1,121 research outputs found
High-dimensional and Permutation Invariant Anomaly Detection
Methods for anomaly detection of new physics processes are often limited to
low-dimensional spaces due to the difficulty of learning high-dimensional
probability densities. Particularly at the constituent level, incorporating
desirable properties such as permutation invariance and variable-length inputs
becomes difficult within popular density estimation methods. In this work, we
introduce a permutation-invariant density estimator for particle physics data
based on diffusion models, specifically designed to handle variable-length
inputs. We demonstrate the efficacy of our methodology by utilizing the learned
density as a permutation-invariant anomaly detection score, effectively
identifying jets with low likelihood under the background-only hypothesis. To
validate our density estimation method, we investigate the ratio of learned
densities and compare to those obtained by a supervised classification
algorithm.Comment: 7 pages, 5 figure
CaloScore v2: Single-shot Calorimeter Shower Simulation with Diffusion Models
Diffusion generative models are promising alternatives for fast surrogate
models, producing high-fidelity physics simulations. However, the generation
time often requires an expensive denoising process with hundreds of function
evaluations, restricting the current applicability of these models in a
realistic setting. In this work, we report updates on the CaloScore
architecture, detailing the changes in the diffusion process, which produces
higher quality samples, and the use of progressive distillation, resulting in a
diffusion model capable of generating new samples with a single function
evaluation. We demonstrate these improvements using the Calorimeter Simulation
Challenge 2022 dataset.Comment: 10 pages, 5 figure
Point Cloud Transformers applied to Collider Physics
Methods for processing point cloud information have seen a great success in
collider physics applications. One recent breakthrough in machine learning is
the usage of Transformer networks to learn semantic relationships between
sequences in language processing. In this work, we apply a modified Transformer
network called Point Cloud Transformer as a method to incorporate the
advantages of the Transformer architecture to an unordered set of particles
resulting from collision events. To compare the performance with other
strategies, we study jet-tagging applications for highly-boosted particles.Comment: 12 pages, 3 figure
ABCNet: An attention-based method for particle tagging
In high energy physics, graph-based implementations have the advantage of
treating the input data sets in a similar way as they are collected by collider
experiments. To expand on this concept, we propose a graph neural network
enhanced by attention mechanisms called ABCNet. To exemplify the advantages and
flexibility of treating collider data as a point cloud, two physically
motivated problems are investigated: quark-gluon discrimination and pileup
reduction. The former is an event-by-event classification while the latter
requires each reconstructed particle to receive a classification score. For
both tasks ABCNet shows an improved performance compared to other algorithms
available.Comment: 13 pages, 5 figure
Highlights on top quark measurements from CMS
Recent results from the CMS Collaboration using top quarks are presented. These results are based on partial datasets collected by the CMS Collaboration during the LHC Run 2, at a center-of-mass energy of 13 TeV. This document includes the first measurement of production in association with charm quarks, the first direct measurement of the third generation of the CKM matrix elements, the investigation of the running of the top quark mass, search for CP violation in top quark production, measurement of the forward-backward asymmetry in production at the LHC, and the first global approach in constraining EFT operator coefficients using top quarks
Fast Point Cloud Generation with Diffusion Models in High Energy Physics
Many particle physics datasets like those generated at colliders are
described by continuous coordinates (in contrast to grid points like in an
image), respect a number of symmetries (like permutation invariance), and have
a stochastic dimensionality. For this reason, standard deep generative models
that produce images or at least a fixed set of features are limiting. We
introduce a new neural network simulation based on a diffusion model that
addresses these limitations named Fast Point Cloud Diffusion (FPCD). We show
that our approach can reproduce the complex properties of hadronic jets from
proton-proton collisions with competitive precision to other recently proposed
models. Additionally, we use a procedure called progressive distillation to
accelerate the generation time of our method, which is typically a significant
challenge for diffusion models despite their state-of-the-art precision.Comment: 11 pages, 8 figure
Refining Fast Calorimeter Simulations with a Schr\"{o}dinger Bridge
Machine learning-based simulations, especially calorimeter simulations, are
promising tools for approximating the precision of classical high energy
physics simulations with a fraction of the generation time. Nearly all methods
proposed so far learn neural networks that map a random variable with a known
probability density, like a Gaussian, to realistic-looking events. In many
cases, physics events are not close to Gaussian and so these neural networks
have to learn a highly complex function. We study an alternative approach:
Schr\"{o}dinger bridge Quality Improvement via Refinement of Existing
Lightweight Simulations (SQuIRELS). SQuIRELS leverages the power of
diffusion-based neural networks and Schr\"{o}dinger bridges to map between
samples where the probability density is not known explicitly. We apply
SQuIRELS to the task of refining a classical fast simulation to approximate a
full classical simulation. On simulated calorimeter events, we find that
SQuIRELS is able to reproduce highly non-trivial features of the full
simulation with a fraction of the generation time.Comment: 10 pages, 5 figure
Improving Generative Model-based Unfolding with Schr\"{o}dinger Bridges
Machine learning-based unfolding has enabled unbinned and high-dimensional
differential cross section measurements. Two main approaches have emerged in
this research area: one based on discriminative models and one based on
generative models. The main advantage of discriminative models is that they
learn a small correction to a starting simulation while generative models scale
better to regions of phase space with little data. We propose to use
Schroedinger Bridges and diffusion models to create SBUnfold, an unfolding
approach that combines the strengths of both discriminative and generative
models. The key feature of SBUnfold is that its generative model maps one set
of events into another without having to go through a known probability density
as is the case for normalizing flows and standard diffusion models. We show
that SBUnfold achieves excellent performance compared to state of the art
methods on a synthetic Z+jets dataset.Comment: 9 pages, 5 figure
Comparison of Point Cloud and Image-based Models for Calorimeter Fast Simulation
Score based generative models are a new class of generative models that have
been shown to accurately generate high dimensional calorimeter datasets. Recent
advances in generative models have used images with 3D voxels to represent and
model complex calorimeter showers. Point clouds, however, are likely a more
natural representation of calorimeter showers, particularly in calorimeters
with high granularity. Point clouds preserve all of the information of the
original simulation, more naturally deal with sparse datasets, and can be
implemented with more compact models and data files. In this work, two
state-of-the-art score based models are trained on the same set of calorimeter
simulation and directly compared.Comment: 11 pages, 6 figures, 1 tabl
Measurement of prompt and meson azimuthal anisotropy and search for strong electric fields in PbPb collisions at = 5.02 TeV
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