Euclid joint meeting: WL + GC + CG SWG + OU-LE3

Poll:

 

Date

February 3 - 7, 2020


Venue

IAP - Institue d'Astrophysique de Paris, 98 bis, bd Arago, 75014 Paris


Program

The preliminary schedule can be found here:

https://docs.google.com/document/d/1XHDepk3W4897GMqxABpo4vgubhm2LFVYVOCgyTqGS_I/edit

Slides (password-protected) are on redmine.

The meeting starts on Monday 3 February at 9:30.

 


Participant list

Please add your name to the following list if you intend to participate. To access IAP, external people are required to indicate their name in advance of the meeting, and might have to show identification at the IAP front desk. There is no conference fee.  

https://docs.google.com/document/d/17Hn8Z6LH54fJDbDY2uQPtZPauZotm6IsnNC4LbBcmII/edit


Practical information

How to get to IAP.

Hotel list.

Restaurant list.


Contacts

Martin Kilbinger  <kilbinger@iap.fr>

Sandrine Codis <codis@iap.fr>

 

Cosmostat Day on Machine Learning in Astrophysics

Date: January the 17th, 2020

Organizer:  Joana Frontera-Pons  <joana.frontera-pons@cea.fr>

Venue:

Local information

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 17th, 2020, we organize the 5th day on machine learning in astrophysics at DAp, CEA Saclay. 

Program:

All talks are taking place at DAp, Salle Galilée (Building 713)

10:00 - 10:15h. Welcome and coffee
10:15 - 10:45h. Parameter inference using neural networks Tom Charnock (Institut d'Astrophysique de Paris)
10:45 - 11:15h. Detection and characterisation of solar-type stars with machine learning -  Lisa Bugnet (DAp, CEA Paris-Saclay)
11:15 - 11:45h. DeepMass: Deep learning dark matter map reconstructions with Dark Energy Survey data - Niall Jeffrey (ENS)

12:00 - 13:30h. Lunch

13:30 - 14:00h. Hybrid physical-deep learning models for astronomical image processing - François Lanusse (Berkeley Center for Cosmological Physics and CosmoStat CEA Paris Saclay)
14:00 - 14:30h. A flexible EM-like clustering algorithm for noisy data Violeta Roizman (L2S, CentraleSupélec)                                                           
14:30 - 15:00h. Regularizing Optimal Transport Using Regularity Theory -  François-Pierre Paty (CREST, ENSAE)
15:00 - 15:30h. Deep Learning @ Safran for Image Processing -  Arnaud Woiselle (Safran Electronics and Defense)

15:30 - 16:00h. End of the day


Parameter inference using neural networks

Tom Charnock (Institut d'Astrophysique de Paris)

Neural networks with large training sets are currently providing tighter constraints on cosmological and astrophysical parameters than ever before. However, in their current form, these neural networks are unable to give true Bayesian inference of such model parameters. I will describe why this is true and present two methods by which the information extracting power of neural networks can be built into the necessary robust statistical framework to perform trustworthy inference, whilst at the same time massively reducing the quantity of training data required.


Detection and characterisation of solar-type stars with machine learning

Lisa Bugnet (DAp, CEA Paris-Saclay)

Stellar astrophysics has been strengthened in the 70’s by the discovery of stellar oscillations due to acoustic waves inside the Sun. These waves evolving inside solar-type stars contain information about the composition and dynamics of the surrounding plasma, and are thus very interesting for the understanding of stellar internal and surface physical processes. With classical asteroseismology we are able to extract very precise and accurate masses, radius, and ages of oscillating stars, that are key parameters for the understanding of stellar evolution.
However, classical methods of asteroseismology are time consuming processes, that can only be applied for stars showing a large enough oscillation signal. In the context of the hundred of thousand stars observed by the Transiting Exoplanet Survey Satellite (TESS), the stellar community has to adapt the methodologies previously built for the study of the few ten thousand of stars observed with much better resolution by the Kepler satellite. Our “method exploits the use of Random Forest machine learning algorithms that aim at automatically 1) classifying and 2) characterizing any stellar pulsators from global non-seismic parameters. We also present a recent result based on neural networks on the automatic detection of peculiar solar-type pulsators that have a surprinsigly low dipolar-oscillation amplitude, the signature of an unknown physical process affecting oscillation modes inside the core.


