Thunch

The goal of the Thunch seminar is to encourage the scientific interaction of the Princeton Astrophysics community. In addition, it should give everybody (students and postdocs in particular) a chance to present their research to a broad audience. Potential visiting speakers may contact the Thunch Czars to request a talk.

The talks are held on Thursdays at 12:15 pm, currently in Grand Central. They are rather informal, so leave time for questions throughout. Please try to keep your talk to about 50 minutes.

Most importantly, attendees are supposed to eat lunch throughout the talk!  Participants bring their own lunch; to honor tradition, you may wish to pick up a hoagie from Hoagie Haven.   

Please send the title and abstract of your talk a week prior to the Thunch Czars, Matt Sampson and Rachel Wang.

A few rules apply:

  • Priority goes to those who have not given a talk recently, and to postdocs and graduate students.
  • Visiting speakers must be explicitly invited by our department
  • There is, unfortunately, no money in Thunch to pay for travel, so visitors need another source of funding.

You now know everything about Thunch! So, go grab an empty slot for your chance to give a Thunch Talk this semester, and plan to join the group to learn about the fascinating research being done right next door!

Fall 2023 Upcoming Speakers:

 

Date Name and Institution Title Abstract
September 7th Andrew Saydjari (Harvard) Probabilistic Component Separation: Deconstructing Photometric and Spectroscopic Pipelines

A ubiquitous problem in astronomy is correctly assigning absorption/emission in an image/spectrum to the multiple processes occurring along the line of sight within the field of view. We introduce a method to decompose the images/spectra, with full posteriors on the joint distribution of the components.

The decomposition is obtained by modeling each component as a draw from a high-dimensional Gaussian distribution in the data-space (the observed image/spectrum)---a method we call “Marginalized Analytic Data-space Gaussian Inference for Component Separation” (MADGICS). This technique provides statistically rigorous uncertainties and detection thresholds, which allows better leveraging of low signal-to-noise data. 

I will discuss the application of these component separation techniques at scale to several of the largest photometric and spectroscopic surveys: the Dark Energy Camera Plane Survey (DECaPS2), Gaia Radial Velocity (RVS) spectra, and SDSS APOGEE spectra. We focus on applying these techniques in near-infrared wavelengths so as to penetrate through dust in the interstellar medium and map its spatial and chemical complexity at Galactic scales.
 

September 14th Samantha Wu (CalTech) How waves in stars can solve observational puzzles: from tides to pre-supernova outbursts From ground-based photometric surveys like the Zwicky Transient Facility (ZTF), to high-precision space-based photometry by Kepler and TESS, an influx of abundant astronomical data has generated open questions about the underlying processes driving many observational discoveries. On the theoretical end, improving our understanding of phenomena such as internal stellar oscillations has led to promising predictions for diverse systems ranging from stars hosting exoplanets, to massive stars undergoing supernovae and binary interaction. In this talk, I will describe how the dissipation of internal stellar oscillations in low-mass stars governs the orbital evolution of their planetary companions. Furthermore, I will discuss how excitation of waves from core convection in massive stars may underlie pre-supernova variation for massive stars, as well as the challenges this mechanism faces in producing the expected mass loss from such supernova progenitors. Nevertheless, I will demonstrate that the properties of circumstellar material (CSM) ejected due to binary interaction in stripped helium star systems are consistent with observations of several interacting supernovae.
September 21st Viraj Karambelkar (CalTech)    
September 28th Fan Zou (Penn State)    
October 5th Zhuhai Li (CalTech)    
October 12th Prof. Yue Shen (University of Illinois Urbana-Champaign)    
October 19th Mor Rozner (Israel Institute of Technology)    
October 26th

Shangjia Zhang  (University of Nevada)

   
November 2nd Teodor Grosu Astronomy Education Research, 2013-2023: A Literature Review In the United States, 200,000 students take an introductory astronomy course each year, and for some, it is the last science course they ever enroll in. This poses a unique challenge for instructors not only to provide students with essential astronomical knowledge, but also with scientific and analytical tools to navigate everyday life. This literature review is the first ever to focus entirely on undergraduate learning and instruction. It is also the first one to have a section dedicated to the confluence of astronomy education and scientific literacy, aside from education scholarship concerning exclusively the discipline of astronomy . It aims to aggregate the major topics in astronomy education research, with the findings attributed to each topic illustrating either essential developments in the field, or novel avenues of research that have contributed to expanding its breadth. This study uncovers general trends in the field in the past decade, such as an increased interest in cosmology instruction, and increased attention to visual learning, as well as gaps in literature, such as studies focused on experiences of students from underrepresented backgrounds, or on courses more advanced than the introductory one.
November 9th Hsiang-Chih Hwang (IAS)    
November 16th Kishore Patra (Berkeley)    
November 30th Princeton Graduate Students

Description
Short form talks by our own junior graduate students about rotation and/or other projects they have been working on. Come down to find out more about our students! 
 

 

Spring 2023:

February 16th: Chia-Yu Hu (University of Florida)
February 23rd: Dan Foreman-Mackey (CCA)
March 2nd: Lyla Jung (ANU)
March 16th: Vicente Valenzuela-Villaseca (Princeton University)
March 30th: Roohi Dalal (Princeton)
April 6th: David Velasco (Princeton)
April 13th: Zili Shen (Yale)
April 20th: Sabrina Appel (Rutgers)
May 4th: Ewine van Dishoeck Leiden (Leiden Observatory, the Netherlands)
May 11th: Charlotte Ward (Princeton University)

Fall 2022:

September 15th: Aritra Ghosh (Graduate Student, Yale University)
September 22nd: Thales Gutcke (NASA Hubble Fellow and Lyman Spitzer, Jr. Postdoctoral Fellow, Princeton)
September 29th: Fengwu Sun (Graduate Student, University of Arizona)
October 6th: Yubo Su (Lyman Spitzer Jr. Postdoctoral Fellow, Princeton)
October 13th: Yinhao Wu (Graduate Student, Leicester University)
October 20th: Fall break
October 27th: Lizhong Zhang (Graduate Student, University of California, Santa Barbara)
November 3rd: Oliver Zier (Graduate Student, Max Planck Institute for Astrophysics, Garching, Germany)
November 10th: Chang-Goo Kim (Post-Doctoral Associate Research Scholar, Princeton)
November 17th: Tsun Hin Navin Tsung (Graduate Student, University of California, Santa Barbara)
December 1st: Ore Gottlieb (Rothschild Fellow, CIERA Postdoctoral Fellow, Northwest University)
December 8th: Sihao Cheng (Postdoc Member at the Institute for Advanced Study)

Spring 2022:


February 3rd: Benjamin Crinquand (Post-Doctoral Associate Research Scholar, Princeton)
February 10th: Matthew Coleman (Post-Doctoral Associate Research Scholar, Princeton)
February 17th: Riddhi Bandyopadhyay (Post-Doctoral Associate Research Scholar, Princeton)
February 24th: Alex Gagliano (Pre-Doctoral Fellow, CCA Flatiron)
March 10th: Igor Andreoni (Postdoctoral Fellow, Joint Space-Science Institute)
March 17th: Sihao Cheng (Postdoctoral Fellow, Johns Hopkins University)
March 24th: Keith Hawkins (Assistant Professor, University of Texas at Austin)
March 31th: Frank van den Bosch (Professor of Theoretical Astrophysics, Yale)
April 7th: Mor Rozner (Graduate Student, Technion - Israel Institute of Technology)

April 14th: Sam Yee (Graduate Student, Princeton)

Click here for the latest schedule information

 

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