Pith Number

pith:VVUMUZH7

pith:2019:VVUMUZH7LEVAK64NRSOCJ7API7

not attested not anchored not stored refs resolved

The Zwicky Transient Facility: Data Processing, Products, and Archive

Adam A. Miller, Angela Van Sistine, Ashish Mahabal, Ben Rusholme, Brian Bue, Christoffer Fremling, Christopher B. Cannella, David Flynn, David Imel, David Levitan, David L. Shupe, Edward Jackson, Eran O. Ofek, Eric C. Bellm, Eugean Hacopians, Frank J. Masci, George Helou, Hsing-Wen Lin, Jason Surace, Justin Howell, Kevin Burdge, Mansi M. Kasliwal, Maria T. Patterson, Mario Juri\'c, Matteo Giomi, Matthew Graham, Nadejda Blagorodnova, Quan-Zhi Ye, Reed Riddle, Richard G. Dekany, Richard Walters, Roger M. Smith, Ron Beck, Russ R. Laher, S. Bradley Cenko, Scott Terek, Serge Monkewitz, Shrinivas R. Kulkarni, Stefanie Wachter, Steven Groom, Thomas A. Prince, Thomas Kupfer, Tim Brooke, Tom Barlow, Umaa Rebbapragada, Vandana Desai, Virginia Cunningham, V. Zach Golkhou, Walter Landry

ZTF's real-time pipeline detects point-source transients via novel image differencing and issues data-rich alerts within 13 minutes at 95th percentile.

arxiv:1902.01872 v1 · 2019-02-05 · astro-ph.IM

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\usepackage{pith}
\pithnumber{VVUMUZH7LEVAK64NRSOCJ7API7}

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

The realtime pipeline employs a novel image-differencing algorithm, optimized for the detection of point source transient events. These events are vetted for reliability using a machine-learned classifier and combined with contextual information to generate data-rich alert packets. The packets become available for distribution typically within 13 minutes (95th percentile) of observation. The reconstructed astrometric accuracy per science image with respect to Gaia is typically 45 to 85 milliarcsec. The derived photometric precision (repeatability) at bright unsaturated fluxes varies between 8 and 25 millimag. Photometric calibration accuracy with respect to Pan-STARRS1 is generally better than 2%.

C2weakest assumption

The assumption that the novel image-differencing algorithm and machine-learned classifier achieve the stated detection reliability and low contamination in real operational data without significant unmodeled systematics or selection effects that would alter the reported performance metrics.

C3one line summary

ZTF's realtime pipeline uses novel image differencing and machine learning to generate transient alerts within 13 minutes, achieving 45-85 mas astrometry and 8-25 millimag photometry with better than 2% calibration.

References

3 extracted · 3 resolved · 1 Pith anchors

[1] The Pan-STARRS1 Surveys 2019 · arXiv:1612.05560

[2] 29 Publications of the Astronomical Society of the Paci ﬁc, 131:018003 (30pp), 2019 January Masci et al 2019

[3] 05243 Gaia Collaboration, Brown, A. G. A., Vallenari, A., et al. 2016, A&A, 595, A2 Graham, E., Kulkarni, S. R., Bellm, E. C., et al. 2018, PASP, submitted Laher, R. R., Masci, F. J., Groom, S., et al 2016

Cited by

65 papers in Pith

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Decoding the Early-Time Light Curves of Type Ia Supernovae. II. Population Parameters of One Thousand ZTF Supernovae

Leveraging Multimodality for Real-Time Classification of Transients and Variables found by the Zwicky Transient Facility

Kepler Image-Subtracted Light Curves and Variable Star Catalog of NGC 6819

Receipt and verification

First computed	2026-07-04T23:22:30.042369Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

ad68ca64ff592a057b8d8c9c24fc0f47e81702c841197a3e824cb79d43a836be

Aliases

arxiv: 1902.01872 · arxiv_version: 1902.01872v1 · doi: 10.48550/arxiv.1902.01872 · pith_short_12: VVUMUZH7LEVA · pith_short_16: VVUMUZH7LEVAK64N · pith_short_8: VVUMUZH7

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/VVUMUZH7LEVAK64NRSOCJ7API7 \
  | 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: ad68ca64ff592a057b8d8c9c24fc0f47e81702c841197a3e824cb79d43a836be

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "efa830b54f15c5706565d0440eb95f9586dd77ce45ef15eab9eae49e94a55ced",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "astro-ph.IM",
    "submitted_at": "2019-02-05T19:10:51Z",
    "title_canon_sha256": "7b4263ad22e05edf929a8ed897562991fc882fbbec5bad52aa08eb3ec234647b"
  },
  "schema_version": "1.0",
  "source": {
    "id": "1902.01872",
    "kind": "arxiv",
    "version": 1
  }
}