1,579 research outputs found
Direct optimisation of the discovery significance when training neural networks to search for new physics in particle colliders
We introduce two new loss functions designed to directly optimise the
statistical significance of the expected number of signal events when training
neural networks to classify events as signal or background in the scenario of a
search for new physics at a particle collider. The loss functions are designed
to directly maximise commonly used estimates of the statistical significance,
, and the Asimov estimate, . We consider their use in a toy
SUSY search with 30~fb of 14~TeV data collected at the LHC. In the case
that the search for the SUSY model is dominated by systematic uncertainties, it
is found that the loss function based on can outperform the binary cross
entropy in defining an optimal search region
Performance and optimization of support vector machines in high-energy physics classification problems
In this paper we promote the use of Support Vector Machines (SVM) as a
machine learning tool for searches in high-energy physics. As an example for a
new- physics search we discuss the popular case of Supersymmetry at the Large
Hadron Collider. We demonstrate that the SVM is a valuable tool and show that
an automated discovery- significance based optimization of the SVM
hyper-parameters is a highly efficient way to prepare an SVM for such
applications. A new C++ LIBSVM interface called SVM-HINT is developed and
available on Github.Comment: 20 pages, 6 figure
Point Cloud Generation using Transformer Encoders and Normalising Flows
Data generation based on Machine Learning has become a major research topic
in particle physics. This is due to the current Monte Carlo simulation approach
being computationally challenging for future colliders, which will have a
significantly higher luminosity. The generation of collider data is similar to
point cloud generation, but arguably more difficult as there are complex
correlations between the points which need to be modelled correctly. A
refinement model consisting of normalising flows and transformer encoders is
presented. The normalising flow output is corrected by a transformer encoder,
which is adversarially trained against another transformer encoder
discriminator/critic. The model reaches state-of-the-art performance while
yielding a stable training
Validation of Deep Convolutional Generative Adversarial Networks for High Energy Physics Calorimeter Simulations
In particle physics the simulation of particle transport through detectors
requires an enormous amount of computational resources, utilizing more than 50%
of the resources of the CERN Worldwide Large Hadron Collider Grid. This
challenge has motivated the investigation of different, faster approaches for
replacing the standard Monte Carlo simulations. Deep Learning Generative
Adversarial Networks are among the most promising alternatives. Previous
studies showed that they achieve the necessary level of accuracy while
decreasing the simulation time by orders of magnitudes. In this paper we
present a newly developed neural network architecture which reproduces a
three-dimensional problem employing 2D convolutional layers and we compare its
performance with an earlier architecture consisting of 3D convolutional layers.
The performance evaluation relies on direct comparison to Monte Carlo
simulations, in terms of different physics quantities usually employed to
quantify the detector response. We prove that our new neural network
architecture reaches a higher level of accuracy with respect to the 3D
convolutional GAN while reducing the necessary computational resources.
Calorimeters are among the most expensive detectors in terms of simulation
time. Therefore we focus our study on an electromagnetic calorimeter prototype
with a regular highly granular geometry, as an example of future calorimeters.Comment: AAAI-MLPS 2021 Spring Symposium at Stanford Universit
Precise Image Generation on Current Noisy Quantum Computing Devices
The Quantum Angle Generator (QAG) is a new full Quantum Machine Learning
model designed to generate accurate images on current Noise Intermediate Scale
(NISQ) Quantum devices. Variational quantum circuits form the core of the QAG
model, and various circuit architectures are evaluated. In combination with the
so-called MERA-upsampling architecture, the QAG model achieves excellent
results, which are analyzed and evaluated in detail. To our knowledge, this is
the first time that a quantum model has achieved such accurate results. To
explore the robustness of the model to noise, an extensive quantum noise study
is performed. In this paper, it is demonstrated that the model trained on a
physical quantum device learns the noise characteristics of the hardware and
generates outstanding results. It is verified that even a quantum hardware
machine calibration change during training of up to 8% can be well tolerated.
For demonstration, the model is employed in indispensable simulations in high
energy physics required to measure particle energies and, ultimately, to
discover unknown particles at the Large Hadron Collider at CERN
JetFlow: Generating Jets with Conditioned and Mass Constrained Normalising Flows
Fast data generation based on Machine Learning has become a major research
topic in particle physics. This is mainly because the Monte Carlo simulation
approach is computationally challenging for future colliders, which will have a
significantly higher luminosity. The generation of collider data is similar to
point cloud generation with complex correlations between the points.
