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Upcoming Seminars
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Jan. 9th: CWoLa Hunting: Machine Learning for Model Agnostic Searches for New Physics at the LHC
Date: January 9th at 12:30pm
Location: Bldg. 53 Rm 4002Speaker: Jack Collins (University of Maryland)
Abstract: One of the main goals for the LHC is to search for new physics around the TeV scale, but the precise signature is not known in advance. I will discuss some of the Machine Learning strategies that have been proposed to search for new physics with minimal assumptions on the signal model, with a focus on 'CWoLa (Classification Without Labels) Hunting'. In this bump-hunting strategy, it is assumed that the signal forms a resonant bump in some variable (as is common in particle physics) in which the Standard Model backgrounds are smooth, which can be used to determine sideband and signal regions. Using a set of orthogonal auxiliary observables, a classifier is trained to identify signal-region over-densities which may indicate the presence of new physics.
Jan. 11th: Using machine learning to unlock Gaia’s full potential to determine the dark matter halo
Date: January 11th at 12:30pm :30pm
Location: Bldg. 53 Rm 4002Speaker: Bryan Ostdiek (University of Oregon)
Abstract: Understanding the properties of our dark matter halo is relevant to both astrophysics as it informs the formation history of our galaxy, and particle physics in that it impacts the interpretation of dark matter experiments. This talk reviews the spherical cow assumptions that underly the model for the halo that is typically assumed, and then questions those assumptions, making clear that a data driven approach is warranted. Recent work that has shown that low metallicity stars can act as tracers for different components of dark matter, which has allowed data from the Gaia satellite to be interpreted as a measurement of the halo. However, the number of stars in the Gaia dataset with metallicity information (~200,000) is a small fraction of the order 1 billion measured stars. With the aid of modern machine learning technology, we seek to find if the tracer stars can be identified only by the kinematic information measure by Gaia. Our positive initial results indicate that it could be possible to “learn the dark matter halo” with much finer resolution than is currently possible.
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