Please join us for the LINCC Tech Talk next week, Thursday, November 10, at 10h PT = 13h ET = 14h CLT = 19h CET on Zoom. We will discuss topics related to time-domain astronomy. Will Barnes will talk about Sunpy, and Andjelka Kovacevic will present the periodicity pipeline InKind contribution to LSST project. Find their abstracts below.
Events are also advertised at our web page and also provided in calendar form; and the #lincc-tech-talks LSSTC Slack channel is always available for discussions before, during, and after the talks
sunpy: A community-driven, open-source Python package for solar data analysis
The sunpy package is an openly-developed, community-driven Python package for solar data analysis. It is designed to provide the fundamental tools for accessing, loading, and interacting with solar physics data in Python. In particular, sunpy provides search and download functionality, data containers for image and time series data, as well as commonly used coordinate frames and transformations between such frames. In this talk, I will give an overview of the capabilities of the sunpy package and provide some examples of the kind of workflows that sunpy enables. Furthermore, I will provide a brief overview of the SunPy Project, which includes the core maintainers of the sunpy package as well as the interoperable software ecosystem surrounding sunpy.
SER-SAG Periodicity pipeline Inkind contribution: overview of Conditional Neural Process module for nonparametric light curve modeling
A. Kovačević, D. Ilić, V. Radović, R. Street, L. Č. Popović, M. Nikolić, Yan-Rong Li, Shiyuan He, N. Andrić Mitrović, S. Simić, I. Čvorović-Hajdinjak
Conditional Neural Processes (CNPs) were created as an expansion of Generative Query Networks (GQNs) in sense to extend GQN training regime to tasks such as regression and classification. Here we describe the various components of a CNP module which is important segment of our pipeline for periodicity mining, which is part of the SER-SAG in-kind contribution to the LSST. We contrasted CNP to an example of application of the Deep Gaussian process. This presentation is not intended to promote specific algorithms, but rather to demonstrate how our in-kind contribution team applies nonparametric modeling to AGN light curves in preparation for LSST data.
The SER-SAG team is currently experimenting with these algorithms and welcomes feedback from the LSST community.