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1. The Next Generation Deep Extragalactic Exploratory Public (NGDEEP) Survey

2. UVCANDELS: The Role of Dust on the Stellar Mass–Size Relation of Disk Galaxies at 0.5 ≤ z ≤ 3.0

3. The Next Generation Deep Extragalactic Exploratory Public Near-infrared Slitless Survey Epoch 1 (NGDEEP-NISS1): Extragalactic Star-formation and Active Galactic Nuclei at 0.5 < z < 3.6

4. Dusty Starbursts Masquerading as Ultra-high Redshift Galaxies in JWST CEERS Observations

5. CEERS Key Paper. III. The Diversity of Galaxy Structure and Morphology at z = 3–9 with JWST

6. The Physical Thickness of Stellar Disks to z ∼ 2

7. Probing the Earliest Phases in the Formation of Massive Galaxies with Simulated HST+JWST Imaging Data from Illustris

8. Identifying Galaxy Mergers in Simulated CEERS NIRCam Images Using Random Forests

9. A Long Time Ago in a Galaxy Far, Far Away: A Candidate z ∼ 12 Galaxy in Early JWST CEERS Imaging

10. Effects of feedback on galaxies in the VELA simulations: elongation, clumps, and compaction

17. Studying the physical properties of tidal features – I. Extracting morphological substructure in CANDELS observations and VELA simulations

18. Investigating the Effect of Galaxy Interactions on the Enhancement of Active Galactic Nuclei at 0.5

19. A Census of the Bright z = 8.5–11 Universe with the Hubble and Spitzer Space Telescopes in the CANDELS Fields

20. A Long Time Ago in a Galaxy Far, Far Away: A Candidate z ~ 12 Galaxy in Early JWST CEERS Imaging

21. The illustris simulation: Public data release.

22. 3D-DASH: The Widest Near-Infrared Hubble Space Telescope Survey

23. High-resolution Hubble Space Telescope Imaging Survey of Local Star-forming Galaxies. I. Spatially Resolved Obscured Star Formation with Hα and Paschen-β Recombination Lines

24. Mock Galaxy Surveys for HST and JWST from the IllustrisTNG Simulations

25. Morphological signatures of mergers in the TNG50 simulation and the Kilo-Degree Survey: the merger fraction from dwarfs to Milky Way-like galaxies

26. Probing the earliest phases in the formation of massive galaxies with simulated HST+JWST imaging data from Illustris

27. Stellar masses of giant clumps in CANDELS and simulated galaxies using machine learning

28. The mass-metallicity relation at z~1-2 and its dependence on star formation rate

29. A Census of the Bright z=8.5-11 Universe with the Hubble and Spitzer Space Telescopes in the CANDELS Fields

30. Are all post-starbursts mergers? HST reveals hidden disturbances in the majority of PSBs

31. The optical morphologies of galaxies in the IllustrisTNG simulation: a comparison to Pan-STARRS observations

32. DeepMerge II: Building Robust Deep Learning Algorithms for Merging Galaxy Identification Across Domains

34. Powderday: Dust Radiative Transfer for Galaxy Simulations

35. Investigating the Effect of Galaxy Interactions on the Enhancement of Active Galactic Nuclei at 0.5 < $z$ < 3.0

36. Figuring Out Gas & Galaxies In Enzo (FOGGIE). IV. The Stochasticity of Ram Pressure Stripping in Galactic Halos

37. The nature of giant clumps in high-z discs: a deep-learning comparison of simulations and observations

38. Observational Constraints on the Merger History of Galaxies since z ≈ 6: Probabilistic Galaxy Pair Counts in the CANDELS Fields

39. Distinguishing Mergers and Disks in High Redshift Observations of Galaxy Kinematics

40. Studying the Physical Properties of Tidal Features I. Extracting Morphological Substructure in CANDELS Observations and VELA Simulations

41. The Hubble Sequence at $z\sim0$ in the IllustrisTNG simulation with deep learning

42. Galactic Angular Momentum in the Illustris Simulation: Feedback and the Hubble Sequence

43. Merging Things Together: Merger Pair Analysis from the IllustrisTNG Simulation Suite

45. Beyond spheroids and discs: classifications of CANDELS galaxy structure at 1.4 <z< 2 via principal component analysis

46. Galaxy morphology and star formation in the Illustris Simulation atz = 0

47. The Morphology–Density Relationship in 1 < z < 2 Clusters

48. DeepMerge: Classifying high-redshift merging galaxies with deep neural networks

49. Deep Learning Identifies High- z Galaxies in a Central Blue Nugget Phase in a Characteristic Mass Range

50. Galaxy Zoo: Morphological classification of galaxy images from the Illustris simulation

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