Browse State-of-the-Art › Photometric Redshift Estimation
Photometric Redshift Estimation
7 papers with code · 0 benchmarks · 0 datasets archive 2025-07-28
Benchmarks archive 2025-07-28
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Datasets archive 2025-07-28
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Most implemented papers archive 2025-07-28
7 shown of 7 papers with code (12 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.
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30 Aug 2019 5 repositories listed Syntology ran 0 of 6 samples · 6 unverifiedWe provide sample code in Python and R as well as examples of applications to photometric redshift estimation and likelihood-free cosmological inference via CDE.
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15 Jan 2025 1 repository listedIn comparison to tabular models, image models have additional design complexities: it is largely unknown how to fuse inputs from different instruments which have different resolutions or noise properties.
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11 Jan 2025 1 repository listedAccurate and reliable photometric redshift determination is one of the key aspects for wide-field photometric surveys.
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25 Oct 2024 1 repository listedIt leverages supervised contrastive learning (SCL) and k-nearest neighbours (KNN) to construct and calibrate raw probability density estimates, and implements a refitting procedure to resume end-to-end discriminative…
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4 Oct 2023 1 repository listed Syntology ran 2 of 2 samples · 0 unverifiedThese embeddings can then be used - without any model fine-tuning - for a variety of downstream tasks including (1) accurate in-modality and cross-modality semantic similarity search, (2) photometric redshift…
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29 May 2022 1 repository listed Syntology ran 0 of 13 samples · 13 unverifiedKey science questions, such as galaxy distance and weather forecasting, often require knowing the full predictive distribution of a target variable y given complex inputs 𝐱.
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24 Dec 2020 1 repository listedWe show that, without the need for labels, self-supervised learning recovers representations of sky survey images that are semantically useful for a variety of scientific tasks.
Syntology lines on 3 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.
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