Francois Lanusse
Image credit: Peter Melchior
Image credit: Peter Melchior
Image credit: Peter Melchior
astro-ph abstracts mentioning Deep Learning, CNN, or Neural Networks
The vast majority of these results has relied on supervised learning and networks trained from scratch.
=> Limits in practice the ease of using deep learning for analysis and discovery
Project led by Francois Lanusse, Liam Parker, Siavash Golkar, Miles Cranmer
Accepted contribution at the NeurIPS 2023 AI4Science Workshop
Contrastive Language Image Pretraining (CLIP)
(Radford et al. 2021)
Flamingo: a Visual Language Model for Few-Shot Learning (Alayrac et al. 2022)
Hierarchical Text-Conditional Image Generation with CLIP Latents (Ramesh et al. 2022)
Cosine similarity search
PCA of patch features
Dense Semantic Segmentation
Dense Depth Estimation
Image Similarity
Spectral Similarity
Image-Spectral Similarity
Supervised baseline
We use estimates of galaxy properties from the PROVABGS catalog (Hahn et al. 2023) (Bayesian spectral energy distribution (SED) modeling of DESI spectroscopy and photometry method)
of regression
Negative Log Likelihood of Neural Posterior Inference
Classification Accuracy
We test a galaxy morphology classification task using as labels the GZ-5 dataset (Walmsley et al. 2021)
Most works so far have relied on (Variational) Auto-Encoders
Autoencoding Galaxy Spectra II: Redshift Invariance and Outlier Detection (Liang et al. 2023)
Self-Supervised similarity search for large scientific datasets (Stein et al. 2021)
Project led by Alice Desmons, Francois Lanusse, Sarah Brough
Shared physical information about galaxies between images and spectra
=> We are building summary statistics for the physical parameters describing an object in a completely data driven way
Redshift information in image embedding
Redshift information in spectra embedding
=> We find in practice that our contrastive alignment behave similarly to Canonical Correlation Analysis
Most General
Most Specific
Independent models for every type of observation
Single model capable of processing all types of observations
Most General
Most Specific
Independent models for every type of observation
Single model capable of processing all types of observations
Bytes Are All You Need (Horton et al. 2023)
Most General
Most Specific
Independent models for every type of observation
Single model capable of processing all types of observations
Bytes Are All You Need (Horton et al. 2023)
AstroCLIP
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