In this study, the generation of jets with up to 30 constituents with
Normalising Flows using Rational Quadratic Spline coupling layers is
investigated. Without conditioning on the jet mass, our Normalising Flows are
unable to model all correlations in data correctly, which is evident when
comparing the invariant jet mass distributions between ground truth and
generated data. Using the invariant mass as a condition for the coupling
transformation enhances the performance on all tracked metrics. In addition, we
demonstrate how to sample the original mass distribution by interpolating the
empirical cumulative distribution function. Similarly, the variable number of
constituents is taken care of by introducing an additional condition on the
number of constituents in the jet.
Furthermore, we study the usefulness of including an additional mass
constraint in the loss term. On the \texttt{JetNet} dataset, our model shows
state-of-the-art performance combined with fast and stable training
A Study of Failure Modes in the ILC Main Linac
Failures in the ILC can lead to beam loss or even damage the machine. In the paper quadrupole failures and errors in the klystron phase are being investigated and the impact on the machine protection is being considered for the main linac
Reduced Precision Strategies for Deep Learning: A High Energy Physics Generative Adversarial Network Use Case
Deep learning is finding its way into high energy physics by replacing
traditional Monte Carlo simulations. However, deep learning still requires an
excessive amount of computational resources. A promising approach to make deep
learning more efficient is to quantize the parameters of the neural networks to
reduced precision. Reduced precision computing is extensively used in modern
deep learning and results to lower execution inference time, smaller memory
footprint and less memory bandwidth. In this paper we analyse the effects of
low precision inference on a complex deep generative adversarial network model.
The use case which we are addressing is calorimeter detector simulations of
subatomic particle interactions in accelerator based high energy physics. We
employ the novel Intel low precision optimization tool (iLoT) for quantization
and compare the results to the quantized model from TensorFlow Lite. In the
performance benchmark we gain a speed-up of 1.73x on Intel hardware for the
quantized iLoT model compared to the initial, not quantized, model. With
different physics-inspired self-developed metrics, we validate that the
quantized iLoT model shows a lower loss of physical accuracy in comparison to
the TensorFlow Lite model.Comment: Submitted at ICPRAM 2021; from CERN openlab - Intel collaboratio
Multiplicity Structure of the Hadronic Final State in Diffractive Deep-Inelastic Scattering at HERA
The multiplicity structure of the hadronic system X produced in
deep-inelastic processes at HERA of the type ep -> eXY, where Y is a hadronic
system with mass M_Y< 1.6 GeV and where the squared momentum transfer at the pY
vertex, t, is limited to |t|<1 GeV^2, is studied as a function of the invariant
mass M_X of the system X. Results are presented on multiplicity distributions
and multiplicity moments, rapidity spectra and forward-backward correlations in
the centre-of-mass system of X. The data are compared to results in e+e-
annihilation, fixed-target lepton-nucleon collisions, hadro-produced
diffractive final states and to non-diffractive hadron-hadron collisions. The
comparison suggests a production mechanism of virtual photon dissociation which
involves a mixture of partonic states and a significant gluon content. The data
are well described by a model, based on a QCD-Regge analysis of the diffractive
structure function, which assumes a large hard gluonic component of the
colourless exchange at low Q^2. A model with soft colour interactions is also
successful.Comment: 22 pages, 4 figures, submitted to Eur. Phys. J., error in first
submission - omitted bibliograph
Measurements of Transverse Energy Flow in Deep-Inelastic Scattering at HERA
Measurements of transverse energy flow are presented for neutral current
deep-inelastic scattering events produced in positron-proton collisions at
HERA. The kinematic range covers squared momentum transfers Q^2 from 3.2 to
2,200 GeV^2, the Bjorken scaling variable x from 8.10^{-5} to 0.11 and the
hadronic mass W from 66 to 233 GeV. The transverse energy flow is measured in
the hadronic centre of mass frame and is studied as a function of Q^2, x, W and
pseudorapidity. A comparison is made with QCD based models. The behaviour of
the mean transverse energy in the central pseudorapidity region and an interval
corresponding to the photon fragmentation region are analysed as a function of
Q^2 and W.Comment: 26 pages, 8 figures, submitted to Eur. Phys.
- âŠ