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SUMMARY:Information and Statistics in Nuclear Experiment and Theory (ISNET
v8)
DTSTART;VALUE=DATE-TIME:20201214T130000Z
DTEND;VALUE=DATE-TIME:20201217T220000Z
DTSTAMP;VALUE=DATE-TIME:20210304T071429Z
UID:indico-event-21@indico.frib.msu.edu
DESCRIPTION:Dear Colleagues\;\nDue to the spread of COVID-19\, the ISNET L
ocal Organizing Committee will postpone the in-person conference for 2021.
\n\nA Virtual conference will be held on December 14-17 2020\, updates wil
l follow.\n\n Thank you for your understanding.\n INSET 8 Lo
cal Organizing Committee\n\n———————————————
—————————————————————\n\nThe in-
person Information and Statistics in Nuclear Experiment and Theory (ISNE
T 8) conference will be held at the Facility for Rare Isotope Beams (FRI
B) on the campus of Michigan State University (MSU) in East Lansing\, MI U
SA in Dec.\n\nData is expensive to obtain and comes with uncertainty. What
is the best way to use experimental data in the formulation of theoretica
l models that attempt to explain the results? This workshop will discuss t
he use of information theory in the analysis of experiments\, and the use
of applied mathematics and statistics within the context of theoretical mo
dels dealing with current and future data.\n\n—————————
————————————————————————
———\n\nTopics\n\n• Information theory\n\n• Bayesian approaches\n
\n• Uncertainty quantification\n\n• Statistical correlations\n\n• Co
mputational techniques\n\n————————————————
————————————————————\n\nKey Questi
ons\n\n• How can we estimate statistical and systematic errors on calcul
ated quantities?\n\n• How can the uniqueness and usefulness of an observ
able be assessed\, i.e.\, its information content with respect to current
theoretical models?\n\n• How can model-based extrapolations be validated
and verified?\n\n• What experimental data are crucial for better constr
aining current nuclear models?\n\n• How can statistical tools of nuclear
theory help planning future experiments and experimental programs?\n\n—
————————————————————————
———————————\n\nBackground\n\nThe scientific method u
ses experimentation to assess theoretical predictions. Based on experiment
al data\, the theory is modified and can be used to guide future measureme
nts. The process is then repeated until the theory is able to explain obse
rvations\, and the experiment is consistent with theoretical predictions.
The positive feedback in the loop "experiment-theory-experiment-" can be e
nhanced if statistical methods and scientific computing are applied to det
ermine the independence of model parameters\, parameter uncertainties\, an
d the errors of calculated observables.\n\nNuclei communicate with us thro
ugh a great variety of observables. Some are easy to measure\; some take a
considerable effort and experimental ingenuity. But not every observable
has a potential to impact theoretical developments: some are more importan
t than the others. Nuclear theory is developing tools to deliver uncertain
ty quantification and error analysis for theoretical studies as well as fo
r the assessment of new experimental data. Statistical tools can also be u
sed to assess the information content of an observable with respect to cur
rent theoretical models\, and evaluate the degree of correlation between d
ifferent observables. Such technologies are essential for providing predic
tive capability\, estimate uncertainties\, and assess model-based extrapol
ations - as theoretical models are often applied to entirely new nuclear s
ystems and conditions that are not accessible to experiment.\n\nhttps://in
dico.frib.msu.edu/event/21/
LOCATION:NSCL/FRIB 1200 FRIB
URL:https://indico.frib.msu.edu/event/21/
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