Abstract Number:
1814
Submission Type:
Topic-Contributed Paper Session
Participants:
Vinay Kashyap (1) (,2), Aneta Siemiginowska (4), Yang Chen (3), David Stenning (5), Ashish Mahabal (6), Daniela Huppenkothen (7), Ashley Villar (8), Giovanni Motta (9)
Institutions:
(1) Center for Astrophysics , N/A, (2) Harvard & Smithsonian, N/A, (3) University of Michigan, N/A, (4) Harvard-Smithsonian Center for Astrophysics, N/A, (5) Simon Fraser University, N/A, (6) Caltech, N/A, (7) N/A, N/A, (8) Harvard University, Cambridge, MA, (9) Columbia University, N/A
Chair:
Co-Organizer:
Session Organizer:
Speaker(s):
Session Description:
Astronomy, as an observation science, has a unique relationship with data. New telescopes are regularly built, with more sensitive instruments, operating over many wavelengths, with new modes of measurement like neutrinos and gravitational waves, and the data quality keeps increasing. Many observations are unrepeatable because they document one-time phenomena (like supernovae or flares) each of which have unique characteristics, and yet must be analyzed in a population that spans observations over decades or centuries. This poses a challenge to analysis when older data must be used together with newer data. This is important especially because the variability of sources carries information about physical processes, e.g., allowing us to constrain the physical sizes of unresolved sources, measure masses, detect exoplanets, etc.
We focus here on long duration time series data. A prominent example is the record of the daily sunspot numbers which has been maintained since the mid-1700s, and the data have been steadily augmented with additional proxies like magnetic field and radio measurements. The generation-spanning maintenance of these data presents an incredible analysis challenge to statisticians. A rich trove of photometric plate surveys (existing since mid-1800s) are in the process of being digitized (DASCH). Recently, we have had space telescopes like Hubble (optical) and Chandra and XMM-Newton (X-ray) that have been observing for several decades continuously, and which are augmented by a fleet of smaller missions and new great observatories like the JWST. The EUV and X-ray emission from the Sun is monitored with space-borne telescopes like GOES and SDO over decades. Ground based surveys like PanSTARRS and the ZTF have been producing years-long light curves, compiling information at high cadence and sensitivity. Telescopes like Kepler and TESS have compiled high-quality data on selected stars in the galaxy as a byproduct of exoplanet huntin. But all these datasets are set to be dwarfed by the next generation of surveys like the Square Kilometer Array and the Rubin LSST, which will produce an order of magnitude more raw data and bring a qualitative revolution to astronomy.
The challenges in merging information across this large variety of data streams is self-evident. Analyses require a multi-disciplinary effort to avoid pitfalls of improper or false inferences. Astronomers are recognizing this and have begun the work to strategize solutions (e.g., working groups on variability monitoring strategies for HST and JWST, the Information and Stats collaboration for LSST). Our session brings together astronomers and statisticians who have been working on these problems and can speak of their hard-won triumphs. It will introduce the challenges involved in collecting and analyzing long duration astro datasets, and demonstrate the techniques currently used. We expect this to spark discussions between astronomers and statisticians and lead to new and improved methods to tease out qualitatively new information from these datasets.
We will have four speakers describing a variety of long-duration datasets and methods used to analyze them, followed by a statistician summarizing classical statistics techniques. Each speaker will have 20 minutes to speak, with the remaining time devoted to Q&A and discussion from the audience. Our experience with past astrostatistics sessions at the JSM is that the Q&A will be lively and extensive.
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
No
Applied
Yes
Estimated Audience Size
Small (<80)
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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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