Summary, Delegate Assembly, Fri Apr 28

Galaxy Cluster Finding for Lensed Explosive Transients with DP0.2
Dan Ryczanowski (30 min)

A presentation based on the paper “Enabling discovery of gravitationally lensed explosive transients: a new method to build an all-sky watch-list of groups and clusters of galaxies”, which includes an analysis of DP0.2 data. I will discuss the need for an all-sky catalogue of group and cluster-scale objects in order to facilitate and optimise detection of gravitationally-lensed transient events such as supernovae and kilonovae with Rubin, especially in its first years of operation. I will discuss the method developed to create such a catalogue, and how DP0 played an important role in the testing and validation stages of development.


Finding long-period Solar System or interstellar objects with machine learning in LSST
Antonio Vanzanella (15 min)

We developed a machine learning-based pipeline to detect slow objects (SMOs) in LSST images. We created a data-set of animations from DP0.1 images by using a collection of small cutouts around the same random coordinates but at different observation times and then we injected the slow-moving simulated object into the animation. The motion of the objects was created on the basis of the real ephemerides of Trans Neptunian Objects downloaded from the Jet Propulsion Laboratory (JPL) after some rescaling. Finally, we trained a 3D-CNN model on this dataset to discriminate among the animations in the data set whether SMO was present or not.

Antonio has provided a Jupyter Notebook that introduces the procedure of sample creation for a Dataset used to train and test a Machine learning pipeline able to detect simulated Slow-moving objects injected in the samples. Detecting-slow-moving-objs-LSST/script_campioni.ipynb at main · AntonioVanzanella/Detecting-slow-moving-objs-LSST · GitHub