The Galileon model is a tensor-scalar theory of gravity which offers a theoretically viable explanation to the late acceleration of the Universe expansion and recovers General Relativity in the strong field limit. The main goal is to establish the status of the model from cosmological observations. Though, the multi-messenger observation of GW170817 and its consequences for the Galileon model will be briefly discussed, since most allowed Galileon scenarios have a gravitational wave speed different than the speed of light.
Most constraints obtained so far on Galileon model parameters from cosmological data were derived for the limited subset of tracker solutions and reported tensions between the model and data. We present here an exploration of the general solution of the Galileon model, which is confronted against recent cosmological data.
We find that, while the general solution provides a good fit to CMB spectra, it fails to reproduce cosmological data when extending the comparison to BAO and SNIa data. Tensions remain if the models are extended with an additional free parameter, such as the sum of active neutrino masses or the normalization of the CMB lensing spectrum.
Optimal transport has become a mathematical gem at the interface of probability, analysis and optimization. It is a theory longly developed by the mathematician community, started by Monge and followed by Kantorovich which found applications in several fields like differential geometry, PDEs or gradient flows just to name a few.
Lately, it began to make its way into the machine learning and data treatment community. The optimal transport can be used to define a distance that is very useful when comparing histograms or point clouds, a typical scenario in nowadays applications. Some breakthrough contributions, like the entropic regularization, allowed to convexify and efficiently solve the transport problem opening the doors for many applications like Wasserstein barycenters or dictionary learning for example.
Nevertheless, Optimal Transport has not entered fully into the signal treatment community. One of the obstacles is the fact that the theory is well developed in the space of nonnegative measures but very little work has been done to extend it to signed measures. Considering a machine learning point of view, this presentation will deal with some theoretic aspects of an Optimal Transport based "distance" for signed measures that can be useful for future applications like Blind Source Separation. An algorithm for its efficient calculation will be presented as well.
- The Sound of APALM Clapping: Faster Nonsmooth Nonconvex Optimization with Stochastic Asynchronous PALM- Kostas
- Cosmological Constraints from Multiple Probes in the Dark Energy Survey - Martin
- How to insert the CosmoStat signature in e-mails - Fadi
- Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning - Fadi
- [Highlighted paper]
- Weak-lensing shear estimates with general adaptive moments, and studies of bias by pixellation, PSF distortions, and noise - Morgan
Date: January 14th 2018, 10am
Speaker: Pol del Aguila Pla (KTH Royal Institute of Technology)
Title: Cell detection by functional inverse diffusion and non-negative group sparsity - Biology, physics, math and engineering [slides]
Image-based immunoassays are used every day across the world to develop new drugs, diagnose diseases, and research the workings of the human body. Since August, some of these are analyzed by technology that, at its core, has an algorithm included in my Ph.D. work. In this talk, I will outline the research project that lead to this algorithm and go through the modeling and optimization results we present in  and . This will include, among others, the modeling of complex biochemical assays as systems of partial differential equations, a linear-systems view on diffusion models, investigations in group-sparsity regularization in function spaces, and first-order methods for optimization problems with more than 25 million variables. To conclude the presentation, I will go through the new paths we have started to explore in connecting all this work to deep learning frameworks .
: Pol del Aguila Pla and Joakim Jaldén, “Cell detection by functional inverse diffusion and non-negative group sparsity—Part I: Modeling and Inverse Problems”, IEEE Transactions on Signal Processing, vol. 66, no. 20, pp. 5407–5421, 2018
: Pol del Aguila Pla and Joakim Jaldén, “Cell detection by functional inverse diffusion and non-negative group sparsity—Part II: Proximal optimization and Performance Evaluation”, IEEE Transactions on Signal Processing, vol. 66, no. 20, pp. 5422–5437, 2018
: Pol del Aguila Pla, Vidit Saxena, and Joakim Jaldén, “SpotNet – Learned iterations for cell detection in image-based immunoassays”, Submitted to the 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), arXiv: 1810.06132 [eess.SP]
Date: January the 24th, 2019
Organizer: Joana Frontera-Pons <firstname.lastname@example.org>
CEA Saclay is around 23 km South of Paris. The astrophysics division (DAp) is located at the CEA site at Orme des Merisiers, which is around 1 km South of the main CEA campus. See http://www.cosmostat.org/contact for detailed information on how to arrive.
On January the 24th, 2019, we organize the fourth day on machine learning in astrophysics at DAp, CEA Saclay.
