Debating the potential of Machine Learning in astronomical surveys

›10:00 (1h)

Contribution

› Henri Mineur amphitheater / Plenary online session

10:00 - 11:00 (1h)

Contribution

Henri Mineur amphitheater / Plenary online session

› The Dark Quest project for cosmological emulation - Takahiro Nishimichi, Kavli Institute for the Physics and Mathematics of the Universe [Tokyo], Yukawa Institute for Theoretical Physics 10:00-10:15 (15min)

› The scattering transform in cosmology, or, a CNN without Training - Sihao Cheng, Johns Hopkins University 10:15-10:30 (15min)

› Considerations for optimizing photometric classification of supernovae from the Rubin Observatory - Catarina S. Alves, University College, London 10:30-10:45 (15min)

› Machine Learning Calibration of Cosmic Shear Redshift Distributions - Angus Wright, Ruhr University Bochum 10:45-11:00 (15min)

›11:00 (20min)

Session intéractive

› Breakout rooms

11:00 - 11:20 (20min)

Session intéractive

Breakout rooms

›11:20 (45min)

Contribution

› Henri Mineur amphitheater / Plenary online session

11:20 - 12:05 (45min)

Contribution

Henri Mineur amphitheater / Plenary online session

› LyAl-Net: A high-efficiency Lyman-α forest simulation with neural network - Chotipan Boonkongkird, Sorbonne Universite 11:20-11:35 (15min)

› Dark Substructure Sensitivity in the Euclid Survey with Machine Learning - Conor O'Riordan, Max Planck Institute for astrophysics 11:35-11:50 (15min)

› Inferring the assembly and merger histories of galaxies with the IllustrisTNG simulations and machine learning - Lukas Eisert, Max Planck Institute for Astronomy 11:50-12:05 (15min)

›12:05 (20min)

Présentation courte

› Henri Mineur amphitheater / Plenary online session

12:05 - 12:25 (20min)

Présentation courte

Henri Mineur amphitheater / Plenary online session

› Applying Deep Neural Network to dark matter halo catalogues to constrain the dark energy equation-of-state parameters - Farida Farsian, INAF - Osservatorio Astronomico di Bologna, Alma Mater Studiorum University of Bologna 12:05-12:09 (04min)

› Cosmological constraints from galaxy-galaxy lensing and galaxy clustering with HSC-Y1 and BOSS data: the first application of emulator-based halo model to cosmology analysis - Hironao Miyatake, Kavli Institute for the Physics and Mathematics of the Universe [Tokyo], Nagoya University 12:09-12:13 (04min)

› Tidal stream detection in HSC-SSP with Deep Learning - Helena Domínguez Sánchez, Institute of Space Sciences [Barcelona] 12:13-12:17 (04min)

› Using convolutional neural networks to identify strong lenses in Euclid and J-PAS - Alberto Manjon García, Instituto de Física de Cantabria 12:17-12:21 (04min)

› Galaxy Cluster Mass Estimation Using Deep Learning - Matthew Ho, Carnegie Mellon University 12:21-12:25 (04min)

›15:00 (1h)

Revue

› Henri Mineur amphitheater / Plenary online session

15:00 - 16:00 (1h)

Revue

Henri Mineur amphitheater / Plenary online session

› Review on machine learning and cosmological simulations - Francisco Villaescusa-Navarro, Flatiron Institute, Department of Astrophysical Sciences [Princeton] 15:00-16:00 (1h)

›16:00 (20min)

Pause café

› Breakout rooms

16:00 - 16:20 (20min)

Pause café

Breakout rooms

›16:20 (20min)

Présentation courte

› Henri Mineur amphitheater / Plenary online session

16:20 - 16:40 (20min)

Présentation courte

Henri Mineur amphitheater / Plenary online session

› Photometric redshift calibration with simulated annealing and self-organising maps - Benjamin Stölzner, University College, London 16:20-16:24 (04min)

› Semi-Supervised Learning for Probabilistic Photometric Redshifts - Rory Coles, National Research Council of Canada, Herzberg Astronomy & Astrophysics Program, University of Victoria 16:24-16:28 (04min)

› Time Delay Estimation in Unresolved Lensed Quasars via Convolutioanl Neural Networks - Luca Biggio, ETH - Alba Domi, University of Genoa - Luca Paganin, University of Genoa 16:28-16:32 (04min)

› Detection of the Damped Lyman-alpha systems in quasar spectra with machine learning algorithms - ting tan, Laboratoire de Physique Nucléaire et de Hautes Énergies 16:32-16:36 (04min)

›16:35 (45min)

Contribution

› Henri Mineur amphitheater / Plenary online session

16:35 - 17:20 (45min)

Contribution

Henri Mineur amphitheater / Plenary online session

› Real-time science-driven follow-up of survey transients: Optimally augmenting ZTF SN Ia light curves for maximizing SALT2 parameter constraints - 16:35-16:50 (15min)

› What drives the scatter in the BPT diagrams? A Machine Learning based analysis - 16:50-17:05 (15min)

› Quantifying uncertainty in deep-learning classification of radio galaxies - 17:05-17:20 (15min)

›17:20 (40min)

Pause café

› Breakout rooms

17:20 - 18:00 (40min)

Pause café

Breakout rooms

›18:00 (1h30)

Débat

› Henri Mineur amphitheater / Plenary online session

18:00 - 19:30 (1h30)

Débat

Henri Mineur amphitheater / Plenary online session

›20:30 (2h30)

Dîner de la conférence

20:30 - 23:00 (2h30)

Dîner de la conférence

Dîner de la conférence pour les participants en présenciel