161 research outputs found
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
\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
-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
Flow Away your Differences: Conditional Normalizing Flows as an Improvement to Reweighting
We present an alternative to reweighting techniques for modifying
distributions to account for a desired change in an underlying conditional
distribution, as is often needed to correct for mis-modelling in a simulated
sample. We employ conditional normalizing flows to learn the full conditional
probability distribution from which we sample new events for conditional values
drawn from the target distribution to produce the desired, altered
distribution. In contrast to common reweighting techniques, this procedure is
independent of binning choice and does not rely on an estimate of the density
ratio between two distributions.
In several toy examples we show that normalizing flows outperform reweighting
approaches to match the distribution of the target.We demonstrate that the
corrected distribution closes well with the ground truth, and a statistical
uncertainty on the training dataset can be ascertained with bootstrapping. In
our examples, this leads to a statistical precision up to three times greater
than using reweighting techniques with identical sample sizes for the source
and target distributions. We also explore an application in the context of high
energy particle physics.Comment: 21 pages, 9 figure
Flows for Flows: Morphing one Dataset into another with Maximum Likelihood Estimation
Many components of data analysis in high energy physics and beyond require
morphing one dataset into another. This is commonly solved via reweighting, but
there are many advantages of preserving weights and shifting the data points
instead. Normalizing flows are machine learning models with impressive
precision on a variety of particle physics tasks. Naively, normalizing flows
cannot be used for morphing because they require knowledge of the probability
density of the starting dataset. In most cases in particle physics, we can
generate more examples, but we do not know densities explicitly. We propose a
protocol called flows for flows for training normalizing flows to morph one
dataset into another even if the underlying probability density of neither
dataset is known explicitly. This enables a morphing strategy trained with
maximum likelihood estimation, a setup that has been shown to be highly
effective in related tasks. We study variations on this protocol to explore how
far the data points are moved to statistically match the two datasets.
Furthermore, we show how to condition the learned flows on particular features
in order to create a morphing function for every value of the conditioning
feature. For illustration, we demonstrate flows for flows for toy examples as
well as a collider physics example involving dijet eventsComment: 15 pages, 17 figures. This work is a merger of arXiv:2211.02487 and
arXiv:2212.0615
PC-Droid: Faster diffusion and improved quality for particle cloud generation
Building on the success of PC-JeDi we introduce PC-Droid, a substantially
improved diffusion model for the generation of jet particle clouds. By
leveraging a new diffusion formulation, studying more recent integration
solvers, and training on all jet types simultaneously, we are able to achieve
state-of-the-art performance for all types of jets across all evaluation
metrics. We study the trade-off between generation speed and quality by
comparing two attention based architectures, as well as the potential of
consistency distillation to reduce the number of diffusion steps. Both the
faster architecture and consistency models demonstrate performance surpassing
many competing models, with generation time up to two orders of magnitude
faster than PC-JeDi and three orders of magnitude faster than Delphes.Comment: 21 pages, 8 tables, 13 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
The Interplay of Machine Learning--based Resonant Anomaly Detection Methods
Machine learning--based anomaly detection (AD) methods are promising tools
for extending the coverage of searches for physics beyond the Standard Model
(BSM). One class of AD methods that has received significant attention is
resonant anomaly detection, where the BSM is assumed to be localized in at
least one known variable. While there have been many methods proposed to
identify such a BSM signal that make use of simulated or detected data in
different ways, there has not yet been a study of the methods' complementarity.
To this end, we address two questions. First, in the absence of any signal, do
different methods pick the same events as signal-like? If not, then we can
significantly reduce the false-positive rate by comparing different methods on
the same dataset. Second, if there is a signal, are different methods fully
correlated? Even if their maximum performance is the same, since we do not know
how much signal is present, it may be beneficial to combine approaches. Using
the Large Hadron Collider (LHC) Olympics dataset, we provide quantitative
answers to these questions. We find that there are significant gains possible
by combining multiple methods, which will strengthen the search program at the
LHC and beyond.Comment: 23 pages, 17 figure
The sweet spot in sustainability: a framework for corporate assessment in sugar manufacturing
The assessment of corporate sustainability has become an increasingly important topic, both within academia and in industry. For manufacturing companies to conform to their commitments to sustainable development, a standard and reliable measurement framework is required. There is, however, a lack of sector-specific and empirical research in many areas, including the sugar industry. This paper presents an empirically developed framework for the assessment of corporate sustainability within the Thai sugar industry. Multiple case studies were conducted, and a survey using questionnaires was also employed to enhance the power of generalisation. The developed framework is an accurate and reliable measurement instrument of corporate sustainability, and guidelines to assess qualitative criteria are put forward. The proposed framework can be used for a company’s self-assessment and for guiding practitioners in performance improvement and policy decision-maki
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