Welcome Room: IRAP

AI for SDU at OMP and the region

• Etienne Gondet (OMP)

Room: IRAP

Comment ça marche Chat GPT ? une introduction à l'Intelligence Artificielle

• Jonathan Sprauel (IRT Saint-Exupéry)

Room: IRAP Tout ce que vous avez toujours voulu savoir sur l'Intelligence Artificielle, depuis l'optimisation et les modèles d'analyse d'image jusqu'au grands modèles de langage : quelles sont les différentes technologies et comment elles marchent, est-ce qu'on peut avoir confiance dans l'IA, quelles techniques d'IA je peux utiliser aujourd'hui dans mon métier, que font les chercheurs en IA, quels sont les impacts écologiques et sociétaux de l'IA.

AI or not AI ? Overview of methods developed at SISU

• Hervé Carfantan

Room: IRAP

Physics models in Unrolling algorithms applied to Coded-Aperture Snapshot Spectral Imagers

• Léo Paillet

Room: IRAP Léo Paillet, Hervé Carfantan, Simon Lacroix, Antoine Monmayrant We made a CASSI (Coded Aperture Snapshot Spectral Imager) optical simulator which allows to consider a realistic optical model. The model is then used to map input positions to output positions in order to adapt unrolling algorithms to realistic systems. Through this simulator and mapping function, we prove that the inverse problem of reconstructing hyperspectral scenes from CASSI acquisitions with unrolling algorithms is similarly solved for any type of CASSI system.

What can we learn from the XMM source catalog ?

• Simon Dupourqué (GAHEC)

Room: IRAP The 4XMM catalogue contains more than 20 years of X-ray observations. However, exploiting this data on the catalogue scale is not straightforward since most of the automatically extracted sources do not have a precise classification. Our aim is to use machine learning, specifically a variational autoencoder, to embed the sources in a latent space that is more suitable for exploring and classifying the sources and finding exotic objects.

Machine Learning-Led Classification of X-ray Sources Through Time-Domain Variability

• Robbie Webbe (GAHEC)

Room: IRAP Unusual patterns of variability in X-ray sources can hold clues to the identities of hard to classify sources, and can be indicators of rare and exotic astrophysical phenomena like magnetar outbursts and Quasi-Periodic Eruptions in AGN. The ability to adequately identify interesting sources through their time-domain variability can often be difficult with the large volumes of data being produced, and in the archives of large missions like XMM-Newton and Chandra. This problem will become ever more important with the anticipated launch of larger observatories like NewAthena in the next decade. Here I will present the preliminary results of an investigation into the use of classifiers for the identification of the rare phenomenon in AGN, Quasi-Periodic Eruptions and Quasi-Periodic Oscillations, and outline a route map for the development of an ensemble classifier aimed at providing robust predictions of sources solely through features extracted from their X-ray lightcurves.

Unsupervised classification of galaxy spectra up to z~1.2. Towards galaxy spectral evolution ?

• Jihane Moultaka (GAHEC)

Room: IRAP I will present our results of spectral galaxy classifications from GAMA and VIPERS surveys in the nearby universe and up to z~1.2 using an unsupervised method called Fisher-EM. This method is particularly well-suited to high-dimensional parameter spaces as is the case of galaxy spectra. Our approach enables us to highlight classes of galaxy spectra that allow to refine the bimodal distribution of galaxies and build an evolutionary tree up to z~1.2.

Panorama des activités IA sur ARIEL

• Orphée Faucoz (CNES)

Room: IRAP La mission ARIEL de l'ESA vise à analyser la composition chimique des atmosphères exoplanétaires grâce à la spectroscopie de transit. Cette présentation se concentrera sur l'utilisation du Machine Learning pour transformer et améliorer les données ARIEL, en s'appuyant notamment sur des Data Challenges dédiés et des approches innovantes. Nous présenterons comment l'apprentissage automatique est utilisé pour traiter les différents niveaux de produit de la mission afin d'améliorer la résolution et de corriger les bruits. Nous aborderons les travaux actuels sur la modélisation chimique des atmosphères au travers des algorithmes IA utilisés. Enfin, nous parlerons des perspectives et des retours d'expérience sur l'utilisation du Machine Learning.

RMI Analyst

• Olivier Gasnault (IRAP)

Room: IRAP We are developing a tool to classify images taken by ChemCam on Mars and rank their similarities.

Investigating the benefits of Deep Learning for particle pulse-shape discrimination in a space-borne silicon detector

• Pierre Devoto (IRAP/CNRS)

Room: IRAP The SP@M (for Solar Particles @ Mars) instrument onboard M-MATISSE will measure Solar Energetic Particles (SEPs, protons and electrons) throughout the Martian magnetosphere and atmosphere. While most of today's on-board instruments discriminate particles using coincidence processing between 2 or more silicon detectors arranged as a telescope, SP@M proposes to use a single thick silicon detector and to discriminate particle types (mainly electrons, protons and alphas ) using digital processing based on charge carrier collection time proportional to particle penetration length inside the detector. However, the first tests show that discriminating particles types can be challenging, particularly at low energies, as there is a confusion zone where electrons and protons have the same energies and collection times. We have investigated different Machine learning algorithms to take advantage of the difference of the pulse shapes between the particles types. We report their efficiency in discrimination and regression and we discuss the possibility of performing such a discrimination onboard a satellite.

SPARTAI — AI-based forecasting pipeline for energetic electrions in the Earth’s radiation belts

• Rungployphan Kieokaew (IRAP et Inria Centre de Paris)

Room: IRAP Highly energetic electrons (at the MeV level) in the vicinity of the Earth’s radiation belts pose significant risks on satellites through single event effects and long-term radiation damage, in GEO, MEO, and LEO orbits. Accurate forecasting of electron fluxes in this environment is essential for risk mitigation and spacecraft operations. Over the past few decades, several physics-based models have been developed to forecast radiation belt conditions. Here, we develop an AI-based forecasting pipeline for MeV-level electron fluxes a few days in advance using NOAA’s space weather instruments and NASA-NOAA’s Geostationary Operational Environmental Satellites (GOES). We tested and benchmarked several architectures of machine learning and neural network architectures (e.g., CNN, LSTM, and Transformers), as well as several combinations of these to address data sparsity while optimising performance. Our preliminary results are rather promising, with an R2 score over 80 % for all L-shells (McIlwain L-parameter). Using data covering for at least one complete solar cycle, we will present our findings during extreme events. We will also consider European-based and the ESA’s space weather data for model training and for the development of the future operational forecasting pipeline. This work is a prototype product demonstration co-developed by Inria for Augura Space, a deep tech startup that delivers AI-powered space weather intelligence.

What do we know about the environmental impact of research computing (including AI, but not limited to it)?

• Karin Dassas (CNRS, CESBIO)

Room: IRAP Cette présentation tentera d'apporter un éclairage sur l'impact environnemental que peuvent avoir les calculs faits à l'OMP, localement ou au sein de Calmip, Trex, ou les machines du GENCI. Elle abordera aussi l'impact de l'usage de l'IA générative pour aider à coder (IA4code), et la question des usages (IA4Green ?).