CPLUOS Astrophysics team meeting

Friday 2 Sept 2022, 14:00 → 15:20 Asia/Seoul

물리학과

https://uos-ac-kr.zoom.us/j/8264902652

COSMOLOGY¶

1 Hyeonmo's Report¶

Speaker: Hyeonmo Koo (University of Seoul)

Head-on Collsion of FDM/CDM Halos

• FDM halo collision
  • Gravitational cooling effect

• Result
  • The velocity decrease of FDM halo after collision is greater than the CDM case.

2 Young's report. FoF and MST algorithm¶

Speaker: Young Ju

Mulguisin (MGS) Clustering Algorithm 1. Comparison of Clustering Algorithms for Study of Cosmic Structure Finding

• MGS algorithm
  • 1. FInd galaxy with the highest density.
  • 2. The galaxy becomes a new cluster, 'MGS'
  • 3. FInd the next galaxy with second highest density and estimate the distance between 'MGS' and next galaxy
  • 4. If the distance is less than our criteria, the second galaxy is merged with 'MGS'
  • 5. This process continues until there is no galaxy.
• Features of MGS algorithm
  • 1. MGS can find structure from galaxy data in detail.
  • 2. Provide topological information.
    • Number of nodes
    • Number of branches
    • Number of children
    • Link length
    • Average node generation

3 Hannah's Report¶

See the slides from 8p

Speaker: Hannah Jhee (University of Seoul)

Tracking Halo Orbits and Their Mass Evolution around the Large-scale Filament Structures

• Summary
  • 1. Halos approaching filaments exhibit two types of phase-space trajectories, which is coupled to the halos' initial condition.
  • 2. Halos are virialized in the filament environment after at least 6 Gyr since their first pericenter crossing, and those virialized (bound) halos and fly-bys show different mass evolution tendency.
  • 3. Mass evolution of halos in the denser filaments would be suppressed.
  • 4. Mass segregation can be understood as excess time difference between massive and less massive halos, and the segregation tendency dilutes after z = 1.
• Future work
  • To be discussed including such as:
    • How to involve filament evolution in the discussion?
    • How about gas properties?
    • How about halos infalling clusters 'through' the filaments?

4 Seyeon's Report - CLML¶

Speaker: Se Yeon Hwang (Universe of Seoul)

Cosmology with Large scale structure using Machine Learning (CLML)

• Preview
  • Data (large scale simulation ~ 1, 2 Gpc, dark matter halos catalog → Pixelize 3-d histogram → Convolution neural network (CNN)+Vision trasformer (ViT) → Cosmological parameters
• Goal
  • Predicting Ω_m

5 Sumi's report¶

Speaker: Sumi Kim (University of Seoul)

Cosmology of High-Order Statistics (CHOA)

• N-point correlation function
  • 2 pcf
    • Isotropic
    • Anisotropic
  • 3 pcf
    • Isotropic
    • Anisotropic
  • 4 pcf
    • Isotropic
    • Anisotropic
• Parameter estimation
  • Simple machine learning
    • N-pcf results array → Decision tree/Random forest → Parameter estimation array

6 John Suarez's report¶

Alignment Correlation between Cosmic Web Filaments and Galaxies Orientations

• Goal
  • To study the environmental Dependent evolution of local galaxies.
• Proposal
  • To understand the correlations between the galaxy distribution and its environment.
  • To identify the alignment of galaxies and their nearest cosmic web filament.
  • Does this alignment depend on some environmental features?
  • To create valued added catalogs for the DESI collaboration.
• Galaxy and mass assembly (GAMA)
• Matching GAMA and DESI
• Segments filaments
• Galaxies and NSF tilts
• Difference between galaxies and NSF tilt
  • α = ϕ-θ
    • There is an alignment between the direction of the galaxies and the tilt of the filament.

7 Dr.Sabiu's Report¶

Speaker: Dr Cristiano Sabiu (University of Seoul)

Deep Learning the Deep Sky: Recovering low surface brightness objects with machine learning

• No progress

8 Discussion¶