images from GRMHD Simulations
Automatic differentiation-computed gradients of GRMHD black hole images can guide parameter exploration even in the presence of noise.
arxiv:2604.11869 v2 · 2026-04-13 · astro-ph.HE
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\pithnumber{NJZK2IAYMAPWHZHLRXTZYZUZEO}
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Claims
Automatic differentiation-computed image gradients can guide parameter exploration effectively even in the presence of noise.
That the mock data analysis and idealized blurring/noise models sufficiently represent the challenges of real EHT observations, including unmodeled systematics and the full complexity of GRMHD parameter degeneracies.
Differentiable GRMHD image sensitivities create a structured error landscape that supports gradient-based parameter recovery for black hole imaging under idealized and noisy conditions.
Receipt and verification
| First computed | 2026-06-19T16:10:37.304535Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
6a72ad2018601f63e4eb8de79c669923a20f3156a2f11595fa60f17bb5b9aa31
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/NJZK2IAYMAPWHZHLRXTZYZUZEO \
| 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: 6a72ad2018601f63e4eb8de79c669923a20f3156a2f11595fa60f17bb5b9aa31
Canonical record JSON
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