435 research outputs found
Search for New Physics Involving Top Quarks at ATLAS
Two searches for new phenomena involving top quarks are presented: a search
for a top partner in ttbar events with large missing transverse momentum, and a
search for ttbar resonances in proton-proton collisions at a center-of-mass
energy of 7 TeV. The measurements are based on 35 pb^-1 and 200 pb^-1 of data
collected with the ATLAS detector at the LHC in 2010 and 2011, respectively. No
evidence for a signal is observed. The first limits from the LHC are
established on the mass of a top partner, excluding a mass of 275 GeV for a
neutral particle mass less than 50 GeV and a mass of 300 GeV for a neutral
particle mass less than 10 GeV. Using the reconstructed ttbar mass spectrum,
limits are set on the production cross-section times branching ratio to ttbar
for narrow and wide resonances. For narrow Z' models, the observed 95% C.L.
limits range from approximately 38 pb to 3.2 pb for masses going from m_Z' =
500 GeV to m_Z' = 1300 GeV. In Randall-Sundrum models, Kaluza-Klein gluons with
masses below 650 GeV are excluded at 95% C.L.Comment: 8 pages, 8 figures, 1 table, proceedings of the Meeting of the
Division of Particles and Fields of the American Physical Society, August
9-13, 2011, Brown University, Providence, Rhode Island, to be published
electronically on the SLAC Electronic Proceedings repositor
Charming the Higgs
We show that current Higgs data permit a significantly enhanced Higgs
coupling to charm pairs, comparable to the Higgs to bottom pairs coupling in
the Standard Model, without resorting to additional new physics sources in
Higgs production. With a mild level of the latter current data even allow for
the Higgs to charm pairs to be the dominant decay channel. An immediate
consequence of such a large charm coupling is a significant reduction of the
Higgs signal strengths into the known final states as in particular into bottom
pairs. This might reduce the visible vector-boson associated Higgs production
rate to a level that could compromise the prospects of ever observing it. We
however demonstrate that a significant fraction of this reduced signal can be
recovered by jet-flavor-tagging targeted towards charm-flavored jets. Finally
we argue that an enhanced Higgs to charm pairs coupling can be obtained in
various new physics scenarios in the presence of only a mild accidental
cancellation between various contributions.Comment: 8 pages, 3 figure
Flows for Flows: Training Normalizing Flows Between Arbitrary Distributions with Maximum Likelihood Estimation
Normalizing flows are constructed from a base distribution with a known
density and a diffeomorphism with a tractable Jacobian. The base density of a
normalizing flow can be parameterised by a different normalizing flow, thus
allowing maps to be found between arbitrary distributions. We demonstrate and
explore the utility of this approach and show it is particularly interesting in
the case of conditional normalizing flows and for introducing optimal transport
constraints on maps that are constructed using normalizing flows
Decorrelation using Optimal Transport
Being able to decorrelate a feature space from protected attributes is an
area of active research and study in ethics, fairness, and also natural
sciences. We introduce a novel decorrelation method using Convex Neural Optimal
Transport Solvers (Cnots), that is able to decorrelate continuous feature space
against protected attributes with optimal transport. We demonstrate how well it
performs in the context of jet classification in high energy physics, where
classifier scores are desired to be decorrelated from the mass of a jet. The
decorrelation achieved in binary classification approaches the levels achieved
by the state-of-the-art using conditional normalising flows. When moving to
multiclass outputs the optimal transport approach performs significantly better
than the state-of-the-art, suggesting substantial gains at decorrelating
multidimensional feature spaces
FETA: Flow-Enhanced Transportation for Anomaly Detection
Resonant anomaly detection is a promising framework for model-independent
searches for new particles. Weakly supervised resonant anomaly detection
methods compare data with a potential signal against a template of the Standard
Model (SM) background inferred from sideband regions. We propose a means to
generate this background template that uses a flow-based model to create a
mapping between high-fidelity SM simulations and the data. The flow is trained
in sideband regions with the signal region blinded, and the flow is conditioned
on the resonant feature (mass) such that it can be interpolated into the signal
