1,968 research outputs found
Constraining Leptonic Flavour Model Parameters at Colliders and Beyond
The observed pattern of mixing in the neutrino sector may be explained by the
presence of a non-Abelian, discrete flavour symmetry broken into residual
subgroups at low energies. Many flavour models require the presence of Standard
Model singlet scalars which can promptly decay to charged leptons in a
flavour-violating manner. We constrain the model parameters of a generic
leptonic flavour model using a synergy of experimental data including limits
from charged lepton flavour conversion, an 8 TeV collider analysis and
constraints from the anomalous magnetic moment of the muon. The most powerful
constraints derive from the MEG collaborations' limit on Br and the reinterpretation of an 8 TeV ATLAS search for anomalous
productions of multi-leptonic final states. We quantify the exclusionary power
of each of these experiments and identify regions where the constraints from
collider and MEG experimental data are complementary.Comment: v1: 28 + 9 pages, 8 figures. v2: 30 + 10 pages, 10 figures. v2
consistent with JHEP accepted version where further discussion of results and
several more references were adde
Branches of a Tree: Taking Derivatives of Programs with Discrete and Branching Randomness in High Energy Physics
We propose to apply several gradient estimation techniques to enable the
differentiation of programs with discrete randomness in High Energy Physics.
Such programs are common in High Energy Physics due to the presence of
branching processes and clustering-based analysis. Thus differentiating such
programs can open the way for gradient based optimization in the context of
detector design optimization, simulator tuning, or data analysis and
reconstruction optimization. We discuss several possible gradient estimation
strategies, including the recent Stochastic AD method, and compare them in
simplified detector design experiments. In doing so we develop, to the best of
our knowledge, the first fully differentiable branching program.Comment: 8 page
Tuning the magnetic anisotropy of single molecules
The magnetism of single atoms and molecules is governed by the atomic scale
environment. In general, the reduced symmetry of the surrounding splits the
states and aligns the magnetic moment along certain favorable directions. Here,
we show that we can reversibly modify the magnetocrystalline anisotropy by
manipulating the environment of single iron(II) porphyrin molecules adsorbed on
Pb(111) with the tip of a scanning tunneling microscope. When we decrease the
tip--molecule distance, we first observe a small increase followed by an
exponential decrease of the axial anisotropy on the molecules. This is in
contrast to the monotonous increase observed earlier for the same molecule with
an additional axial Cl ligand. We ascribe the changes in the anisotropy of both
species to a deformation of the molecules in the presence of the attractive
force of the tip, which leads to a change in the level alignment. These
experiments demonstrate the feasibility of a precise tuning of the magnetic
anisotropy of an individual molecule by mechanical control.Comment: 16 pages, 5 figures; online at Nano Letters (2015
Control of oxidation and spin state in a single-molecule junction
The oxidation and spin state of a metalâorganic molecule determine its chemical reactivity and magnetic properties. Here, we demonstrate the reversible control of the oxidation and spin state in a single Fe porphyrin molecule in the force field of the tip of a scanning tunneling microscope. Within the regimes of half-integer and integer spin state, we can further track the evolution of the magnetocrystalline anisotropy. Our experimental results are corroborated by density functional theory and wave function theory. This combined analysis allows us to draw a complete picture of the molecular states over a large range of intramolecular deformations
Distributed statistical inference with pyhf enabled through funcX
In High Energy Physics facilities that provide High Performance Computing
environments provide an opportunity to efficiently perform the statistical
inference required for analysis of data from the Large Hadron Collider, but can
pose problems with orchestration and efficient scheduling. The compute
architectures at these facilities do not easily support the Python compute
model, and the configuration scheduling of batch jobs for physics often
requires expertise in multiple job scheduling services. The combination of the
pure-Python libraries pyhf and funcX reduces the common problem in HEP analyses
of performing statistical inference with binned models, that would
traditionally take multiple hours and bespoke scheduling, to an on-demand
(fitting) "function as a service" that can scalably execute across workers in
just a few minutes, offering reduced time to insight and inference. We
demonstrate execution of a scalable workflow using funcX to simultaneously fit
125 signal hypotheses from a published ATLAS search for new physics using pyhf
with a wall time of under 3 minutes. We additionally show performance
comparisons for other physics analyses with openly published probability models
and argue for a blueprint of fitting as a service systems at HPC centers.Comment: 9 pages, 1 figure, 2 listings, 1 table, submitted to the 25th
International Conference on Computing in High Energy & Nuclear Physic
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