All talks are taking place at DAp, Salle Galilée (Building 713)
14:00 - 14:30h. Machine Learning in High Energy Physics : trends and successes - David Rousseau (LAL)
14:30 - 15:00h. Learning recurring patterns in large signals with convolutional dictionary learning - Thomas Moreau (Parietal team - INRIA Saclay)
15:00 - 15:30h. Distinguishing standard and modified gravity cosmologies with machine learning - Austin Peel (CEA Saclay - CosmoStat)
15:30 - 16:00h. Coffee break
16:00 - 16:30h. The ASAP algorithm for nonsmooth nonconvex optimization. Applications in imagery - Pauline Tan (LJLL - Sorbonne Université) 16:30 - 17:00h. Deep Learning for Blended Source Identification in Galaxy Survey Data - Samuel Farrens (CEA Saclay - CosmoStat)
Machine Learning in High Energy Physics : trends and successes
David Rousseau (LAL)
Machine Learning has been used somewhat in HEP in the nighties, then at the Tevatron and recently at the LHC (mostly Boosted Decision Tree). However with the birth of internet giants at the turn of the century, there has been an explosion of Machine Learning tools in the industry.. A collective effort has been started for the last few years to bring state-of-the-art Machine Learning tools to High Energy Physics.
This talk will give a tour d’horizon of Machine Learning in HEP : review of tools ; example of applications, some used currently, some in a (possibly distant) future (e.g. deep learning, image vision, GAN) ; recent and future HEP ML Kaggle competitions. I’ll conclude on the key points to set up frameworks for High Energy Physics and Machine Learning collaborations.
Learning recurring patterns in large signals with convolutional dictionary learning
Thomas Moreau (Parietal team - INRIA Saclay)
Convolutional dictionary learning has become a popular tool in image processing for denoising or inpainting. This technique extends dictionary learning to learn adapted basis that are shift invariant. This talk will discuss how this technique can also be used in the context of large multivariate signals to learn and localize recurring patterns. I will discuss both computational aspects, with efficient iterative and distributed convolutional sparse coding algorithms, as well as a novel rank 1 constraint for the learned atoms. This constraint, inspired from the underlying physical model for neurological signals, is then used to highlight relevant structure in MEG signals.
Distinguishing standard and modified gravity cosmologies with machine learning
Austin Peel (CEA Saclay - CosmoStat)
Modified gravity models that include massive neutrinos can mimic the standard concordance model in terms of weak-lensing observables. The inability to distinguish between these cases could limit our understanding of the origin of cosmic acceleration and of the fundamental nature of gravity. I will present a neural network we have designed to classify such cosmological scenarios based on the weak-lensing maps they generate. I will discuss the network's performance on both clean and noisy data, as well as how results compare to conventional statistical approaches.
The ASAP algorithm for nonsmooth nonconvex optimization. Applications in imagery
Pauline Tan (LJLL - Sorbonne Université)
In this talk, I will address the challenging problem of optimizing nonsmooth and nonconvex objective functions. Such problems are increasingly encountered in applications, especially when tackling joint estimation problems. I will propose a novel algorithm and demonstrate its convergence properties. Eventually, three actual applications in industrial imagery problems will be presented.
Deep Learning for Blended Source Identification in Galaxy Survey Data
Samuel Farrens (CEA Saclay - CosmoStat)
Weak gravitational lensing is a powerful probe of cosmology that will be employed by upcoming surveys such as Euclid and LSST to map the distribution of dark matter in the Universe. The technique, however, requires precise measurements of galaxy shapes over larges areas. The chance alignment of galaxies along the line of sight, i.e. blending of images, can lead to biased shape measurements that propagate
to parameter estimates. Machine learning techniques can provide an automated and robust way of dealing with these blended sources. In this talk I will discuss how machine learning can be used to classify sources identified in survey data as blended or not and show some preliminary results for CFIS simulations. I will then present some plans for future developments making use of multi-class classification and segmentation.
Previous Cosmostat Days on Machine Learning in Astrophysics :
Date: October 16th 2018, 2pm
Speaker: Sylvain Vanneste (LAL)
Title: Detecting CMB B-modes
The non-zero mass of neutrinos suppresses the growth of cosmic structure on small scales. Since the level of suppression depends on the sum of the masses of the three active neutrino species, the evolution of large-scale structure is a promising tool to constrain the total mass of neutrinos and possibly shed light on the mass hierarchy. I will discuss recent progress and future prospects to constrain the neutrino mass sum with cosmology, with a focus on observables in the nonlinear regime.
Date: October 9th 2018
Speaker: Chieh-An Lin (IfA, University of Edinburgh)
Title: Predicting weak-lensing covariance with a fast simulator
Weak lensing has been shown as an outstanding tool to constrain cosmology. The state-of-the-art studies have used the power spectrum and peak counts as estimators, and the combination of the two can break down parameter degeneracies and maximize the information extraction.
To constrain cosmology with both estimators, understanding the joint covariance is crucial. However, calculating it analytically seems to be intractable for peaks, and the empirical approach with N-body simulations will be expensive as the size of lensing surveys increase.
I will present a fast solution to solve this problem. The proposed approach simulates lognormal fields and halo models to predict lensing signals. We compared the resulting joint covariance with the one from a large number of N-body simulations and found an excellent agreement. In addition, our approach is orders of magnitude faster than N-body runs.