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COSMO21

May 20 – 24, 2024
Chania, Crete
Europe/Athens timezone

Contribution List

58 out of 58 displayed
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  1. 63. Registration
    5/20/24, 9:00 AM
  2. 54. Introduction by Alan Heavens, George Tzagkarakis and Jean-Luc Starck
    5/20/24, 9:45 AM
  3. 1. Cosmology with the SKA Observatory
    Marta Spinelli
    5/20/24, 10:00 AM
  4. 11. Synergies between radio intensity mapping and optical galaxy surveys
    Alkistis Pourtsidou
    5/20/24, 10:55 AM
  5. 12. Machine Learning for recovering the EoR 21-cm signal power spectrum at z~9.1 from LOFAR data
    Anshuman Acharya
    5/20/24, 11:15 AM
  6. 14. Learning Reionization History with Quasar IGM Damping Wings
    Timo Kist
    5/20/24, 11:35 AM
  7. 13. Using gradient descent-like methods to reconstruct the matter density field during reionization
    Sabrina Berger
    5/20/24, 11:55 AM
  8. 27. Our Universe in Simulation
    Jia Liu
    5/20/24, 3:00 PM
  9. 32. Towards robust Bayesian inference using physics informed priors from cosmological simulations
    Ludvig Doeser
    5/20/24, 3:45 PM
  10. 3. Poster Session
    5/20/24, 4:25 PM
  11. 4. Gravitational-wave cosmology: current results and statistical challenges ahead
    Simone Mastrogiovanni
    5/20/24, 5:55 PM
  12. 65. Cocktail
    5/20/24, 8:00 PM
  13. 2. Higher Order Statistics vs. Deep learning for lensing cosmology
    Bhuvnesh Jain
    5/21/24, 9:00 AM
  14. 20. Cosmic shear analysis of KiDS and DES with higher-order statistics
    Joachim Harnois-Deraps
    5/21/24, 9:45 AM
  15. 18. Baryons and non-Gaussian statistics in weak lensing cosmology
    Daniela Grandon Silva
    5/21/24, 10:05 AM
  16. 16. Dark Energy Survey Year 3 results: likelihood-free, simulation-based wCDM inference with neural compression of weak-lensing map statistics
    Niall Jeffrey
    5/21/24, 10:55 AM
  17. 15. Simulation based inference with contrastive learning and persistent homology
    Judit Prat
    5/21/24, 11:15 AM
  18. 31. KiDS-SBI: Simulation-Based Inference Analysis of KiDS-1000 Cosmic Shear
    Maximilian von Wietersheim-Kramsta
    5/21/24, 11:35 AM
  19. 34. Lossless Neural Compression for Weak Lensing Implicit Inference
    T. Lucas Makinen
    5/21/24, 11:55 AM
  20. 5. Good Data Analysis Practices for AI
    Annalisa Pawlosky
    5/21/24, 3:00 PM
  21. 23. Scientific machine learning in cosmology
    Jason McEwen
    5/21/24, 3:45 PM
  22. 21. Don't trust neural networks? Me neither, but here's how I use them anyway
    Florent Leclercq
    5/21/24, 4:05 PM
  23. 24. Neural Networks as Classifiers of Cosmological Models
    Lisa Goh
    5/21/24, 4:55 PM
  24. 22. Exhaustive Symbolic Regression: Learning Astrophysics directly from Data
    Harry Desmond
    5/21/24, 5:15 PM
  25. 38. One Model to Handle Them All: A versatile framework for galaxy vision tasks by Implanting Human-in-the-loop upon a Large Vision Model
    Nan Li
    5/21/24, 5:35 PM
  26. 10. A comprehensive picture of the galaxy population with simulation-based inference
    Justin Alsing
    5/22/24, 9:00 AM
  27. 37. Machine Learning Pipeline for speeding up SED fitting with Radiative Transfer Models
    Klea Panayidou
    5/22/24, 9:45 AM
  28. 48. A Comparative Analysis of Regression Methods for Spectroscopic Redshift Estimation
    Greg Tsagkatakis
    5/22/24, 10:05 AM
  29. 28. Field-level inferences of galaxy clustering
    Eleni Tsaprazi
    5/22/24, 10:55 AM
