LLM embeddings condition a generative transformer to enable faster convergence, better performance, and generalization to unseen LHC processes using a single model.
AstroCLIP: a cross-modal foundation model for galaxies , volume=
8 Pith papers cite this work, alongside 55 external citations. Polarity classification is still indexing.
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A masked autoencoder model trained on 4.7 million DESI single-fiber observations predicts emission-line maps from images that match independent MaNGA IFU data without any IFU training.
PITA, a new semi-supervised deep learning algorithm, outperforms prior photo-z methods by using a triple-task loss on images, colors, and available redshifts to produce a smooth latent space.
SPENDER autoencoder plus k-d tree nearest-neighbor classification on DESI spectra identifies AGN and broad-line AGN at accuracies 0.952 and 0.965, recovering sources missed by single-line diagnostics.
Hyrax is a GPU-enabled open-source framework for the full ML lifecycle in astronomy, with demonstrations of unsupervised discovery and classification on real survey data from Rubin, ZTF, and other projects.
A CNN detects 19,685 LAEs at z=2-3.5 in DESI DR1 spectra with 95% purity and completeness.
ASTRAFier is a Transformer-BiLSTM-CNN model that classifies stellar variability from light curves, reporting 94.26% accuracy on Kepler data and 88.22% on TESS, then applied to 2.8 million TESS curves to release a catalog.
Proposes foundation models and decision-theoretic policies to manage evolving source representations and optimize follow-up resource allocation in LSST-scale time-domain astronomy.
citing papers explorer
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One Generator, Any Process: LLM-Conditioning for the LHC
LLM embeddings condition a generative transformer to enable faster convergence, better performance, and generalization to unseen LHC processes using a single model.
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Integral Field Unit Spectroscopy with One Fiber
A masked autoencoder model trained on 4.7 million DESI single-fiber observations predicts emission-line maps from images that match independent MaNGA IFU data without any IFU training.
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Optimizing Deep Learning Photometric Redshifts for the Roman Space Telescope with HST/CANDELS
PITA, a new semi-supervised deep learning algorithm, outperforms prior photo-z methods by using a triple-task loss on images, colors, and available redshifts to produce a smooth latent space.
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Beyond traditional emission-line diagnostics: using autoencoders to uncover active galactic nuclei in DESI spectra
SPENDER autoencoder plus k-d tree nearest-neighbor classification on DESI spectra identifies AGN and broad-line AGN at accuracies 0.952 and 0.965, recovering sources missed by single-line diagnostics.
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Hyrax: An Extensible Framework for Rapid ML Experimentation and Unsupervised Discovery in the Era of Rubin, Roman, and Euclid
Hyrax is a GPU-enabled open-source framework for the full ML lifecycle in astronomy, with demonstrations of unsupervised discovery and classification on real survey data from Rubin, ZTF, and other projects.
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Unveiling Hidden Lyman Alpha Emitters in the DESI DR1 Data
A CNN detects 19,685 LAEs at z=2-3.5 in DESI DR1 spectra with 95% purity and completeness.
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ASTRAFier: A Novel and Scalable Transformer-based Stellar Variability Classifier
ASTRAFier is a Transformer-BiLSTM-CNN model that classifies stellar variability from light curves, reporting 94.26% accuracy on Kepler data and 88.22% on TESS, then applied to 2.8 million TESS curves to release a catalog.
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Toward decision-aware AI for LSST-scale time-domain astronomy
Proposes foundation models and decision-theoretic policies to manage evolving source representations and optimize follow-up resource allocation in LSST-scale time-domain astronomy.