DeepMass: Deep learning dark matter map reconstructions with Dark Energy Survey data

Niall Jeffrey (ENS)

I will present the first reconstruction of dark matter maps from weak lensing observational data using deep learning. We train a convolution neural network (CNN) with a Unet based architecture on over 3.6×10^5 simulated data realisations with non-Gaussian shape noise and with cosmological parameters varying over a broad prior distribution. We interpret our newly created DES SV map as an approximation of the posterior mean P(κ|γ) of the convergence given observed shear. DeepMass method is substantially more accurate than existing mass-mapping methods with a a validation set of 8000 simulated DES SV data realisations. With higher galaxy density in future weak lensing data unveiling more non-linear scales, it is likely that deep learning will be a leading approach for mass mapping with Euclid and LSST.


Hybrid physical-deep learning models for astronomical image processing

François Lanusse (Berkeley Center for Cosmological Physics and CosmoStat CEA Paris Saclay)

The upcoming generation of wide-field optical surveys which includes LSST will aim to shed some much needed light on the physical nature of dark energy and dark matter by mapping the Universe in great detail and on an unprecedented scale. However, with the increase in data quality also comes a significant increase in  data complexity, bringing new and outstanding challenges at all levels of the scientific analysis.
In this talk, I will illustrate how deep generative models, combined with physical modeling, can be used to address some of these challenges at the image processing level, specifically by providing data-driven priors of galaxy morphology.
I will first describe how to build such generative models from corrupted and heterogeneous data, i.e. when the training set contains varying observing
conditions (in terms of noise, seeing, or even instruments). This is a necessary step for practical applications, made possible by a hybrid modeling of the
generation process, using deep neural networks to model the underlying distribution of galaxy morphologies, complemented by a physical model of
the noise and instrumental response. Sampling from these models produces realistic galaxy light profiles, which can then be used in survey emulation,
for the purpose of validating and/or calibrating data reduction pipelines. 

Even more interestingly, these models can be used as priors on galaxy morphologies and used as such as part of standard Bayesian inference techniques to solve astronomical inverse problems ranging from deconvolution to deblending galaxy images. I will present how combining these deep morphology priors with a physical forward model of observed blended scenes allows us to address the galaxy deblending problem in a physically motivated and interpretable way.


A flexible EM-like clustering algorithm for noisy data

Violeta Roizman (L2S, CentraleSupélec)

Though very popular, it is well known that the EM algorithm suffers from non-Gaussian distribution shapes and outliers. This talk will present a flexible EM-like clustering algorithm that can deal with noise and outliers in diverse data sets. This flexibility is due to extra scale parameters that allow us to accommodate for heavier tail distributions and outliers without significantly loosing efficiency in various classical scenarios. I will show experiments where we compare it to other clustering methods such as k-means, EM and spectral clustering when applied to both synthetic data and real data sets. I will conclude with an application example of our algorithm used for image segmentation.


Regularizing Optimal Transport Using Regularity Theory

François-Pierre Paty (CREST, ENSAE)

Optimal transport (OT) dates back to the end of the 18th century, when French mathematician Gaspard Monge proposed to solve the problem of déblais and remblais. In the last few years, OT has also found new applications in statistics and machine learning as a way to analyze and compare data. Both in practice and for statistical reasons, OT need be regularized. In this talk, I will present a new regularization of OT leveraging regularity of the Monge map. Instead of considering regularity as a property that can be proved under suitable assumptions, we consider regularity as a condition that must be enforced when estimating OT. This further allows us to transport out-of-sample points, as well as define a new estimator of the 2-Wasserstein distance between arbitrary measures. (Based on a joint work with Alexandre d'Aspremont and Marco Cuturi).


Deep Learning @ Safran for Image Processing

Arnaud Woiselle (Safran Electronics and Defense)

Deep learning has become the natural tool in computer vision for nearly all high-level tasks, such as object detection and classification for many years, and is now state of the art in most image processing (restoration) tasks, such as debluring or super-resolution. Safran looked into these methods for a large variety of problems, focusing on the use of a low number of network structures, due to electronics constraints for future implementation, and transferred them to real-life noisy and blurry data, both in the visible and the infrared. I will show the results in many applications, and conclude with some tips and take-away messages on what seems important to apply deep learning on a given task.


