We propose a novel approach for observing cosmic rays at ultra-high energy ($>10^{18}$~eV) by repurposing the existing network of smartphones as a ground detector array. Extensive air showers generated by cosmic rays produce muons and high-energy pho
tons, which can be detected by the CMOS sensors of smartphone cameras. The small size and low efficiency of each sensor is compensated by the large number of active phones. We show that if user adoption targets are met, such a network will have significant observing power at the highest energies.
Bounds on invisible decays of the Higgs boson from $tbar{t}H$ production were inferred from a CMS search for stop quarks decaying to $tbar{t}$ and missing transverse momentum. Limits on the production of $tbar{t}H$ relied on the efficiency of the CMS
selection for $tbar{t}H$, as measured in a simulated sample. An error in the generation of the simulated sample lead to a significant overestimate of the selection efficiency. Corrected results are presented.
Collisions at high-energy particle colliders are a traditionally fruitful source of exotic particle discoveries. Finding these rare particles requires solving difficult signal-versus-background classification problems, hence machine learning approach
es are often used. Standard approaches have relied on `shallow machine learning models that have a limited capacity to learn complex non-linear functions of the inputs, and rely on a pain-staking search through manually constructed non-linear features. Progress on this problem has slowed, as a variety of techniques have shown equivalent performance. Recent advances in the field of deep learning make it possible to learn more complex functions and better discriminate between signal and background classes. Using benchmark datasets, we show that deep learning methods need no manually constructed inputs and yet improve the classification metric by as much as 8% over the best current approaches. This demonstrates that deep learning approaches can improve the power of collider searches for exotic particles.
This report summarizes the work of the Energy Frontier New Physics working group of the 2013 Community Summer Study (Snowmass).
In models of maximal flavor violation (MxFV) there is at least one new scalar $Phi_{FV}$ which couples to the quarks via $Phi_{FV} q_i q_j propto xi_{ij}$ where $xi_{i3},xi_{3i} sim V_{tb}$ for $i = 1,2$ and $xi_{33} sim V_{td}$ and $V$ is the CKM ma
trix. In this article, we explore the potential phenomenological implications of MxFV for collider experiments. We study MxFV signals of same-sign leptons from same-sign top-quark pair production at the Tevatron and at the LHC. We show that the current Tevatron dataset has strong sensitivity to this signature, for which there are no current limits. For example, if $m_{Phi_{FV}} sim 200$ GeV and the MxFV coupling $xi$ has a natural value of $sim 1$, we expect $sim 12$ MxFV events to survive a selection requiring a pair of same-sign leptons, a tagged $b$-jet and missing transverse energy, over a background of approximately 4-5 events.
