16 research outputs found
Constraining the Parameters of High-Dimensional Models with Active Learning
Constraining the parameters of physical models with parameters is a
widespread problem in fields like particle physics and astronomy. The
generation of data to explore this parameter space often requires large amounts
of computational resources. The commonly used solution of reducing the number
of relevant physical parameters hampers the generality of the results. In this
paper we show that this problem can be alleviated by the use of active
learning. We illustrate this with examples from high energy physics, a field
where simulations are often expensive and parameter spaces are
high-dimensional. We show that the active learning techniques
query-by-committee and query-by-dropout-committee allow for the identification
of model points in interesting regions of high-dimensional parameter spaces
(e.g. around decision boundaries). This makes it possible to constrain model
parameters more efficiently than is currently done with the most common
sampling algorithms and to train better performing machine learning models on
the same amount of data. Code implementing the experiments in this paper can be
found on GitHub
DeepXS: Fast approximation of MSSM electroweak cross sections at NLO
We present a deep learning solution to the prediction of particle production
cross sections over a complicated, high-dimensional parameter space. We
demonstrate the applicability by providing state-of-the-art predictions for the
production of charginos and neutralinos at the Large Hadron Collider (LHC) at
the next-to-leading order in the phenomenological MSSM-19 and explicitly
demonstrate the performance for
and as
a proof of concept which will be extended to all SUSY electroweak pairs. We
obtain errors that are lower than the uncertainty from scale and parton
distribution functions with mean absolute percentage errors of well below
allowing a safe inference at the next-to-leading order with inference
times that improve the Monte Carlo integration procedures that have been
available so far by a factor of from
to per evaluation.Comment: 7 pages, 3 figure
Identifying WIMP dark matter from particle and astroparticle data
One of the most promising strategies to identify the nature of dark matter
consists in the search for new particles at accelerators and with so-called
direct detection experiments. Working within the framework of simplified
models, and making use of machine learning tools to speed up statistical
inference, we address the question of what we can learn about dark matter from
a detection at the LHC and a forthcoming direct detection experiment. We show
that with a combination of accelerator and direct detection data, it is
possible to identify newly discovered particles as dark matter, by
reconstructing their relic density assuming they are weakly interacting massive
particles (WIMPs) thermally produced in the early Universe, and demonstrating
that it is consistent with the measured dark matter abundance. An inconsistency
between these two quantities would instead point either towards additional
physics in the dark sector, or towards a non-standard cosmology, with a thermal
history substantially different from that of the standard cosmological model.Comment: 24 pages (+21 pages of appendices and references) and 14 figures. v2:
Updated to match JCAP version; includes minor clarifications in text and
updated reference
Event Generation and Statistical Sampling for Physics with Deep Generative Models and a Density Information Buffer
We present a study for the generation of events from a physical process with
deep generative models. The simulation of physical processes requires not only
the production of physical events, but also to ensure these events occur with
the correct frequencies. We investigate the feasibility of learning the event
generation and the frequency of occurrence with Generative Adversarial Networks
(GANs) and Variational Autoencoders (VAEs) to produce events like Monte Carlo
generators. We study three processes: a simple two-body decay, the processes
and including the decay of the top
quarks and a simulation of the detector response. We find that the tested GAN
architectures and the standard VAE are not able to learn the distributions
precisely. By buffering density information of encoded Monte Carlo events given
the encoder of a VAE we are able to construct a prior for the sampling of new
events from the decoder that yields distributions that are in very good
agreement with real Monte Carlo events and are generated several orders of
magnitude faster. Applications of this work include generic density estimation
and sampling, targeted event generation via a principal component analysis of
encoded ground truth data, anomaly detection and more efficient importance
sampling, e.g. for the phase space integration of matrix elements in quantum
field theories.Comment: 24 pages, 10 figure
A comparison of optimisation algorithms for high-dimensional particle and astrophysics applications
Optimisation problems are ubiquitous in particle and astrophysics, and
involve locating the optimum of a complicated function of many parameters that
may be computationally expensive to evaluate. We describe a number of global
optimisation algorithms that are not yet widely used in particle astrophysics,
benchmark them against random sampling and existing techniques, and perform a
detailed comparison of their performance on a range of test functions. These
include four analytic test functions of varying dimensionality, and a realistic
example derived from a recent global fit of weak-scale supersymmetry. Although
the best algorithm to use depends on the function being investigated, we are
able to present general conclusions about the relative merits of random
sampling, Differential Evolution, Particle Swarm Optimisation, the Covariance
Matrix Adaptation Evolution Strategy, Bayesian Optimisation, Grey Wolf
Optimisation, and the PyGMO Artificial Bee Colony, Gaussian Particle Filter and
Adaptive Memory Programming for Global Optimisation algorithms
3rd IML Machine Learning Workshop
We present a study for the generation of events from a physical process with generative deep learning. To simulate physical processes it is not only important to produce physical events, but also to produce the events with the right frequency of occurrence (density). We investigate the feasibility to learn the event generation and the frequency of occurrence with Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) to produce events like Monte Carlo generators. We study three toy models from high energy physics, i.e. a simple two-body decay, the processes and including the decay of the top quarks and a simulation of the detector response. We show that GANs and the standard VAE do not produce the right distributions. By buffering density information of Monte Carlo events in latent space given the encoder of a VAE we are able to construct a prior for the sampling of new events from the decoder that yields distributions that are in very good agreement with real Monte Carlo events and are generated times faster. Applications of this work include generic density estimation and sampling, targeted event generation via a principal component analysis of encoded events in the latent space and the possibility to generate better random numbers for importance sampling, e.g. for the phase space integration of matrix elements in quantum perturbation theories. The method also allows to build event generators directly from real data events
Direct or indirect electrification? A review of heat generation and road transport decarbonisation scenarios for Germany 2050
Energy scenarios provide guidance to energy policy, not least by presenting decarbonisation pathways for climate change mitigation. We review such scenarios for the example of Germany 2050, with a focus on the decarbonisation of heat generation and road transport. In this context, we characterize the role of renewable electricity and contrast two rivalling narratives: direct and indirect electrification. On the one hand, electricity directly provides heat and transport, using electric heat pumps, electric heaters, and battery electric vehicles. On the other hand, electricity, heat, and transport are indirectly linked, using gas heat pumps, gas heaters, fuel cell electric vehicles, and internal combustion engine vehicles, in combination with power-to-gas and power-to-liquid processes. To reach climate policy targets, our findings imply that energy stakeholders must (1) plan for the significant additional demand for renewable electricity for heat and road transport, (2) pave the way for system-friendly direct heat electrification, (3) be aware of technological uncertainties in the transport sector, (4) clarify the vision for decarbonisation, particularly for road transport, and (5) use holistic and more comparable scenario frameworks
Differentiable strong lensing: uniting gravity and neural nets through differentiable probabilistic programming
Contains fulltext :
221770.pdf (publisher's version ) (Open Access