CPLUOS Astrophysics team meeting

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

물리학과

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

14:00 → 15:00 COSMOLOGY¶

14:00 Hyeonmo's Report¶

Speaker: Hyeonmo Koo (University of Seoul)

Head-on Collision Fuzzy/Cold Dark Matter Halos

• Made a dark matter halo plot.
• Galic code → No cut-off
• Dark matter halo → Total angular momentum = 0

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

Speaker: Young Ju

FoF and MST algorithm

• No progress

14:20 Hannah's Report¶

Speaker: Hannah Jhee (University of Seoul)

Halos infalling a cluster through filaments

• How many are halos going out from the filaments?
  • How exactly should we treat filament movements?
  • Negative v_perp during > 95% lifetime
  • r_perp [Mpc]-v_perp [km/s]
    • Reasonable halos
    • Unreasonable halos
      • Shouldn't go this way (lower v_perp) in the phase-space.
    • v_perp: Not the relative velocity
    • Color: Snapshot number
• Filament treatment
  • 2-d projection along z-axis
  • 2-d slice of 1 Mpc thickness along z-axis
• We study motion of the filaments around halos.
• Goal: Approaching to secret of galaxy evolution.

14:30 Seyeon's Report - CLML¶

Speaker: Se Yeon Hwang (Universe of Seoul)

Cosmology with Large scale structure using Machine Learning (CLML)

• Simulation is done.
• Get galaxies catalog-HOD pipeline.
• Goal: ra, dec, observed redshift of galaxies
• Single dark matter halo = Set center galaxy with same position and velocity with dark matter halo.
  • Mass [M_sun]-N_avg
  • x-axis: Mass of dark matter halo
  • y-axis: Average number of galaxies at each mass
  • This slope depends on parameters.
  • Dot: This dot is the galaxy.
• Check finger of god effect in redshift space with galaxies catalog.
  • M ~ 10¹³ or M ~ 10¹⁵
• Future work
  • Apply this Halo Occupation Distribution (HOD) pipeline to dark matter halo catalog.

14:40 Sumi's report¶

Speaker: Sumi Kim (University of Seoul)

Cosmology of High-Order Statistics (CHOA)

• No progress

14:50 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

15:00 → 15:10 Discussion¶