Abstract Number:
1528
Submission Type:
Topic-Contributed Paper Session
Participants:
Sara Algeri (1), Vinay Kashyap (3) (,4), David Stenning (2), Gwendolyn Eadie (5), Xiangyu Zhang (6), Purvasha Chakravarti (7), Hu Sun (6)
Institutions:
(1) University of Minnesota, N/A, (2) Simon Fraser University, N/A, (3) Center for Astrophysics , N/A, (4) Harvard & Smithsonian, N/A, (5) University of Toronto, N/A, (6) N/A, N/A, (7) University College London, N/A
Chair:
Discussant:
Session Organizer:
Speaker(s):
Session Description:
DESPRIPTION: The data generated from large astronomical surveys and cutting-edge experiments in particle physics have revealed the vital role of statistics in conducting reliable and reproducible statistical analyses, i.e., minimizing the risk of false discoveries while maximizing the power of the detection tools adopted. In this setting, a crucial stage of the analysis of astrophysical data is that of assessing the presence of new signals. Given the complexity and the lack of regularity of the models under study, however, classical statistical tools are typically not applicable. As a result, by developing robust statistical solutions to address this problem, statisticians have the unprecedented opportunity to contribute to groundbreaking discoveries in physics and astronomy. While focusing on the novel data-driven frontiers of the physical sciences, this session aims to (i) stimulate the interest of the audience by showing how important astrophysical questions often translate into fundamental statistical questions, (ii) describe some of the most recent solutions proposed in the literature, and (iii) motivate new research developments in the intersection between statistics and the physical sciences while overcoming cross-disciplinary language barriers.
FORMAT: 4 speakers (20 mins talk + 2 mins Q&A) + 1 discussant (15 mins talk + 2 mins Q&A) and a chair.
DEMOGRAPHICS: The pool of participants (speakers+discussant+chair+organizer) includes 3 females, 4 males with affiliations in 3 different countries (United States, Canada, and UK) and from 5 different countries of origin (United States, Canada, China, India, and Italy).
SPEAKER 1: Gwendolyn Eadie is an Assitant Professor of Astrostatistics at the University of Toronto. Gwen has a background in astrophysics and is well-known within the astrostatistics community thanks to her extensive expertise on Hierarchical Bayesian modelling for complex data.
Title: Stellar Flares in Hiding: using hidden Markov models to find stellar flares in time series data from TESS
SPEAKER 2: Xiangyu Zhang is a PhD candidate in Statistics at the University of Minnesota. Despite the early stage of his academic career, he has substantial experience in collaborating with both astronomers and astrophysicists on different projects.
Title: A distribution-free approach to testing astrophysical models for angular power spectra
SPEAKER 3: Purvasha Chakravarti is a Lecturer (Assistant Professor with tenure) at the University of College London. Purvasha is an expert in machine learning and has developed several machine learning solutions capable to directly address problems arising in particle physics.
Title: Signal Detection in Particle Physics using a Classifier Decorrelated through Optimal Transport
SPEAKER 4: Hu Sun is a Ph.D. candidate in Statistics at the University of Michigan. Hu expertise lies in tensor data model, spatio-temporal statistics and applications in astrophysics and geophysics data.
Title: Statistical Methods for Interpretable and Trustworthy Solar Flare Prediction
DISCUSSANT: Vinay Kashyap is an astrophysicist at the Harvard-Smithsonian Center for Astrophysics and a active member of the ASA Astrostatistics Interest Group. He has extensive experience in astrostatistics and has collaborated with several world-class statisticians throughout his career.
Sponsors:
Astrostatistics Interest Group 3
Section on Physical and Engineering Sciences 1
Section on Statistical Computing 2
Theme:
Statistics and Data Science: Informing Policy and Countering Misinformation
Yes
Applied
Yes
Estimated Audience Size
Small (<80)
I have read and understand that JSM participants must abide by the Participant Guidelines.
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I understand and have communicated to my proposed speakers that JSM participants must register and pay the appropriate registration fee by June 1, 2024. The registration fee is nonrefundable.
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