CPLUOS Astrophysics team meeting

Friday 10 Jun 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¶

Speaker: Hyeonmo Koo (University of Seoul)

Head-on Collsion of FDM/CDM Halos

• No progress

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

Speaker: Young Ju

FoF and MST algorithm

• No progress

14:20 Hannah's Report¶

See the slides from 8p

Speaker: Hannah Jhee (University of Seoul)

Nyx: A Massively Parallel AMR Code for Computational Cosmology - AxioNyx

• No progress

14:30 Sumi's report¶

Speaker: Sumi Kim (University of Seoul)

Calculating Bright Galaxy Survey (BGS) 2pcf

• DA02 BGS EZmocks 2pcf I = 0, Nran = 20, used W_fkp calculation
  • s [Mpc/h] - ξ₂ (s)s² [Mpc/h]² graph
    • 0.1 < z < 0.5
    • 0.1 < z < 0.2
    • 0.2 < z < 0.3
    • 0.3 < z < 0.4 is bigger than other things
    • 0.4 < z < 0.5
    • Plot → Strange!
  • W_FKP: FKP weight
    • Feldman, Kaiser, and Peacock
  • Function of number density, to optimize the clustering measurements facing shot noise and cosmic variance.

14:40 CLML¶

Cosmology with Large scale structure using Machine Learning

Speaker: Se Yeon Hwang

Cosmology with Large scale structure using Machine Learning (CLML)

• Outline
  • We are checking the light cone simulation with different cosmological parameters.
    • Snapshot
    • Light cone
• Light cone
  • For the light cone, we can get set redshift range.
  • There are start redshift and final redshift (in our case z_{f = 0.0}).
  • Depending on start redshift, there is a limitation of box size. If our box is smaller than that redshift distance, the simulation box will be replicate to compensate that range which undesirable case for us.
  • (Start redshift, Minimum box size (Mpc)
    • (0.8, 2000)
    • (0.5, 1500)
    • (0.3, 1000)
• Reference parameter
  • Abacas (Nbody-simulation) default parameter
  • Power spectrum is snapshot at z = 0.0
  • (Ω_m, Ω_b, Ω_cdm, σ₈, w₀, w_a, h, n_s)
    • (0.3133, 0.0493, 0.264, 0.8079, -1, 0, 0.6736, 0.9649)
  • Abacus power spectrum
    • k - P(k) graph
• Cosmological parameter selection: Latin hypercube sampling (LHS)
  • Parameter space
    • Ω_{m -} σ₈ graph
    • Previous: Grid (2 parameters)
  • Latin hypercube sampling
    • The points did not overlap! → Because it is random!
• Using LHS, We picked 6 parameters, run each simulations and drew power spectrum
  • (Omega_m, sigma_8, w0, wa, n_s, h)
  • (0, 1, 2, 3, 4)
  • k - P(k) graph
    • k is very broad!
    • k - P(k) graph is narrow!
• We made 1000 parameter like this and started running simulation on pax3
• We made 6 plots as machine learning data!
• If we use box size = 2000 (Mpc/h) with 1024³ and z_start = 0.8, it will take 3 weeks to make all. So we changed box size = 1000 (Mpc/h) with 512³ and z_start = 0.3, and it will take a week to run

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

• Missing Satellite Problem
  • Around 38 dwarf galaxies have been observed in the Local Group, and only around 11 orbiting the Milky Way, yet dark matter simulations predict that there should be around 500 dwarf satellites for the Milky Way alone.
• Potential Resolutions:
  • Smaller-sized clumps of dark matter may be unable to obtain or retain the baryonic matter needed to form stars in the first place.
  • After they form, dwarf galaxies may be quickly "eaten" by the larger galaxies that they orbit.
  • Non-standard DM models could modify the expected number of low mass halos.
• How to proceed? We need data!
• Optical Sky Surveys
  • Typically detect compact bright sources.
    • Stars, Galaxies, QSO
  • Surveys telescope optics are not optimised for faint and extended objects.
  • We see the tops of the mountains.
  • But small hills deep in the valleys are difficult to detect.
  • These small hills are where the dwarf galaxies lie.
• But wait a moment...
  • Predictions - Actuals graph
    • dE - Randon
      • 63
        • This box is telling us how many random sky boxes were predicted to have a dE.
        • We did not check to see if the random sky patch has dE - they could have!
        • Let's visually inspect some new candidates...
• Performance Metrics
  • Accuracy = (T_dE+T_sky)/N_images
  • Precision = T_dE/(T_dE+F_dE)
  • Recall = T_dE/(T_dE+F_sky)
  • Value accuracy
  • Value precision
  • Value recall
  • Number of false positives

15:00 → 15:10 Discussion¶