Session-27

The twenty-seventh session of the LSST-DA Data Science Fellowship Program focuses on time-series analysis.

Guest instructors for Session 27 are:

• Dr. Luke Bouma, Caltech | IPAC
• Dr. Helen Qu, Flatiron Institute | CCA
• Dr. Sabina Sagynbayeva, McGill

Lectures were also delivered by the DSFP leadership team:

• Dr. Adam Miller, Northwestern | CIERA
• Dr. James Johnson, CIERA

Schedule

Day 1 – Foundations of Time-Series Analysis

Monday, November 16

• 08:30 AM – 09:00 AM o Registration
• 09:00 AM – 09:30 AM o Welcome to Session 27
• 09:30 AM – 10:15 AM o Lecture I: An Introduction to Time-Series Analysis; A Miller
• 10:15 AM – 10:45 AM o BREAK
• 10:45 AM – 12:00 PM o Problem I: Exploring and Characterizing Time-Series Data; A Miller
• 12:00 PM – 01:30 PM o LUNCH
• 01:30 PM – 02:15 PM o Lecture II: Introduction to Fourier Methods; J Johnson
• 02:15 PM – 03:30 PM o Problem II: Fourier Methods in Practice; J Johnson
• 03:30 PM – 04:00 PM o BREAK
• 04:00 PM – 04:45 PM o Lecture III: Finding Periodicity in Unevenly Sampled Data with Lomb-Scargle; A Miller
• 04:45 PM – 06:00 PM o Problem III: Lomb-Scargle Periodograms; A Miller

Day 2 – Signals, Noise, and Models

Tuesday, November 17

• 09:00 AM – 10:00 AM o Lecture IV: Matched Filtering: Finding Signals in Noise; L Bouma
• 10:00 AM – 11:00 AM o Problem IV: Matched Filtering; L Bouma
• 11:00 AM – 11:30 AM o BREAK
• 11:30 AM – 12:00 PM o Problem IV (continued); L Bouma
• 12:00 PM – 01:30 PM o LUNCH
• 01:30 PM – 02:15 PM o Lecture V: Fourier Methods II: Spectral Estimation, Windowing, and Filtering; J Johnson
• 02:15 PM – 03:30 PM o Problem V: Spectral Estimation and Filtering; J Johnson
• 03:30 PM – 04:00 PM o BREAK
• 04:00 PM – 04:45 PM o Lecture VI: Introduction to Gaussian Processes; S Sagynbayeva
• 04:45 PM – 06:00 PM o Problem VI: Gaussian Process Regression; S Sagynbayeva

Day 3 – Gaussian Processes at Scale

Wednesday, Nov 18

• 09:00 AM – 10:0 AM o Lecture VII: Advanced Gaussian Processes: Making GPs Work at Scale; S Sagynbayeva
• 10:00 AM – 10:30 AM o BREAK
• 10:30 AM – 12:00 PM o Problem VII: Scalable Gaussian Processes; S Sagynbayeva
• 12:00 PM – ??:?? PM o BREAK

Day 4 – From Pixels to Predictions

Thursday, Nov 19

• 09:00 AM – 09:45 AM o Lecture VIII: From Pixels to Light Curves; L Bouma
• 09:45 AM – 10:15 AM o BREAK
• 10:15 AM – 12:00 PM o Problem VIII: Building Light Curves from Pixels; L Bouma
• 12:00 PM – 01:30 PM o LUNCH
• 01:30 PM – 02:30 PM o Lecture IX: Machine Learning for Time-Series Analysis; H Qu
• 02:30 PM – 04:00 PM o Problem IX: Machine Learning for Time-Series Data; H Qu
• 04:00 PM – 04:30 PM o BREAK
• 04:30 PM – 05:30 PM o Hack Pitch

Day 5 – AI in Practice and Hack Day

Friday, Nov 20

• 09:00 AM – 10:00 AM o Lecture X: AI in Practice; H Qu
• 10:00 AM – 10:30 AM o BREAK
• 10:30 AM – 12:00 PM o Problem X: Applying AI to Astronomical Data in Practice; H Qu
• 12:00 PM – 01:00 PM o Panel Lunch: What Is the Role of Astronomers in the Agentic AI Era?
• 01:00 PM – 04:30 PM o Hack Session
• 04:30 PM – 05:00 PM o Hack Sharebacks and Meeting Wrap Up