Multimodal Pretraining for Scientific Data

Towards Large Data Models for Astrophysics

Francois Lanusse

  • 1000 images each night, 15 TB/night for 10 years

  • 18,000 square degrees, observed once every few days

  • Tens of billions of objects, each one observed 1000 times

SDSS

Image credit: Peter Melchior

DES

Image credit: Peter Melchior

Image credit: Peter Melchior

HSC (proxy for LSST)

The Deep Learning Boom in Astrophysics

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.

The Limits of Traditional Deep Learning

  • Limited Supervised Training Data
    • ​Rare or novel objects have by definition few labeled examples
       
    • In Simulation Based Inference (SBI), training a neural compression model requires many simulations
       
  • Limited Reusability
    • Existing models are trained supervised on a specific task, and specific data.

=> Limits in practice the ease of using deep learning for analysis and discovery

Meanwhile, in Computer Science...

The Rise of The Foundation Model Paradigm

  • Foundation Model approach
    • Pretrain models on pretext tasks, without supervision, on very large scale datasets.
    • Adapt pretrained models to downstream tasks. 
    • Combine pretrained modules in more complex systems.

The Advantage of Scale of Data and Compute

Linearly Accessible Information

  • Backbone of modern architectures embed input images  as vectors in         where d can typically be between 512 to 2048.
     
  • Linear probing refers to training a single matrix to adapt this vector representation to the desired downstream task.
\mathbb{R}^{d}

What This New Paradigm Could Mean for Us Astrophysicists

  • Never have to retrain my own neural networks from scratch
    • Existing pre-trained models would already be near optimal, no matter the task at hand
       
  • Practical large scale Deep Learning even in very few example regime
    • Searching for very rare objects in large surveys like Euclid or LSST becomes possible
       
  • If the information is embedded in a space where it becomes linearly accessible,  very simple analysis tools are enough for downstream analysis
    • In the future, survey pipelines may add vector embedding of detected objects into catalogs, these would be enough for most tasks, without the need to go back to pixels

Can we translate these innovations into a similar paradigm shift in deep learning for scientific applications?

Polymathic

Colm-Cille
Caulfield
University of Cambridge
Leslie
Greengard
Flatiron Institute
New York University
David
Ha

Sakana AI
Yann
LeCun

Meta AI
New York University
Stephane
Mallat
École Normale Supérieure
Collège de France
Flatiron Institute
David
Spergel

Simons Foundation
 
Olga
Troyanskaya

Flatiron Institute
Princeton University
Laure
Zanna
New York University

SCIENTIFIC ADVISORY GROUP

The Data Diversity Challenge

  • Success of recent foundation models is driven by large corpora of uniform data (e.g LAION 5B). 
  • Scientific data comes with many additional challenges:
    • Metadata matters
    • Wide variety of measurements/observations

Credit:DESI collaboration/DESI Legacy Imaging Surveys/LBNL/DOE & KPNO/CTIO/NOIRLab/NSF/AURA/unWISE

Towards Large Multi-Modal Observational Models

Most General

Most Specific

Independent models for every type of observation

Single model capable of processing all types of observations 

Towards Large Multi-Modal Observational Models

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)

Towards Large Multi-Modal Observational Models

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

AstroCLIP

Cross-Modal Pre-Training for Astronomical Foundation Models

Project led by Francois Lanusse, Liam Parker, Leopoldo Sarra, Siavash Golkar, Miles Cranmer
Accepted contribution at the NeurIPS 2023 AI4Science Workshop
Submitted to Monthly Notices of Royal Astronomical Society

What is CLIP?

Contrastive Language Image Pretraining (CLIP)
(Radford et al. 2021)

One model, many downstream applications!

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)

The AstroCLIP approach

  • We use spectra and multi-band images as our two different views for the same underlying object.
     
  • DESI Legacy Surveys (g,r,z) images, and DESI EDR galaxy spectra.

Cosine similarity search

The Information Point of View

  • The InfoNCE loss is a lower bound on the Mutual Information between modalities

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

The AstroCLIP Model (v2, Parker et al. in prep.)

  • For images, we use a ViT-L Transformer (300M).
     
  • For spectra, we use a decoder only Transformer working at the level of spectral patches.

