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1. Boosting HI-Galaxy Cross-Clustering Signal through Higher-Order Cross-Correlations

2. Quantifying Baryonic Feedback on Warm-Hot Circumgalactic Medium in CAMELS Simulations

3. Cosmological and Astrophysical Parameter Inference from Stacked Galaxy Cluster Profiles Using CAMELS-zoomGZ

4. Constraining Cosmology with Simulation-based inference and Optical Galaxy Cluster Abundance

5. CHARM: Creating Halos with Auto-Regressive Multi-stage networks

6. How DREAMS are made: Emulating Satellite Galaxy and Subhalo Populations with Diffusion Models and Point Clouds

7. Field-level Emulation of Cosmic Structure Formation with Cosmology and Redshift Dependence

8. Towards unveiling the large-scale nature of gravity with the wavelet scattering transform

9. Cosmological simulations of scale-dependent primordial non-Gaussianity

10. The Impact of Non-Gaussian Primordial Tails on Cosmological Observables

11. Cosmology from point clouds

12. Denoising Diffusion Delensing Delight: Reconstructing the Non-Gaussian CMB Lensing Potential with Diffusion Models

13. Introducing the DREAMS Project: DaRk mattEr and Astrophysics with Machine learning and Simulations

14. Debiasing with Diffusion: Probabilistic reconstruction of Dark Matter fields from galaxies with CAMELS

15. Zooming by in the CARPoolGP lane: new CAMELS-TNG simulations of zoomed-in massive halos

16. Probing the Circum-Galactic Medium with Fast Radio Bursts: Insights from the CAMELS Simulations

17. Quijote-PNG: Optimizing the summary statistics to measure Primordial non-Gaussianity

18. Cosmological multifield emulator

19. Can we constrain warm dark matter masses with individual galaxies?

20. A field-level emulator for modeling baryonic effects across hydrodynamic simulations

21. Taming assembly bias for primordial non-Gaussianity

22. Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data Sets

23. Atomic Hydrogen Shows its True Colours: Correlations between HI and Galaxy Colour in Simulations

24. Field-level simulation-based inference with galaxy catalogs: the impact of systematic effects

25. Cosmology with Galaxy Photometry Alone

26. Cosmology with multiple galaxies

27. An Observationally Driven Multifield Approach for Probing the Circum-Galactic Medium with Convolutional Neural Networks

28. Predicting Interloper Fraction with Graph Neural Networks

29. Emulating Radiative Transfer with Artificial Neural Networks

30. Cosmological baryon spread and impact on matter clustering in CAMELS

31. A Hierarchy of Normalizing Flows for Modelling the Galaxy-Halo Relationship

32. Signatures of a Parity-Violating Universe

33. Quijote-PNG: The Information Content of the Halo Mass Function

34. Inferring Warm Dark Matter Masses with Deep Learning

35. Cosmology with one galaxy? -- The ASTRID model and robustness

36. The CAMELS project: Expanding the galaxy formation model space with new ASTRID and 28-parameter TNG and SIMBA suites

37. Invertible mapping between fields in CAMELS

38. A universal equation to predict $\Omega_{\rm m}$ from halo and galaxy catalogues

39. Robust Field-level Likelihood-free Inference with Galaxies

40. Predicting the impact of feedback on matter clustering with machine learning in CAMELS

41. Inferring the impact of feedback on the matter distribution using the Sunyaev Zel'dovich effect: Insights from CAMELS simulations and ACT+DES data

42. Machine-learning cosmology from void properties

43. Calibrating cosmological simulations with implicit likelihood inference using galaxy growth observables

44. Quijote-PNG: Quasi-maximum likelihood estimation of Primordial Non-Gaussianity in the non-linear halo density field

45. Emulating cosmological multifields with generative adversarial networks

46. Robust field-level inference with dark matter halos

47. The SZ flux-mass ($Y$-$M$) relation at low halo masses: improvements with symbolic regression and strong constraints on baryonic feedback

48. Quijote PNG: The information content of the halo power spectrum and bispectrum

49. Field Level Neural Network Emulator for Cosmological N-body Simulations

50. Simple lessons from complex learning: what a neural network model learns about cosmic structure formation

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