{
  "abstract": "Photometric redshifts are essential for large-scale structure analyses, yet extracting optimal point estimates and reliability measures from the probability distribution functions (PDZs) delivered by modern photo-$z$ pipelines remains challenging. We introduce turboPDZ, a machine-learning framework using PDZ features and associated observables to optimize redshift point estimates and derive a data-driven reliability score. We apply it to the three independent HSC-SSP PDR3 pipelines (DEmP, DNNz, Mizuki) across Wide and Deep/UltraDeep layers. Each PDZ is compressed via PCA and combined with summary descriptors. A multilayer perceptron, optimized with Optuna under a composite objective ($σ_{\\rm NMAD}$ and $η_{0.15}$), predicts $z_{\\rm ml}$. A second network, trained in log-space with MAE and calibrated post hoc, yields an empirical error scale $σ_{\\rm ml}$, from which the reliability score $r_{\\rm ml}$ is derived via percentile ranking. Relative to catalog point estimates, $z_{\\rm ml}$ improves both metrics in all six pipeline-layer combinations. Absolute median bias is smaller in four configurations and tied in two. The reliability score achieves the lowest retained-fraction AUC in 10 of 12 native pipeline-layer/metric comparisons. For the Mizuki template-fitting pipeline, catalog reliability indicators have AUC values up to about 13 times larger than those of $r_{\\rm ml}$. Feature importance shows that point estimation is dominated by PCA and location statistics, whereas reliability estimation gives greatest weight to peak, PCA, and shape information. The framework is survey-agnostic, but each PDZ dataset requires its own fitted preprocessing, PCA basis, and trained models. The code will be released as a reusable Python library at \\href{https://github.com/valerio-marra/turboPDZ}{github.com/valerio-marra/turboPDZ}.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2607.26822",
  "author_total": 2,
  "authors": [
    {
      "name": "Rodrigo Duarte"
    },
    {
      "name": "Valerio Marra"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9sba5zncp0j1k.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2607.26822",
  "fulltext_url": null,
  "id": "9sba5zncp0j1k",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2607.26822",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "astro-ph.CO"
    ],
    [
      "arxiv",
      "astro-ph.IM"
    ],
    [
      "openalex",
      "3103|Galaxies: Formation, Evolution, Phenomena"
    ]
  ],
  "openalex": "W7171770374",
  "pdf_url": "https://arxiv.org/pdf/2607.26822",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-29",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/9sba5zncp0j1k.ris",
  "sources": [
    {
      "first_seen": "2026-11-02T12:21:15Z",
      "native_id": "2607.26822",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2607.26822"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.CO",
      "subfield": "cosmology"
    },
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.IM",
      "subfield": "instrumentation-methods"
    }
  ],
  "title": "Optimizing the extraction of information from redshift probability distribution functions",
  "title_text": "Optimizing the extraction of information from redshift probability distribution functions",
  "type": "preprint",
  "updated_at": "2026-11-04T15:20:50Z",
  "url": "https://research.draconitic.com/paper/9sba5zncp0j1k",
  "venue": "arXiv",
  "versions": []
}