 Previous Cosmostat Days on Machine Learning in Astrophysics :

École Euclid de cosmologie 2019

Date: August 19 - August 31, 2019

Venue: Banyuls, Occitanie, France

Website: http://ecole-euclid.cnrs.fr/accueil-session-2019


Lecture ``Weak gravitational lensing'' (Le lentillage gravitationnel), Martin Kilbinger.

Find here links to the lecture notes, TD exercises, "tables rondes" topics, and other information.

  • Resources.
    • A great and detailed introduction to (weak) gravitational lensing are the 2005 Saas Fee lecture notes by Peter Schneider. Download Part I (Introduction to lensing) and Part III (Weak lensing) from my homepage.
    • Check out Sarah Bridle's video lectures on WL from 2014.
  • TD cycle 1, Data analysis.
    1.  We will work on a rather large (150 MB) weak-lensing catalogues from the public CFHTLenS web page. During the TD I will show instructions how to create and download this catalogue. These catalogues will also be available on the virtual machine for the school, or download here.
      If you want to do the TD on your laptop, you'll need to download and install athena (the newest version 1.7). Available on the VM.
  • Lecture notes and exercise classes.  You can already download the slides in one file (40 - 60 MB), but be ware that the content will still change slightly until the classes.
    • Part I (Cycle 1):    [all ]
    • Part II (Cycle 2):  [day 1 (4/6)] The lectures for day 2+3 are given by Nicolas Martinet]
    • TD:                             [cycle 1]. The TDs for cycle 2 are given by Nicolas as well.
    • Table Ronde sujet

GOLD : The Golden Cosmological Surveys Decade

This 10-week programme on the Golden Cosmological Surveys Decade will be held at the new Institut Pascal, in Paris Orsay, from 1st April 2020 to 5th June 2020. The Institut Pascal provides offices, seminar rooms, common areas and supports long-term scientific programmes. 
 
GOLD 2020 will include a summer school, three workshops (on Lensing, Galaxy Clustering, Theory and Interpretation of the Data). 
In-between, an active training programme will be run. We plan to host around 40 people for the whole programme, plus around 30 scientists during the workshops. 
Whether you are a PhD, a postdoc, a senior scientist and are interested in attending this programme, you can now apply. Deadline for applications: 1st October 2019.

 

 

Cosmostat Day on Machine Learning in Astrophysics

Date: January the 24th, 2019

Organizer:  Joana Frontera-Pons  <joana.frontera-pons@cea.fr>

Venue:

Local information

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. 

Program:

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 :

Euclid – France atelier/workshop gravitational lensing

Date: October 22, 2018

Organizer:  Martin Kilbinger & Karim Benabed

Venue: IAP,  98bis bd Arago, 75014 Paris. Salle Entresol

Local information

http://www.iap.fr/accueil/acces/acces.php


Participants

Martin Kilbinger
Karim Benabed
Sandrine Codis
Eric Jullo
Francis Bernardeau
Yohan Dubois
Santiago Casas
Raphael Gavazzi
Alain Blanchard
Patrick Hudelot
Calum Murray
Matteo Rizzato
Samuel Farrens
Alexandre Barthelemy
Austin Peel
Nicolas Martinet
Morgan Schmitz
Virginia Ajani
Henry McCracken
Peter Taylor
Bertrand Morin
Céline Gouin

Program

 

10:00   Café
10:30   Martin Kilbinger                Welcome, introduction, goals of the meeting, resources
10:45   Matteo Rizzato                   Information content in the weak lensing bispectrum
11:15   Eric Jullo                                 WLSWG work package “Galaxy-galaxy lensing”
11:45   Alexandre Barthelemy    One-point statistics of weak lensing maps
12:15    Peter Taylor                         k-cut Cosmic Shear: Tunable Power Spectrum Sensitivity to Test Gravity
12:45   Henry Joy McCracken    Euclid VIS activities and weak lensing requirements
13:00   Lunch
14:15   Austin Peel                           Peak counts: breaking degeneracies & machine learning
14:45   Nicolas Martinet               WL peak/mass mapping/shear calibration
15:15   Céline Gouin                       The impact of baryons on WL statistics
15:45   Bertrand Morin                  COSEBIs - Implementation of cosmic shear E-/B- modes
16:15   Martin Kilbinger, all          WL projects in Euclid-France, discussion, future plans
17:15   End

 

 

French-Chinese Days on Weak Lensing

Date: October 4-5, 2018

Organizer:  Jean-Luc Starck and Martin Kilbinger

Venue:

Local information

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/link/how-to-get-to-sap/ for detailed information on how to arrive.