region. To illustrate this approach, we use simulated collisions from the Large
Hadron Collider (LHC) Olympics Dataset. We find that our flow-constructed
background method has competitive sensitivity with other recent proposals and
can therefore provide complementary information to improve future searches.Comment: 13 pages, 11 figure
-Flows: Fast and improved neutrino reconstruction in multi-neutrino final states with conditional normalizing flows
In this work we introduce -Flows, an extension of the -Flows
method to final states containing multiple neutrinos. The architecture can
natively scale for all combinations of object types and multiplicities in the
final state for any desired neutrino multiplicities. In dilepton
events, the momenta of both neutrinos and correlations between them are
reconstructed more accurately than when using the most popular standard
analytical techniques, and solutions are found for all events. Inference time
is significantly faster than competing methods, and can be reduced further by
evaluating in parallel on graphics processing units. We apply -Flows to
dilepton events and show that the per-bin uncertainties in unfolded
distributions is much closer to the limit of performance set by perfect
neutrino reconstruction than standard techniques. For the chosen double
differential observables -Flows results in improved statistical
precision for each bin by a factor of 1.5 to 2 in comparison to the Neutrino
Weighting method and up to a factor of four in comparison to the Ellipse
approach.Comment: 20 pages, 16 figures, 5 table
\nu-Flows: Conditional Neutrino Regression
We present -Flows, a novel method for restricting the likelihood space
of neutrino kinematics in high energy collider experiments using conditional
normalizing flows and deep invertible neural networks. This method allows the
recovery of the full neutrino momentum which is usually left as a free
parameter and permits one to sample neutrino values under a learned conditional
likelihood given event observations. We demonstrate the success of -Flows
in a case study by applying it to simulated semileptonic events and
show that it can lead to more accurate momentum reconstruction, particularly of
the longitudinal coordinate. We also show that this has direct benefits in a
downstream task of jet association, leading to an improvement of up to a factor
of 1.41 compared to conventional methods.Comment: 26 pages, 15 figure
Topological Reconstruction of Particle Physics Processes using Graph Neural Networks
We present a new approach, the Topograph, which reconstructs underlying
physics processes, including the intermediary particles, by leveraging
underlying priors from the nature of particle physics decays and the
flexibility of message passing graph neural networks. The Topograph not only
solves the combinatoric assignment of observed final state objects, associating
them to their original mother particles, but directly predicts the properties
of intermediate particles in hard scatter processes and their subsequent
decays. In comparison to standard combinatoric approaches or modern approaches
using graph neural networks, which scale exponentially or quadratically, the
complexity of Topographs scales linearly with the number of reconstructed
objects.
We apply Topographs to top quark pair production in the all hadronic decay
channel, where we outperform the standard approach and match the performance of
the state-of-the-art machine learning technique.Comment: 25 pages, 24 figures, 8 table
Improving new physics searches with diffusion models for event observables and jet constituents
We introduce a new technique called Drapes to enhance the sensitivity in
searches for new physics at the LHC. By training diffusion models on side-band
data, we show how background templates for the signal region can be generated
either directly from noise, or by partially applying the diffusion process to
existing data. In the partial diffusion case, data can be drawn from side-band
regions, with the inverse diffusion performed for new target conditional
values, or from the signal region, preserving the distribution over the
conditional property that defines the signal region. We apply this technique to
the hunt for resonances using the LHCO di-jet dataset, and achieve
state-of-the-art performance for background template generation using high
level input features. We also show how Drapes can be applied to low level
inputs with jet constituents, reducing the model dependence on the choice of
input observables. Using jet constituents we can further improve sensitivity to
the signal process, but observe a loss in performance where the signal
significance before applying any selection is below 4.Comment: 34 pages, 19 figure
- …