Exoclimes VII - ExoSLAM

ExoSLAM Summer School
3rd to 5th July, 2025, Université de Montréal, QC, CA

From light to knowledge: data reduction and analysis techniques for exoplanet atmosphere characterization

The ExoSLAM summer school will be held at Campus MIL at Université de Montréal on the Thursday, Friday, and Saturday morning (3-5 July) prior to Exoclimes VII. The summer school is aimed at students working towards their Master's or PhD in exoplanet science, as well as postdocs who are new to the field. Through a series of lectures and hands-on tutorials, ExoSLAM will offer participants a crash course in state-of-the-art methods to reduce, analyze, and interpret observations from space-based and ground-based telescopes. Furthermore, there will be lectures focusing on statistical methods as well as machine learning applications in astrophysics. A breakdown of the program can be found below:

Lecture 1: An overview of observing techniques to study exoplanet atmospheres
Lecture 2: Algorithms and statistical methods for exoplanet science
Lecture 3: An introduction to JWST data-reduction pipelines
Tutorial 1: Reducing JWST time-series observations
Lecture 4: From transmission to emission: radiative transfer in a nutshell
Lecture 5: An introduction to atmospheric retrieval methods
Lecture 6: Observing exoplanet atmospheres from the ground with high-resolution spectroscopy
Tutorial 2: Reducing and analysing high-resolution observations with the Starships framework
Panel Discussion: Navigating life as an early-career scientist
Lecture 7: An overview of machine-learning techniques in exoplanet science and astrophysics

ExoSLAM Schedule

Time	Thursdsay 3 July	Friday 4 July	Saturday 5 July
08:00 AM	Breakfast & Coffee	Breakfast & Coffee
09:00 AM	Welcome and Introduction Joost Wardenier	Lecture: Radiative transfer in a nutshell Érika Le Bourdais & Joost Wardenier	Breakfast & Coffee
09:15 AM	Welcome and Introduction Joost Wardenier
09:30 AM	Mini-lectures, part I: Transmission spectroscopy Emission spectroscopy and eclipse mapping Phase curves and reflected light Pierre-Alexis Roy, Louis-Phillipe Coulombe, & Jared Splinter
09:45 AM
10:00 AM		Lecture: Introduction to atmospheric retrievals, part I Caroline Piaulet-Ghorayeb & Luis Welbanks	Mini-lectures, part I Introduction to machine learning Machine learning for atmospheric retrievals Inference problems in gravitational lensing Salma Salhi, Chloe Fisher, & Ronan Legin
10:15 AM	Buffer for questions and discussion
10:30 AM	Coffee break	Coffee break
11:00 AM	Mini-lectures, part II Direct imaging Single-line observations and atmospheric escape Polluted white dwarfs Clémence Fontanive, Romain Allart, & Érika Le Bourdais	Lecture: Introduction to atmospheric retrievals, part II Caroline Piaulet-Ghorayeb & Luis Welbanks	Coffee break
11:15 AM			Coffee break
11:30 AM		Buffer for questions and discussion	Mini-lectures, part II Machine learning for JWST noise correction Machine learning for interior modelling Salma Salhi & Vanesa Ramirez
11:45 AM	Lecture: Algorithms and statistical methods for exoplanet studies Thomas Vandal	Lecture: High-resolution spectroscopy Stevanus Nugroho & Stefan Pelletier
12:00 PM
12:15 PM			Buffer for questions and discussion
12:30 PM			Closing remarks Joost Wardenier
12:45 PM	Buffer for questions and discussion	Buffer for questions and discussion
13:00 PM	Lunch break	Lunch break
14:15 PM	Lecture: JWST data reduction Michael Radica & Taylor Bell	Tutorial: High-resolution retrievals with the Starships analysis framework (90 min) Georgia Mraz, Olivia Pereira, Mathis Bouffard, Romain Allart, & Joost Wardenier
14:30 PM
14:45 PM
15:00 PM
15:15 PM	Tutorial: JWST data reduction (30 min) Michael Radica & Taylor Bell
15:30 PM
15:45 PM	Coffee break	Coffee break
16:15 PM	Tutorial: JWST data reduction (90 min) Michael Radica & Taylor Bell	Tutorial: High-resolution retrievals with the Starships analysis framework (30 min) Starships team
16:30 PM
16:45 PM		Panel discussion: Navigating life as an early career scientist Lisa Dang, Taylor Bell, Chloe Fisher & Stevanus Nugroho
17:00 PM
17:15 PM
17:30 PM
18:30 PM	Dinner & Fun	Dinner & Fun