Sparsity and Deep Learning for Modern Surveys
Statistical Challenges in 21st Century Cosmology -- Valencia, Spain
The Large Synoptic Survey Telescope
- 18,000 square degrees, observed once every few days
- 1000 images each night , 15 TB/night for 10 years
- Tens of billions of objects, each one observed $\sim1000$ times
$\Longrightarrow$ Unprecedented volume of data
Huang et al. (2017)
$\Longrightarrow$ Unprecedented complexity of data
The challenges of modern surveys
- Handling the increased data rates
- Accessing the information present in the data
- Controlling systematic errors
$y = \mathbf{A} x + n$
$\mathbf{A}$ non-invertible or ill-conditioned
$\Longrightarrow$
ill-posed inverse problem with no unique solution $ x$
Deconvolution
Inpainting
Denoising
The sparse recovery framework
- Sparsity as a powerful and generic signal prior
- Fast algorithms for recovering a MAP solution
$\mathrm{argmin}_{x} \quad \parallel y - A x \parallel_2^2 \ + \ \lambda \parallel \Phi^* x \parallel_1$
The GLIMPSE mass-mapping algorithm
Peel, Lanusse, Starck (2017)
$\mathrm{argmin}_\kappa \frac{1}{2} \parallel \mathcal{C}_\kappa^{-1} ( (1 - \kappa) g - \mathbf{T} \ \mathbf{Q} \ \mathbf{F} \kappa ) \parallel_2^2 + \lambda \parallel \Phi^* \kappa \parallel_1 + \ i_{\mathbb{R}}(\kappa)$
$\mathrm{argmin}_\delta \frac{1}{2} \parallel \mathcal{C}_\kappa^{-1} ( (1-\kappa) g - \mathbf{T} \ \mathbf{P} \ \mathbf{Q} \ \mathbf{F} \delta ) \parallel_2^2 + \lambda \parallel \Phi^* \delta \parallel_1 + \ i_{>0}(\delta)$
- Includes reduced shear and individual redshift PDFs
- Solved using proximal adapted from (Vu, 2013)
Lanusse et al. (2016)
Lanusse & Starck, in prep.
3D reconstruction of the COSMOS field
Transverse Wiener Filter
Simon et al. (2012)
Glimpse
Lanusse & Starck, in prep.
A few comments
$\mathrm{argmin}_{x} \quad \parallel y - A x \parallel_2^2 \ + \ \lambda \parallel \Phi^* x \parallel_1$
- Relies on knowledge of $\mathbf{A}$
- Requires expert knowledge to choose $\Phi$
In the news lately...
- Self-driving Uber takes the road in Pittsburgh (Sept. 2016)
- CMU's Libratus beats top poker players (Jan. 2017)
- Google's AlphaGo beats world's top Go player (May 2017)
Technological revolution brought about by the advancement of Deep Learning.
WWAD: What Would an Astrophysicist Do ?
gri composite
g - $\alpha$ i
detected areas
HST images
RingFinder (Gavazzi et al. 2014)
Requires ~30 person-minute/sq. deg. for visual inspection
Gavazzi et al. (2014), Collett (2015)
Plainly intractable at the scale of LSST
A conventional Convolutional Neural Network
Preactivated Residual Unit
(He et al. 2016)
CMUDeepLens Architecture
(Lanusse et al. 2017)
Some CMUDeepLens results
True Positive Rate = $\frac{TP}{TP + FN}$
- $TP$: True Positives
- $FN$: False Negatives
False Positive Rate = $\frac{FP}{FP + TN}$
- $FP$: False Positives
- $TN$: True Negatives
The Euclid strong-lens finding challenge
Better accuracy than human visual inspection !
The promise of Deep Learning
- Purely data-driven
- Little expert knowledge necessary
- No painstacking feature design
The need for complex data models
Tenneti et al. (2015)
The need for data-driven generative models
- Lack or inadequacy of physical model
- Extremely computationally expensive simulations
Can we learn a model for the signal from the data ?
The evolution of generative models
- Deep Belief Network
(Hinton et al. 2006)
- Variational AutoEncoder
(Kingma & Welling 2014)
- Generative Adversarial Network
(Goodfellow et al. 2014)
- Wasserstein GAN
(Arjovsky et al. 2017)
A visual Turing test
Fake PixelCNN samples
Real SDSS
Learning COSMOS galaxy morphologies
Ravanbakhsh, Lanusse et al. (2017)
Learning the galaxy-halo connection
Lanusse et al. (in prep.)
Deep Learning on graphs
Kipf & Welling (2017)
Preliminary results on graph Mixture Density Network model
Lanusse et al. (in prep.)
Conclusions
- Mathematics based methods should be used whenever possible
- Deep Learning allows you to automatize Machine Learning
- Generative models will be a key element in making our simulations more realistic