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arxiv 2303.02154 v1 pith:UPVTLDQ5 submitted 2023-03-03 astro-ph.IM

YSE-PZ: A Transient Survey Management Platform that Empowers the Human-in-the-Loop

classification astro-ph.IM
keywords yse-pzdatatransientsurveyarchivalassociateddeployeddiscovery
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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The modern study of astrophysical transients has been transformed by an exponentially growing volume of data. Within the last decade, the transient discovery rate has increased by a factor of ~20, with associated survey data, archival data, and metadata also increasing with the number of discoveries. To manage the data at this increased rate, we require new tools. Here we present YSE-PZ, a transient survey management platform that ingests multiple live streams of transient discovery alerts, identifies the host galaxies of those transients, downloads coincident archival data, and retrieves photometry and spectra from ongoing surveys. YSE-PZ also presents a user with a range of tools to make and support timely and informed transient follow-up decisions. Those subsequent observations enhance transient science and can reveal physics only accessible with rapid follow-up observations. Rather than automating out human interaction, YSE-PZ focuses on accelerating and enhancing human decision making, a role we describe as empowering the human-in-the-loop. Finally, YSE-PZ is built to be flexibly used and deployed; YSE-PZ can support multiple, simultaneous, and independent transient collaborations through group-level data permissions, allowing a user to view the data associated with the union of all groups in which they are a member. YSE-PZ can be used as a local instance installed via Docker or deployed as a service hosted in the cloud. We provide YSE-PZ as an open-source tool for the community.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Catching Disguised Transients with ASTRANet: Anomaly-Aware Spectroscopic Classification and Conformal Calibration

    astro-ph.IM 2026-07 conditional novelty 6.0

    ASTRANet combines a redshift-free spectral classifier, a 16-score anomaly detector, and conformal prediction to identify and calibrate uncertainty for out-of-taxonomy astronomical transients.

  2. BOOM and Babamul: a real-time, multi-survey, optical alert broker system operating at scale

    astro-ph.IM 2025-10 conditional novelty 5.0

    BOOM is a new high-throughput alert broker using Rust, MongoDB, Valkey and Kafka that matches prior ZTF features at ~7x speed and is extended as Babamul for LSST's 20 million nightly alerts.