01:04:21

The physics of molecular hydrogen in space with JWST

01:12:56

The vigorous growth of early galaxies

01:09:40

Modeling gravitational waves from asymmetric binaries: from the intermediate to the extreme

01:09:45

The Emergence of Dwarf Galaxies, Star Clusters and Something In-between

59:47

The GBAR project, progress and prospects

01:03:20

Measuring and modelling galaxy intrinsic alignment

01:05:10

Cosmic ray feedback and magnetic dynamos in galaxy formation

01:30:34

LA RÉVOLUTION DES EXOPLANÈTES

01:02:14

String theory: from the landscape to the swampland

57:33

From X-rays to gamma-rays: recent developments on supernova remnants physics

01:09:32

Énergie nucléaire, cycles du combustible et technologies de réacteurs

01:05:48

Simulations and models of the cosmic reionization evolution

52:56

The main sequence of active galaxies: a star formation history

58:18

Dissecting core-collapse supernovae using neutrinos

01:03:36

Dark matter searches: status and prospects

58:37

Search for gravitational waves with pulsar timing array

00:59

L’Institut d’astrophysique de Paris vous souhaite une excellente année 2024 !

54:25

Review: Symmetries in Deep Learning (S. Villar)

01:00:46

Review: Deep learning algorithms for morphological classification of galaxies (H. Dominguez-Sanchez)

01:25:49

ML-IAP/CCA-2023: Debate #3 "What is the impact of large language models in astronomy?"

36:30

Conclusions to ML-IAP/CCA-2023 (Licia Verde)

17:28

Scientific Discovery from Ordered Information Decomposition

15:18

Anomaly detection using local measures of uncertainty in latent representations

14:26

Significance Mode Analysis (SigMA) for hierarchical structures

15:59

Reionisation time fields reconstruction from 21 cm signal maps

16:00

Generative Topographic Mapping for tomographic redshift estimates

15:17

Spatially Variant Point Spread Functions for Bayesian Imaging

10:06

Machine-directed gravitational-wave counterpart discovery

08:35

Machine learning as a key component in the science processing pipelines of space- and ground-ba ...

03:35

Finding Observable Environmental Measures of Halo Properties using Neural Networks

03:59

Embedding Neural Networks in ODEs to Learn Linear Cosmological Physics

04:18

Optimizing Galaxy Sample Selections for Weak Lensing Cluster Cosmology

14:30

Likelihood-free Forward Modeling for Cluster Weak Lensing and Cosmology

16:26

Data-driven galaxy morphology at z higher than 3 with contrastive learning and cosmological si ...

03:18

Assessing and Benchmarking the Fidelity of Posterior Inference Methods for Astrophysics Data An ...

17:26

Cosmological constraints from HSC survey first-year data using deep learning

16:22

Cosmology with Galaxy Photometry Alone

15:43

Machine Learning Powered Inference in Cosmology

15:23

HySBI - Hybrid Simulation-Based Inference

06:21

ChatGaia

13:29

Extending the Reach of Gaia DR3 with Self-Supervision

03:38

CNNs reveal crucial degeneracies in strong lensing subhalo detection

05:51

Selection functions of strong lens finding neural networks

07:53

CNNs reveal crucial degeneracies in strong lensing subhalo detection

11:21

Efficient and fast deep learning approaches to denoise large radioastronomy line cubes ​and to ...

16:38

Causal graphical models for galaxy surveys

01:00:29

Review: Capitalizing on Artificial Intelligence for LSS Cosmology (Tomasz Kacprzak)

01:28:54

ML-IAP/CCA-2023: Debate #2 "What can machine-learning do for the next generation surveys?"

04:12

Investigations for LSST with Machine Learning: Photometric redshift predictions, strong lens de ...

13:45

Who threw that rock? Tracing the path of martian meteorites back to the crater of origin using ML

03:26

Emulating the Universe: overcoming computational roadblocks with Gaussian processes

03:59

Debating the Benefits of Differentiable Cosmological Simulators for Weak Lensing Full-Field In ...

03:40

Fast realistic, differentiable, mock halo generation for wide-field galaxy surveys

03:10

Perturbation theory emulator for cosmological analysis

12:12

Explaining dark matter halo abundance with interpretable deep learning

16:35

Deep Learning Generative Models to Infer Mass Density Maps from SZ, X-ray and Galaxy Members Ob ...

16:31

Subhalo effective density slope measurements from HST strong lensing data with neural likelihoo ...

14:05

Doing More With Less; Label-Efficient Learning for Euclid and Rubin

03:38

Deconstructing the galaxy merger sequence with machine vision

03:14

Vision Transformers for Cosmological Inference from Weak Lensing