  30. 29. Almanac: Generic Field Level Inference for Full-Sky Cosmological Fields and Angular Power Spectra
    Arthur Loureiro
    5/22/24, 11:15 AM
  31. 30. Weak lensing field -level inference with KaRMMa
    Supranta Sarma Boruah
    5/22/24, 11:35 AM
  32. 36. Extracting optimal and robust information from cosmological surveys with field-level inference and joint analyses
    Adrian Bayer
    5/22/24, 11:55 AM
  33. 9. How to quantify textures and random fields in cosmology?
    Sihao Cheng
    5/22/24, 3:00 PM
  34. 40. Mass-Mapping with Conditional Generative Adversarial Networks
    Jessica Whitney
    5/22/24, 3:45 PM
  35. 49. Scalable Bayesian uncertainty quantification with learned convex regularisers
    Tobias Liaudat
    5/22/24, 4:55 PM
  36. 52. GPU-accelerated Hierarchical Bayesian Inference of Intrinsic and Extrinsic Properties of Type Ia Supernovae
    Matthew Grayling
    5/22/24, 5:15 PM
  37. 25. Learned harmonic mean with normalizing flows for Bayesian model comparison in cosmology
    Alicia Polanska
    5/22/24, 5:35 PM
  38. 64. Conference Diner
    5/22/24, 8:00 PM
  39. 7. Explainable deep learning models for cosmological structure formation
    Luisa Lucie-Smith
    5/23/24, 9:30 AM
  40. 39. CAMELS-SAM: untangling the galaxy-halo connection with machine learning in observational space
    Lucia Perez
    5/23/24, 10:15 AM
  41. 50. Unveiling the connections between halos and galaxies with Machine Learning
    Raul Abramo
    5/23/24, 10:35 AM
  42. 51. Using graph neural networks to detect dark matter in stellar streams
    Keir Rogers
    5/23/24, 11:25 AM
  43. 58. A representation learning approach to probe for dynamical dark energy in matter power spectra
    Davide Piras
    5/23/24, 11:45 AM
  44. 47. Unsupervised Searches for Parity-Violation
    Matt Craigie
    5/23/24, 12:05 PM
  45. 8. Cosmic fields beyond 2pt: practical challenges opportunities
    Chihway Chang
    5/23/24, 3:00 PM
  46. 19. Theoretical wavelet l1-norm from one-point PDF prediction
    Vilasini Tinnaneri Sreekanth
    5/23/24, 3:45 PM
  47. 17. An exact likelihood for the weak lensing correlation function
    Veronika Oehl
    5/23/24, 4:05 PM
  48. 42. The interplay between analysis choices in cosmic shear
    Naomi Robertson
    5/23/24, 4:55 PM
  49. 43. DeepLensing Flow: Score based and flow models for weak lensing statistics
    Joaquin Armijo
    5/23/24, 5:15 PM
  50. 44. Mitigating 3x2pt Systematics: Augmented Photo-z Training Samples and Optimal Tomographic Binning
    Irene Moskowitz
    5/23/24, 5:35 PM
  51. 46. The strong lensing contribution to the H0 tension
    Frederic Courbin
    5/24/24, 9:00 AM
  52. 56. Abundance of Primordial Black Holes from a Bayesian inference analysis between quasar microlensing observations and simulated magnification maps
    Ana Esteban Gutiérrez
    5/24/24, 9:45 AM
  53. 59. Understanding the distribution of dark matter in halos from cluster to dwarf scales
    Susmita Adhikari
    5/24/24, 10:05 AM
  54. 35. MLPatch: Learned local patch-based models for high-dimensional cosmological inverse problems
    Amir Ehsan Khorashadizadeh
    5/24/24, 10:55 AM
  55. 67. Joint Multi-channel Deconvolution for Euclid and LSST Images
    Utsav Akhaury
    5/24/24, 11:15 AM
  56. 33. Pseudaria: Map-Level Inference of Unknown Systematics in Galaxy Number Counts
    André Zamorano Vitorelli
    5/24/24, 11:35 AM
  57. 66. Panel Discussion: Moderator Elena Sellentin
    5/24/24, 11:55 AM
  58. 62. Conclusions
    5/24/24, 12:35 PM

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