DiNOv2 (Oquab et al. 2023) Image Pretraining

  • Common practice for SOTA CLIP models is to initially pretrain the image encoder before CLIP alignment
  • We adopt the DiNOv2 state of the art Self-Supervised Learning model for the initial large scale training of the model.

 

 

 

 

 

 

  • We pretrain the DiNOv2 model on ~70 million postage stamps from DECaLS   

PCA of patch features

Dense Semantic Segmentation

Dense Depth Estimation

Spectrum Transformer Pretraining by Masked Modeling

  • To pretrain the spectrum embedder, we use a simple Masked Image Modeling strategy
\mathcal{L}_{\textrm{MM}} = \frac{1}{NK} \sum_{j=1}^K \sum_{i=1}^N \textbf{m}_i \cdot (\textbf{x}_{i} - \hat{\textbf{x}}_{i})^2,

Evaluation of the model: Similarity Search

  • Cross-Modal similarity search

Image Similarity

Spectral Similarity

Image-Spectral Similarity

Detecting Galaxy Tidal Features Using Self-Supervised Representation Learning

Project led by Alice Desmons, Francois Lanusse, Sarah Brough

  • Redshift Estimation From Images

Supervised baseline

z_{true}
z_{true}
z_{true}
z_{true}
z_{true}
z_{true}
z_{true}
z_{true}
  • Zero-shot prediction                    
    • k-NN regression

 

 

  • Few-shot prediction
    • MLP head trained on top of frozen backbone

Evaluation of the model: Parameter Inference

  • Galaxy Physical Property Estimation from Images and Spectra

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)

R^2

of regression

Negative Log Likelihood of Neural Posterior Inference

  • Galaxy Morphology Classification

Classification Accuracy

We test a galaxy morphology classification task using as labels the GZ-5 dataset (Walmsley et al. 2021)

Towards Large Multi-Modal Observational Models

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

Towards Large Multi-Modal Observational Models

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

"Massively Multi-Modal Large Data Model for Astrophysics"

Towards Massively Multimodal Large Data Models for Astrophysics

New Generation of Token-Based Multimodal Models

Flamingo: a Visual Language Model for Few-Shot Learning (Alayrac et al. 2022)

Chameleon: Mixed-Modal Early-Fusion Foundation Models (Chameleon team, 2024)

All-to-All Foundation Models

Why Is It Interesting to Us?

Galaxy Image Segmentation
Walsmley & Spindler (2023)

Galaxy Image Deblending

Bosch et al. (2017), Sampson et al. (2024)

=> Foundation Models that build a deep understanding of the data at the pixel level.

Data Collection and Curation

  • Development of large models requires access to "web scale" datasets
     
  • Astrophysics generates large amounts of publicly available data,
    • BUT, data is usually not stored or structured in an ML friendly way.
       
  • Accessing and using scientific data requires significant expertise, for each dataset.

The AstroPile Project

  • Goal: Assemble the first large-scale multi-modal dataset for machine learning in astrophysics.
     
  • Main pillars:
    • Engage with a broad community of AI+Astro experts.
       
    • Adopt standardized conventions for storing and accessing data and metadata through mainstream tools (e.g. Hugging Face Datasets).
       
    • Target large astronomical surveys, varied types of instruments, many different astrophysics sub-fields.
       
  • 10^8 multiband images, 10^6 optical spectra, 10^4 hyperspectral images, 10^9 tabular data, 10^6 time series, and the ability to cross-match all these datasets

Scientific Data Tokenization

Input

Reconstructed

Our strategy: 

  • Develop modality specific but universal tokenizers, i.e. a single model to embed all type of astronomical images
     
  • This requires specific innovations to take into account the metadata of observations.

Example of strategy to embed different bands

Field Embedding Strategy Developed for Multiple Physics Pretraining (McCabe et al. 2023)

What comes next: Any-to-Any modeling

Example of 4M model

Mizrahi et al. (2023)

  • Learns the joint and all conditional distributions of provided modalities:  
  • Can be further fine-tuned to build specialist models for news tasks.
  • Coming soon: training!
\forall m,n \quad p(x_m | x_n)

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Multimodal PreTraining for Scientific Data

By eiffl

Multimodal PreTraining for Scientific Data

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