CPLUOS Astrophysics team meeting

Friday 15 Jul 2022, 14:00 → 15:10 Asia/Seoul

물리학과

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

14:00 → 14:50 COSMOLOGY

14:00 Hyeonmo's Report

Speaker: Hyeonmo Koo (University of Seoul)

Brief Introduction of the Calculation inside GADGET2 Simulation & Plotting

• Calculate the density of particles.

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

Speaker: Young Ju

FoF and MST Algorithm

• Change density calculation method: Voronoi (recalculate).
  • Left figure
    • Results from agglomerated data
  • Right figure
    • Results from dispersed data
  • Number difference
    • Original
    • Voronoi
      • Voronoi is justifiable → No kink
  • How to calculate density?
    • Divide all points by equal volume. And take it as the reciprocal to find it.
    • Density calculation is performed by applying MGS sequentially from the highest density to the lowest density.

14:20 Hannah's Report

See the slides from 8p

Speaker: Hannah Jhee (University of Seoul)

Axionyx: CDM Mixed Simulation

• Initial configuration of CDM particles
  • 1. Generate random radial distribution.
    • Plummer profile with mass of 0.05 M_tot
    • Use @ njit, 11.9 s becomes 0.5 s.
  • 2. Determine rest of the position components.
  • 3. Generate random velocities.
    • No radial velocities (only orbital = sqrt(GM(r)/r))
    • First randomly determine vtheta, and then vphi.
    • Transform to cartesian velocity components.
  • 4. Write as an ascii file (input for the sim).
  • r  - ρ(r) graph
    • Generated
    • Theoretical
• Modification of code due to optimization
• Uniform random distribution → Theta is wrong. → Because theta is random. → Get hints from (x, y, z) code.
• Probability of radius r → ρ x r²
• Non-homogeneous → Consider theta!
• Set number of particles. → The error occurred!

14:30 Sumi's report

Speaker: Sumi Kim (University of Seoul)

Plan for IAU Poster

• The data is only occupied by the high σ₈. → Error in calculation

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