43 research outputs found
Bulk Fermions in Soft Wall Models
We discuss the implementation of bulk fermions in soft wall models. The
introduction of a position dependent bulk mass allows for a well defined
Kaluza-Klein expansion for bulk fermions. The realization of flavor and the
contribution to electroweak precision observables are shown to be very similar
to the hard wall case. The bounds from electroweak precision test are however
milder with gauge boson Kaluza-Klein modes as light as TeV
compatible with current experimental bounds.Comment: Based on seminars given by the authors. To appear in the SUSY 09
proceeding
Universality and Evolution of TMDs
In this talk, we summarize how QCD evolution can be exploited to improve the
treatment of transverse momentum dependent (TMD) parton distribution and
fragmentation functions. The methods allow existing non-perturbative fits to be
turned into fully evolved TMDs that are consistent with a complete
TMD-factorization formalism over the full range of kT. We argue that evolution
is essential to the predictive power of calculations that utilize TMD parton
distribution and fragmentation functions, especially TMD observables that are
sensitive to transverse spin.Comment: To appear in the proceedings of the Third International Workshop on
Transverse Polarization Phenomena in Hard Scattering (Transversity 2011), in
Veli Losinj, Croatia, 29 August - 2 September 2011. 5 pages, 1 figur
The one-loop gluon amplitude for heavy-quark production at NNLO
We compute the one-loop QCD amplitude for the process gg-->Q\bar{Q} in
dimensional regularization through order \epsilon^2 in the dimensional
regulator and for arbitrary quark mass values. This result is an ingredient of
the NNLO cross-section for heavy quark production at hadron colliders. The
calculation is performed in conventional dimensional regularization, using well
known reduction techniques as well as a method based on recent ideas for the
functional form of one-loop integrands in four dimensions.Comment: 27 pages, 3 figure
Bulk Fermions in Warped Models with a Soft Wall
We study bulk fermions in models with warped extra dimensions in the presence
of a soft wall. Fermions can acquire a position dependent bulk Dirac mass that
shields them from the deep infrared, allowing for a systematic expansion in
which electroweak symmetry breaking effects are treated perturbatively. Using
this expansion, we analyze properties of bulk fermions in the soft wall
background. These properties include the realization of non-trivial boundary
conditions that simulate the ones commonly used in hard wall models, the
analysis of the flavor structure of the model and the implications of a heavy
top. We implement a soft wall model of electroweak symmetry breaking with
custodial symmetry and fermions propagating in the bulk. We find a lower bound
on the masses of the first bosonic resonances, after including the effects of
the top sector on electroweak precision observables for the first time, of
m_{KK} \gtrsim 1-3 TeV at the 95% C.L., depending on the details of the Higgs,
and discuss the implications of our results for LHC phenomenology.Comment: 34 pages, 8 figure
Soft gluons and superleading logarithms in QCD
After a brief introduction to the physics of soft gluons in QCD we present a
surprising prediction. Dijet production in hadron-hadron collisions provides
the paradigm, i.e. h_1 +h_2 \to jj+X. In particular, we look at the case where
there is a restriction placed on the emission of any further jets in the region
in between the primary (highest p_T) dijets. Logarithms in the ratio of the jet
scale to the veto scale can be summed to all orders in the strong coupling.
Surprisingly, factorization of collinear emissions fails at scales above the
veto scale and triggers the appearance of double logarithms in the hard
sub-process. The effect appears first at fourth order relative to the leading
order prediction and is subleading in the number of colours.Comment: Talk presented at the workshop "New Trends in HERA Physics", Ringberg
Castle, Tegernsee, 5-10 October 200
High Probability and Risk-Averse Guarantees for a Stochastic Accelerated Primal-Dual Method
We consider stochastic strongly-convex-strongly-concave (SCSC) saddle point
(SP) problems which frequently arise in applications ranging from
distributionally robust learning to game theory and fairness in machine
learning. We focus on the recently developed stochastic accelerated primal-dual
algorithm (SAPD), which admits optimal complexity in several settings as an
accelerated algorithm. We provide high probability guarantees for convergence
to a neighborhood of the saddle point that reflects accelerated convergence
behavior. We also provide an analytical formula for the limiting covariance
matrix of the iterates for a class of stochastic SCSC quadratic problems where
the gradient noise is additive and Gaussian. This allows us to develop lower
bounds for this class of quadratic problems which show that our analysis is
tight in terms of the high probability bound dependency to the parameters. We
also provide a risk-averse convergence analysis characterizing the
``Conditional Value at Risk'', the ``Entropic Value at Risk'', and the
-divergence of the distance to the saddle point, highlighting the
trade-offs between the bias and the risk associated with an approximate
solution obtained by terminating the algorithm at any iteration
Robust Accelerated Primal-Dual Methods for Computing Saddle Points
We consider strongly convex/strongly concave saddle point problems assuming
we have access to unbiased stochastic estimates of the gradients. We propose a
stochastic accelerated primal-dual (SAPD) algorithm and show that SAPD iterate
sequence, generated using constant primal-dual step sizes, linearly converges
to a neighborhood of the unique saddle point, where the size of the
neighborhood is determined by the asymptotic variance of the iterates.
Interpreting the asymptotic variance as a measure of robustness to gradient
noise, we obtain explicit characterizations of robustness in terms of SAPD
parameters and problem constants. Based on these characterizations, we develop
computationally tractable techniques for optimizing the SAPD parameters, i.e.,
the primal and dual step sizes, and the momentum parameter, to achieve a
desired trade-off between the convergence rate and robustness on the Pareto
curve. This allows SAPD to enjoy fast convergence properties while being robust
to noise as an accelerated method. We also show that SAPD admits convergence
guarantees for the gap metric with a variance term optimal up to a logarithmic
factor --which can be removed by employing a restarting strategy. Furthermore,
to our knowledge, our work is the first one showing an iteration complexity
result for the gap function on smooth SCSC problems without the bounded domain
assumption. Finally, we illustrate the efficiency of our approach on
distributionally robust logistic regression problems