CPLUOS Astrophysics team meeting

Friday 18 Mar 2022, 14:00 → 15:10 Asia/Seoul

물리학과

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

14:00 → 15:00 COSMOLOGY

14:00 Hyeonmo's Report - Head-on Collsion of FDM/CDM Halos(Writing Paper)

Speaker: Hyeonmo Koo (University of Seoul)

Head-on Collsion of FDM/CDM Halos(Writing Paper)

• Figure 1 : Snapshots of FDM halo collision for v_i = 112.776km/s and v_i = 225.552km/s. For the last two snapshots of each v_i, the COM of each halos should be located on the dotted line if any other interactions are excluded.
• The comparison of two figure in FDM and CDM

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

Speaker: Young Ju

Mulguisin Clustering Algorithm 1

Comparison of Clustering Algorithms for Study of Cosmic Structure Finding

• According to step 5, schematic idea of MGS
• MGS performance test
  • Ratio = (returned member)/(original member)
  • For MST, there is no link with distance larger than about 10. So even if I increase the linking-length, the number of cluster does not change.
  • MST → flat → because of algorithm
• There is fault
  • Check the hierarchical clustering and DBSCAN again
  • Hierarchical clustering : there are two criteria
  • 1. Single : calculate euclidean distance
  • 2. Ward : calculate variance of distance
  • Single : distance_threshold = 1.0
  • Ward : distance_threshold = 1.0
  • DBSCAN : eps = 1.0, min_sample = 3
  • When min_sample is very small, the result of DBSCAN return the same result of hierarchical

14:20 Hannah's Report

Speaker: Hannah Jhee (University of Seoul)

Filament

• DisPerSE : from Morse Theory
• T-ReX : from MLE on MST
• Running DisPerSE
• Create 2D Mock Data
• Create 3D Mock Data

14:30 CHOA(Cosmology of High-Order Statistics)

Speaker: Sumi Kim (University of Seoul)

14:40 CLML

Cosmology with Large scale structure using Machine Learning

Speaker: Se Yeon Hwang

Cosmology with Large scale structure using Machine Learning(CLML)

• Preparing for the KAS
• DATA
• Basic model
• DATA normalization
• How to get this contour?
  • Ω_m = 0.3072
  • σ_8 = 0.8288
• Epoch : 200, learning rate = 0.0005
  • Predict one : use same model & some input data, but the output is only one.(Ω_m or σ_8)

14:50 Dr.Sabiu's Report - N-PCF

Speaker: Dr Cristiano Sabiu (University of Seoul)

15:00 → 15:10 Discussion