NPAC (Nuclear/Particle/Astro/Cosmo) Forums |
Organized by: Prof. Lu Lu
Events During the Week of April 22nd through April 29th, 2018
Monday, April 23rd, 2018
- No events scheduled
Tuesday, April 24th, 2018
- No events scheduled
Wednesday, April 25th, 2018
- No events scheduled
Thursday, April 26th, 2018
- Observation of the highest-energy gamma rays with the HAWC Observatory
- Time: 2:30 pm
- Place: 5310 Chamberlin Hall
- Speaker: Kelly Malone, Penn State
- Abstract: Galactic sources that accelerate particles to PeV energies (“PeVatrons”) are expected to exist, but to date only the Galactic Center has been identified as such. One of the signatures of a PeVatron is a hard gamma-ray spectrum that extends without any apparent spectral cutoff to at least tens of TeV. High-energy (> 50 TeV) gamma-ray observations are therefore essential in identifying PeVatron candidates. The High Altitude Water Cherenkov Observatory (HAWC) has sensitivity to gamma rays in this previously largely unexplored energy regime. HAWC is well suited to performing all-sky surveys due to its large instantaneous field of view (~2 sr) and high duty cycle (> 95%). I will discuss candidate sources seen above 50 TeV in the first 1000 days of HAWC data and discuss potential connections to the IceCube neutrinos. I will also briefly discuss the energy estimation method used by HAWC.
- Host: Stefan Westerhoff
- Can a tool developed for hurricane prediction be taken to predict neutrino flavor evolution?
- Time: 4:00 pm
- Place: 5280 Chamberlin Hall
- Speaker: Eve Armstrong, University of Pennsylvania
- Abstract: We assess the utility of an optimization-based data assimilation (D.A.)
technique for treating the problem of nonlinear neutrino flavor
transformation in core-collapse supernovae. D.A. was invented for
numerical weather prediction, and it shares some features of machine
learning for the purposes of predictive power. Within the D.A. framework,
one uses measurements obtained from a physical system to estimate the
state variable evolution and parameter values of the associated model.
Formulated as an optimization procedure, D.A. can offer an
integration-blind approach to predicting model evolution, which offers an
advantage for models that thwart solution via traditional numerical
integration techniques. Further, D.A. performs most optimally for models
whose equations of motion are nonlinearly coupled. In this exploratory
work, we consider a simple steady-state model with two mono-energetic
neutrino beams coherently interacting with each other and a background
medium. As this model can be solved via numerical integration, we have an
independent consistency check for D.A. solutions.
We find that the procedure can capture key features of flavor evolution
over the entire trajectory, even given measurements of neutrino flavor
only at the endpoint, and with an assumed known initial flavor
distribution. Further, the procedure permits an examination of the
sensitivity of flavor evolution to estimates of unknown model parameters,
locates degeneracies in parameter space, and can identify the specific
measurements required to break those degeneracies. - Host: Baha Balantekin
Friday, April 27th, 2018
- No events scheduled