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arxiv 2509.08064 v1 pith:W6TU2WBL submitted 2025-09-09 astro-ph.GA hep-ph

Via Machinae 3.0: A search for stellar streams in Gaia with the CATHODE algorithm

classification astro-ph.GA hep-ph
keywords streamstreamscathodedetectiongaiamachinaestellaranode
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We apply the model-agnostic anomaly detection method Cathode - originally developed for particle physics - to search for stellar streams in Gaia data. We combine Cathode with Via Machinae 3.0: a re-optimized version of the stellar stream detection method that was previously applied to Gaia data together with the related anomaly detection technique Anode. We demonstrate that the combination of Via Machinae 3.0 with Cathode, called VM3-C, not only re-discovers previously known streams, but also confirms many candidate streams identified in combination with Anode (denoted VM3-A). Compared to VM3-A, the number of stream candidates detected by VM3-C increases by around 10%. Moreover, both of the methods discover the same two large clusters of stream candidates in the Northern Galactic hemisphere. We dub these highly significant anomalous structures the Raritan stream and the Passaic stream. These two structures may indicate the presence of larger objects, such as dwarf galaxy streams, or non-trivial orbital dynamics resulting in bifurcation or fanning, and are promising and high-priority targets for further analysis.

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Forward citations

Cited by 2 Pith papers

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

  1. Characterizing Stellar Streams with Error-Aware Machine Learning

    astro-ph.GA 2026-06 unverdicted novelty 7.0

    SCREAM adapts the CATHODE method to treat stellar streams as feature-space over-densities, incorporates measurement uncertainties into neural network training, and achieves F1=0.745 on GD-1 while recovering faint memb...

  2. Weakly supervised machine learning for model-agnostic searches of new phenomena in the $\gamma$-ray sky

    astro-ph.HE 2026-07 conditional novelty 5.0

    Weakly supervised classifiers trained on background-versus-mixture samples can identify anomalous gamma-ray sources without labeled signal templates, approaching supervised performance in controlled benchmarks.