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

    • COSMOLOGY
      • 1
        Hyeonmo's Report - Head-on Collsion of FDM/CDM Halos(POSTER)
        Speaker: Hyeonmo Koo (University of Seoul)
      • 2
        Young's report. FoF and MST algorithm
        Speaker: Young Ju
      • 3
        Hannah's Report

        See the slides from 8p

        Speaker: Hannah Jhee (University of Seoul)
      • 4
        Sumi's report
        Speaker: Sumi Kim (University of Seoul)
      • 5
        CLML

        Cosmology with Large scale structure using Machine Learning

        Speaker: Se Yeon Hwang
      • 6
        Dr.Sabiu's Report
        Speaker: Dr Cristiano Sabiu (University of Seoul)

        Deep Learning the Deep Sky: Recovering low surface brightness objects with machine learning

        • New candidates ...
        • Next:
          • We are planning to update the catalogue of low mass dwarf galaxies to create the most complete and comprehensive survey of the local universe
          • A dataset that will be useful not only for galaxy physics but maybe also cosmology
        • Testing Data Result
        • Detailed inspection
        • label: Predict class % (truth class)
        • Test Data Confusion Matrix
          • Predictions - Actuals graph
        • Locating the dwarf in the image
          • We can look into the network at any point.
          • Choosing one of the last convolution layers shows where in the image is highly activated.
          • We feed in an image to the trained network then look at the activation after the 4th convolution layer which has (8x8) spatial dimension.
            • Find weighted centre
            • Adjust to input image dimensions, overlay original
        • Self-Attention
          • This paper revolutionised the field of natural language processing (NLP) e.g. GPT3 as we showed many months ago can write code etc.
          • The transformer network introduced in this paper is now used in many cutting edge ML models not only in NLP but also computer vision (CV).
            • Reference - Attention Is All You Need
        • Vision Transformers
          • Building upon the powerful self attention transformers this paper shows a successful application to computer vision tasks like classification.
          • Cutting edge only a few years old
          • Outperforming the older convolutional neural network (CNN) models.
            • Reference - An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
    • 7
      Discussion