I’m very happy to announce a new monthly LINCC Tech Talks series.
Delivered virtually every second Thursday of the month at 10am Pacific (1pm ET, 2pm Chile, 7pm CET) these talks and demos will showcase work done by the broad Rubin software and archives community that’s designed to enable LSST science. We hope it will provide a forum for a range of groups and authors to present, learn about, and discuss efforts of interest to analysis of LSST data. The talks will be recorded and made publicly available.
Current talk is:
When: Thursday, November 10th, 10am Pacific
Topic: sunpy: A community-driven, open-source Python package for solar data analysis" (Will Barnes)
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.
Topic: SER-SAG Periodicity pipeline Inkind contribution: overview of Conditional Neural Process module for nonparametric light curve modeling (Andjelka Kovacevic, 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.
Each talk is to be followed by lots of time for Q&A and discussions.
We’re looking forward to assembling the speaker list for the next few months. The tentative schedule for next few months includes:
- December 8: The ALeRCE broker (Franciso Forster)
- January 12: IDACs, Archives, and Data Access (speakers TBD)
- February 9: The TOM Toolkit: Observing platform for Rubin follow-up and recent upgrades for O4 (Rachel Street, William Lindstrom, Joey Chatelain)
If you wish to receive future seminar announcements, add the following calendar, subscribe to this discussion, the #lincc-tech-talks channel on LSSTC Slack, or the LINCC announcements mailing list (send a blank e-mail to firstname.lastname@example.org).