CPLUOS Astrophysics team meeting

Friday 13 Jan 2023, 14:00 → 15:20 Asia/Seoul

물리학과

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

14:00 → 14:50 COSMOLOGY

14:00 Hyeonmo's Report_Planar System Generation

Speaker: Hyeonmo Koo (University of Seoul)

20230113.pptx

Planar System Generation

• Refer to "Analyzing Planar Galactic Halo Distributions with Fuzzy/Cold Dark Matter Models.
• Each Planar System has a quantity of the following.
  • Number of Halo(Hernquist Profile Seed): 100
  • Each mass of Halo(Seed): 𝜋×10⁸𝑀⊙
  • Profile of System: Plummer Profile 𝜌𝑟 = (3𝑀₀/4𝜋𝑎³)(1+r²/a²)^(-5/2)
  • Half-Light Radius of System: 𝑎 = 13.6 kpc
  • Radius Cut of System: 𝑅_max  = 40.8 kpc
  • 𝑣_ini  = 𝑞₁𝑣_P+𝑞₂𝑣±
    • q₁  = randomness
  • Comparison of CDM and FDM plots
• Next work
  • Find ellipticity and planarity of satellites.
  • Colliding two planar system.

14:10 Young's report. FoF and MST algorithm

Speaker: Young Ju

ppt_230112_young.pdf

FoF and MST algorithm

• We submitted the paper to MNRAS.
• Also, we can see the paper on arxiv: arXiv:2301.03278
• Our plans are the followng.
  • Improve MGS code.
  • Find more topological variables.
  • Parameter constraint  using MGS
• 1. Increase speed of calculation.
  • We use MPI and increase speed of density calculation.
  • Normal way: ~110 seconds
  • MPI using 6 processes (laptop): ~25 seconds
• 2. For test, We made topological variables with size of cluster.

14:20 Hannah's Report

Speaker: Hannah Jhee (University of Seoul)

Halos infalling a cluster through filaments

• No progress

14:30 Seyeon's Report - CLML

Speaker: Se Yeon Hwang (Universe of Seoul)

Cosmology with Large scale structure using Machine Learning (CLML)

• Preview
  • CNN result
• These are the plots from CNN and ViT.
  • From CNN
  • From ViT
• They predicted gravitational lensing parameter.
  • Blue: CNN (ResNet)
  • Orange: ViT
• What we are doing now: We are trying to use pre-trained 3-D ViT so that can make any differences with previous result.

14:40 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

14:50 → 15:00 Discussion