CPLUOS Astrophysics team meeting

Asia/Seoul
물리학과

물리학과

Zoom Meeting https://uos-ac-kr.zoom.us/j/8264902652 Meeting ID: 826 490 2652
Description

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

    • 16:30 17:45
      COSMOLOGY
      • 16:30
        Dr.Sangnam Park's Report - FDM_Offset 15m
        Speaker: Sangnam Park
        • Corotation of Satellites
          • Initial ratio between number of satellites with initial velocity v^+_0 and v^-_0 = 3:1
          • CDM : 0.25 → 0.1934 ± 0.3560
          • FDM : 0.25 → 0.0664 ± 0.0526
      • 16:45
        Hyeonmo's report - CDM Halo Evolution for Gadget2 Simulation 15m
        Speaker: Hyeonmo Koo (University of Seoul)
      • 17:00
        Young's report. FoF and MST algorithm 15m
        Speaker: Young Ju
        • Test MGS, MST in Noise data 4
          • Added 3000, 4000 noise galaxies in the random data set.
          • Check the number of cluster
          • Cluster with more than 3 member
            • 3000 noise galaxies ; MST : 273 / MGS : 344
            • 4000 noise galaxies ; MST : 306 / MGS : 344
          • Cluster with more than 10 member
            • 3000 noise galaxies ; MST : 63 / MGS : 63
            • 4000 noise galaxies ; MST : 79 / MGS : 63
          • None noise data
            • MST : 44 / MGS : 47
          • Limit the number of member, the number of cluster is higher than none noise data results.
          • In MGS, 10 clusters and MST, 10 clusters, only pick up largest 10 cluster, there is evident difference.

         

      • 17:15
        CHOA(Cosmology of High-Order Statistics) 15m
        Speaker: Sumi Kim (University of Seoul)
        • Compare Abacus Real & Pinocchio with Observations
          • To compare which simulation more fits with data
          • Abacus Parameters
            • Om = 0.3142
            • Ode = 0.6858
            • w = -1
            • Z_b = 0.5
            • Box size = 1100
            • H0 = 67.62
          • In Abacus & Observation 2pcf Comparison,
            • About 100r(Mpc/h) : Observation amplitude is bigger than abacus
            • About 30r(Mpc/h) : Fits with RSD in lowscale
      • 17:30
        Dr.Sabiu's Report - Higher Order Statistics 15m
        Speaker: Dr Cristiano Sabiu (University of Seoul)
        • Estimating the General N-point Statistics
          • 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.
            • N(r_(1-2)) = Fast binning! Combinitronics(we know all the combinations!)
            • N(r_(3-4)) = Slower binning Need to search the database
          • Graph Query (Disconnected Part)
            • To bin the N-point measurement we need the distance between disconnected galaxies (point).
            • Database Entry for Galaxy 265
              • (Distance, Unique ID)
                • (13, 342)
                • (17, 620)
                • (14, 3675) → New method :  Divide-and-Conquer
                • (..., ...)
                • (7, 13465)
            • Generative Pre-trained Transformer 3 (GPT-3) is an autoregressive language model that uses deep learning  to produce human-like text.
            • GPT-3's full version has a capacity of 175 billion machine learning parameters (10x more than any previous model).
            • Back to the N-points : How much faster can we make this?
              • When the number of data points reaches 500000, the speed of new is about twice as fast as the old.
    • 17:45 17:55
      Discussion 10m