Announcing the LINCC Tech Talks series

Hi everyone,

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.

We have started off the series with a talk by the LINCC Frameworks team and the first recording can be found at our channel here.

Current talk is:

When: Thursday, November 10th, 10am Pacific

Zoom: https://ls.st/lincc-talks

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’re interested in presenting your work, or have any questions, please contact the tech talk series moderators Neven Caplar ncaplar@uw.edu and Alex Malz aimalz@cmu.edu.

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 lincc-join@lists.lsst.org).

On behalf of the LINCC team, @mjuric, @nevencaplar & @aimalz .

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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.

thank you very much

Please join us for the LINCC Tech Talk next week, Thursday, December 8, at 10h PT = 13h ET = 14h CLT = 19h CET on Zoom . We will hear from Francisco Förster Burón, who will present the ALeRCE broker. Find the abstract 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

The ALeRCE broker: machine learning enabled processing of astronomical alert streams
A new generation of large aperture and large field of view telescopes is allowing the exploration of large volumes of the Universe in an unprecedented fashion. In order to take advantage of these new telescopes, notably the Vera C. Rubin Observatory, a new time domain ecosystem is developing. Among the tools required are fast machine learning aided discovery and classification algorithms, interoperable tools to allow for an effective communication with the community and follow-up telescopes, and new models and tools to extract the most physical knowledge from these observations. In this talk I will review the challenges and progress of building one of these systems: the Automatic Learning for the Rapid Classification of Events (ALeRCE) astronomical alert broker. ALeRCE (http://alerce.science/) is an alert annotation and classification system led by an interdisciplinary and interinstitutional group of scientists from Chile since 2019. ALeRCE is focused around three scientific cases: transients, variable stars and active galactic nuclei. Thanks to its state-of-the-art machine learning models, ALeRCE has become the 2nd group to report most transient candidates to the Transient Name Server, and it is enabling new science with different astrophysical objects, e.g. AGN science. I will discuss some of the challenges associated with the problem of alert classification, including the ingestion of multiple alert streams, annotation, database management, training set building, feature computation and distributed processing, machine learning classification and visualization, or the challenges of working in large interdisciplinary teams. I will also show some results based on the real‐time ingestion and classification using the Zwicky Transient Facility (ZTF) alert stream as input, as well as some of the tools available.