Exoplanet direct imaging data challenge Carlos Alberto Gomez Gonzalez Paris-Saclay Center for Data Science, 11/04/2018 

Grenoble Alpes Data Institue ■ WP1: Data Science for Earth, Space and Environmental Sciences ■ WP2: Data Science for Life Sciences ■ WP3: Massive and Rich Data for Humanities ■ WP4: Data Science, Social Media and Social Sciences ■ WP5: Data Governance, Data Protection and Privacy WP0: Coordination MSTIC - Mathematics, Information and Communication Sciences and Technologies CBS - Chemistry, Biology, Health PAGE - Particle physics, Astrophysics, Geosciences, Environment and ecology SHS - Humanities and Social Sciences PSS - Social Sciences USMB – Univ Savoie Mont Blanc 19 Labs involved that cover 5 research domains IDEX Cross disciplinary Program • 1.7 million euros • From 2017 to 2020 

Grenoble Alpes Data Institue • Data Challenge: • epigenetic & High-dimension Mediation Data Challenge, • audio-visual diarization, • cancer research, • home-made framework (codalab / jupyter)? • Data Science in the Alpes (March 20) • R in Grenoble group. • PySciDataGre group. • Data club. • Data Carpentry. 

What do I do • Interdisciplinary research. • Exoplanetary science and astrophysics with CS & ML. • Integrating cutting-edge ML developments. • Ensuring the use of robust statistical approaches and well-suited metrics. • Open-source development. • Data challenges. 

Mostly, we rely on indirect methods for detecting exoplanets … it’s very hard to “see” them 

Credit: NASA, http://planetquest.jpl.nasa.gov ANIMATION 

Milli et al. 2016 Konopacky et al. 2013 Bowler 2016 Marois et al. 2010 HR8799, L’ band 20 AU 0.5” b c d e Power of direct imaging 

SPHERE, Vigan et al. 2015 Very Large Telescope (VLT), Chile 

Basic calibration and “cosmetics” • Dark/bias subtraction • Flat fielding • Sky or thermal background subtraction • Bad pixel correction Raw astronomical images Detection on final residual image Image recentering Bad frames removal PSF modeling • Median • Pairwise, ANDROMEDA • LOCI • PCA, NMF • LLSG Image combination Model PSF subtraction De-rotation (ADI) or rescaling (mSDI) Characterization of detected companions Sequence of calibrated images 

calib. im ages planet Angular differential imaging - bright synthetic planet Speckle noise ANIMATION 

HR8799 bcde (Marois et al. 2008-2010) On of the lucky cases! Final images after post-processing (several epochs) post- proc. ANIMATION 

• Available on Pypi • https://github.com/vortex-exoplanet/VIP • Documentation (Sphynx): http://vip.readthedocs.io/ • Jupyter tutorial • Python 2/3 compatibility • Continuous integration (Travis CI) and automated testing (Pytest) Gomez Gonzalez et al. 2017 Vortex Image Processing library 

Marois et al. 2007 Gomez Gonzalez et al. 2016 Lafrenière et al. 2007 A lgo- ZO O Marois et al. 2007 Soummer et al. 2012 Amara & Quanz 2012 Absil et al. 2013 Gomez Gonzalez et al. 2017 Gomez Gonzalez et al. 2016 Gomez Gonzalez et al. 2016 Marois et al. 2014 Marois et al. 2014 Hagelberg et al. 2015 Mugnier et al. 2009 Cantalloube et al. 2015 

Open Science & reproducibility Open source 

“Today, software is to scientific research what Galileo’s telescope was to astronomy: a tool, combining science and engineering. It lies outside the central field of principal competence among the researchers that rely on it. … it builds upon scientific progress and shapes our scientific vision.” Pradal et al. 2015 

Image sequence Detection Final residual image ? ? ? ? ? ? ? Speckles (?) Real planet Synth. planet 

ANIMATION 

“Essentially, all models are wrong, but some are useful.” George Box “…if the model is going to be wrong anyway, why not see if you can get the computer to ‘quickly’ learn a model from the data, rather than have a human laboriously derive a model from a lot of thought.” Peter Norvig 

PC 1 PC 2 Unsupervised Supervised Regression Classification Dimensionality reduction Clustering ML in a nutshell Density estimation and reinforcement learning 

The goal is to learn a mapping from the input samples to the labels: given a labeled dataset : Supervised learning Goodfellow et al. 2016 

Deep neural networks 

Supervised detection of exoplanets Gomez Gonzalez et al. 2018 

Model PSF subtraction combined residuals Supervised detection (SODINN) noisy and unlabelled images data transformation + adequate (ML) model higher sensitivity 

Data-driven performance assessment 

No need to reinvent the wheel :) 

• Small committee takes care of most of the planing. • Main organizer takes care of logistics/ leaderboard. • Main organizer writes a review-type paper. • Community effort. • Using a robust framework for data challenges creation. • Hands-on sessions. • Workshop for analyzing results and learning from different approaches. Old school Open science Data challenges 

• Low detection rate so far - observational bias or reality? • Several instruments/surveys with large databases. • New instruments coming online in the next years. • ~13 years of image processing techniques. • Discovering new techniques! Exoplanet DI challenge Motivation 

• Benchmark datasets — Metrics — Sub-challenges. • Finding the right platform (RAMP?). • Avoid excluding “old” code/pipelines: IDL, MATLAB, etc. Exoplanet DI challenge Planning: • benchmark datasets • sub-challenges • metrics Kick-off one-day session (RAMP?) Submission of results • Final leaderboard • Comparison of results/ approaches Workshop: image processing for exoplanet DI 

• Possible sub-challenges. • Each observing technique: different data format/dimensionality, variability to exploit. • Detection & characterization. • Focus on exoplanets (excluding disks?). Exoplanet DI challenge ADI (NACO, SPHERE/ IRDIS, NIRC2, LMIRcam, etc) ADI + mSDI (SPHERE/IFS, GPI, etc) RDI, other techniques? 

¡Gracias! [email protected] carlgogo carlosalbertogomezgonzalez https://carlgogo.github.io/