On 4 and 5 October, 2018, we are organizing the first French-Chinese weak-lensing meeting at DAp, CEA Saclay. 

Program:

All talks are taking place at DAp, Salle Kepler (Building 709)

Thursday, October 4

9:30 - 10:00h.  Café

10:00 - 10:15h.    Welcome & introductions

10:15 - 10:45h.   Hu Zhan,   Overview of CSS-OS

10:45 - 11:15h.   Martin Kilbinger, Overview of CFIS Weak Lensing

11:15 - 11:45h.  Jun Zhang, Fourier_Quad,  a shear measurement method in Fourier Space

11:45 - 14:00h.  Lunch at the Rotonde

14:00 - 14:30h. Morgan Schmitz,  PSF Modeling using a Graph Manifold

14:30 - 15:00h. Chengliang Wei, A full sky WL simulation with semi-analytic galaxy formation 

15:00 - 15:30h. Jean-Luc Starck,  WL Mass Mapping

15:30 - 16:00h. Zuhui Fan,  WL peak statistics

16:00 - 16:30h. Austin Peel,  Cosmology with Mass Maps

Friday, October 5

9:30 - 10:00h.      Café

10:00 - 10:30h.   Sam Farrens,   The CFIS pipeline

10:30 - 11:00h.  Ran Li,  Lensing studies of sub-structures

11:00 - 11:30h.  Axel  Guinot,  Preliminary CFIS results

11:30 - 12:00h.  Liping Fu, Shear measurement from VOICE deep survey

12:00 - 14:00h. Lunch at Les Algorithmes

14:00 - 14:30h. Jean-Charles Cuillandre, The Euclid mission and ground-based observations

14:30 - 15:00h.  Huanyuan Shan: KiDS WL studies (via skype)

15:00 - 15:30h.  Alexandre Bruckert, Machine learning for blended objects separation

15:30 - 16:00h.   Rebeca Araripe Furtado Cunha,  Optimal Transport and PSF Modeling

16:00 -  17:00h. Discussion

 

École Euclid de cosmologie 2018

Date: August 20 - September 1, 2018

Venue: Roscoff, Bretagne, France

Website: http://ecole-euclid.cnrs.fr/accueil-session-2018


Lecture ``Weak gravitational lensing'' (Le lentillage gravitationnel), Martin Kilbinger.

Find here links to the lecture notes, TD exercises, "tables rondes" topics, and other information.

  • Resources.
    • A great and detailed introduction to (weak) gravitational lensing are the 2005 Saas Fee lecture notes by Peter Schneider. Download Part I (Introduction to lensing) and Part III (Weak lensing) from my homepage.
    • Check out Sarah Bridle's video lectures on WL from 2014.
  • TD cycle 1+2, Data analysis.
    1.  We will work on a rather large (150 MB) weak-lensing catalogues from the public CFHTLenS web page. During the TD I will show instructions how to create and download this catalogue. These catalogues will also be available on the virtual machine for the school.
      If you like, you can however download the catalogue on your laptop at home. Please have a look at the instructions in the TD slides.
    2. If you want to do the TD on your laptop, you'll need to download and install athena (the newest version 1.7). Available on the VM.
    3.  For one of the bonus TD you'll need a new version of pallas.py (v 1.8beta). Download it here. Available on the VM.
  • Lecture notes and exercise classes.  You can already download the slides in one file (40 - 60 MB), but be ware that the content will still change slightly until the classes.
    • Part I (Cycle 1):    [all | day 1 (1/6)  |   day 2 (2/6) |  day 3 (3/6)]
    • Part II (Cycle 2):  [all | day 1 (4/6)   |   day 2 (5/6)  | day 3 (6/6)]
    • TD:                             [1/2 and 2/2]
    • Table Ronde sujet
  • Slack channel: ede2018.slack.com