pith:INZWB3BG
Learning What's Real: Disentangling Signal and Measurement Artifacts in Multi-Sensor Data, with Applications to Astrophysics
Overlapping observations from different instruments train a model to isolate intrinsic galaxy signals from sensor artifacts.
arxiv:2604.09787 v2 · 2026-04-10 · astro-ph.IM · astro-ph.GA · cs.LG
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\pithnumber{INZWB3BG3HLYDHA7UXDW5EUI5D}
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Record completeness
Claims
The resulting representations explicitly separate intrinsic signals from sensor-specific distortions and noise, and can be used for counterfactual view generation, parameter inference unconfounded by measurement distortions, and instrument-independent similarity search.
That overlapping observations of the same physical objects across instruments provide sufficient signal to train a dual-encoder model to isolate sensor artifacts via counterfactual generation without additional labels or strong priors on the artifact distribution.
A dual-encoder deep learning method disentangles intrinsic astrophysical signals from measurement artifacts by treating sensor effects as augmentations and using counterfactual generation on overlapping observations.
Receipt and verification
| First computed | 2026-06-09T01:05:17.078259Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
437360ec26d9d7819c1fa5c76e9288e8e886470907c6a27951879d2452dc993e
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/INZWB3BG3HLYDHA7UXDW5EUI5D \
| 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: 437360ec26d9d7819c1fa5c76e9288e8e886470907c6a27951879d2452dc993e
Canonical record JSON
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"license": "http://creativecommons.org/licenses/by/4.0/",
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"submitted_at": "2026-04-10T18:11:05Z",
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