351 research outputs found
Spatial prioritisation of revegetation sites for dryland salinity management: an analytical framework using GIS
[Abstract]: To address the lack of analytical and modelling techniques in prioritising revegetation sites for dryland salinity management, a case study of the Hodgson Creek catchment in Queensland, Australia, was conducted. An analytical framework was developed, incorporating the use of spatial datasets (Landsat 7 image, DEM, soil map, and salinity map) which were processed using image processing techniques and a geographic information system (GIS). Revegetation sites were mapped and their priority determined based on recharge area, land use/cover and sub-catchment salinity. The analytical framework presented here enhances the systematic use of land information, widens the scope for scenario testing, and improves the testing of alternative revegetation options. The spatial patterns of revegetation sites could provide an additional set of information relevant in the design of revegetation strategies
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
Improved HVAC energy throughput system
Currently heating, ventilation and air conditioning (HVAC) systems are difficult and costly to monitor for energy efficiency performance and reliability. As buildings evolve, they will require higher levels of insulation and air tightness which will require ventilation systems to provide the minimum number of air changes and reduced energy usage by recovering heat from the air before it is expelled. This will necessitate the need for monitoring of the operating performance of these systems so that air quality or building energy efficiency is not detrimentally affected.
A typical duct airflow monitoring device uses a pressure differential method to determine the airflow rate but they are fragile, expensive and create an additional pressure loss. The monitoring of airflow rates can indicate problems in the design, installation and operation of a HVAC system. One of the possible alternatives to using pressure differential type devices such as Pitot tube/arrays, orifice plates and Venturis is to use an ultrasonic flow rate sensor, but historically their high cost has restricted their use in HVAC systems.
This project has looked at improving on existing measuring systems by developing an ultrasonic in-duct flowmeter system to measure the mean airflow, temperature and humidity of a ventilation duct so that a comparative energy level can be accurately deduced.
A proof of concept in-duct ultrasonic airflow monitoring device has been developed and has obtained results within ±3.5% RMS of a Venturi airflow measuring device.
Matlab code for a Monte Carlo acoustic ray/particle tracing ultrasonic flowmeter simulation has been developed to study the effects of non-ideal installation scenarios. The fully developed centreline computational fluid dynamics (CFD) mean flow velocity to duct total mean flow velocity error can be up to 13%. Analysis of the CFD data for various duct scenarios has shown that this could be reduced to below 5% by using a transducer offset of approximately ±0.25 duct diameters or widths from the centreline at distances as close as one duct hydraulic diameter from an upstream disturbance, such as caused by a bend
-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
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
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