CPLUOS Astrophysics team meeting

Monday 6 Sept 2021, 16:30 → 17:55 Asia/Seoul

물리학과

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

16:30 → 17:45 COSMOLOGY¶

16:30 Dr.Sangnam Park's Report - FDM_Offset¶

Speaker: Sangnam Park

16:45 Hyeonmo's report - CDM Halo Evolution for Gadget2 Simulation¶

Speaker: Hyeonmo Koo (University of Seoul)

• Halo collision simulation
  • Previous : M[4π x 10^7M_s] = 20~50, v[28.1km/s] = 18 ~ 25
  • Next : M[4π x 10^7M_s] = 50, v[28.1km/s] = 50(18 ~ 25)/M
• Initial velocity[km/s] - Decreased velocity[km/s] graph
  • Dashed : Newtonian result ; right upper
  • Dotted : Recent result(+PBC) ; left lower
• t[Gadget unit] - r_(1/2) Evolution[kpc] graph
  • t = 0 : All simulations of 30 halo samples are initiated from here!!
  • t = 0 ~ (about) t = 0.07 : Initiate from all times before one period of oscillation

17:00 Young's report. FoF and MST algorithm¶

Speaker: Young Ju

17:15 CHOA(Cosmology of High-Order Statistics)¶

Speaker: Se Yeon Hwang

• Outline
  • Chi square
  • Joint chi square
  • Introduce new simulation, Abacus
• Full range & cut range
  • Multiplied rescaling factor to 2-point correlation function
  • Ordinary(Rescaling factor = 1.0)
    • Range : Full range(35 < r < 135)
    • No wiggle : 8.75
    • With wiggle : 7.97
  • Rescaling factor = 0.7
    • Range : Full range(35 < r < 135)
    • No wiggle : 2.94
    • With wiggle : 2.05
  • Rescaling factor = 0.8 and 0.9
    • Range : Cut range(60 < r < 135)
    • No wiggle : 3.31
    • With wiggle : 1.89
• Full range & cut range
  • Multiplied rescaling factor to 3-point correlation function
  • Ordinary(Rescaling factor = 1.0)
    • Range : Full range(35 < r < 135)
    • No wiggle : 6.17
    • With wiggle : 4.43
  • Rescaling factor = 0.75 and 0.7
    • Range : Full range(35 < r < 135)
    • No wiggle : 2.91
    • With wiggle : 1.49
  • Rescaling factor = 0.8 and 0.65
    • Range : Cut range(60 < r < 135)
    • No wiggle : 3.27
    • With wiggle : 1.57

• Full range & cut range
  • Multiplied rescaling factor to 5-point correlation function
  • Ordinary(Rescaling factor = 1.0)
    • Range : Full range(35 < r < 135)
    • No wiggle : 8.04
    • With wiggle : 6.92
  • Rescaling factor = 0.35 and 0.35
    • Range : Full range(35 < r < 135)
    • No wiggle : 0.23
    • With wiggle : 0.08
  • Rescaling factor = 0.35 and 0.3
    • Range : Cut range(60 < r < 135)
    • No wiggle : 0.23
    • With wiggle : 0.07
• Calculated joint chi square using combined 2pcf + 3pcf covariance matrix
  • Full range(35 < r < 135), nw chi : 7.37, ww chi : 6.63
  • Cut range(60 < r < 135), nw chi : 3.95, ww chi : 3.01
• Compare with different simulation : Abacus
  • Pinocchio : Lagrangian perturbation theory based simulation
  • Abacus : N-body simulation
  • Box size = 1100(Mpc/h)
    • (Parameter, Value)
      • (ombh^2, 0.02222)
      • (omcdmh^2, 0.1199)
      • (omh^2, 0.14212)

17:30 Dr.Sabiu's Report - Higher Order Statistics¶

Speaker: Dr Cristiano Sabiu (University of Seoul)

• Estimating the General N-point Statistics
  • Use tree-code to find all data points close to 1
  • Do this for each data point
  • Creating a huge database of pairs
  • It's quite fast to create, but memory intensive
  • Query for N-point
    • All 'pairs' (w/ distances) are precomputed.
    • N-point exact counting.
    • I.e. No approximation
    • The majority of the time is taken in binning in high-dimension.
  • Create a graph-database
    • Finding the disconnected distances
    • (Distance, Unique ID)
      • (14, 3675)
      • (17, 620)
      • (13, 342)
      • Find distance(gal 1, gal 3675)
    • (Distance, Unique ID)
      • (13, 342)
      • (17, 620)
      • (14, 3675) → Bisecting
      • Python 'where' is slow
      • Fortran loops are ok, not optimized

17:45 → 17:55 Discussion¶