144 research outputs found
Deep Learning Applied to the Asteroseismic Modeling of Stars with Coherent Oscillation Modes
We develop a novel method based on machine learning principles to achieve
optimal initiation of CPU-intensive computations for forward asteroseismic
modeling in a multi-D parameter space. A deep neural network is trained on a
precomputed asteroseismology grid containing about 62 million coherent
oscillation-mode frequencies derived from stellar evolution models. These
models are representative of the core-hydrogen burning stage of
intermediate-mass and high-mass stars. The evolution models constitute a 6D
parameter space and their predicted low-degree pressure- and gravity-mode
oscillations are scanned, using a genetic algorithm. A software pipeline is
created to find the best fitting stellar parameters for a given set of observed
oscillation frequencies. The proposed method finds the optimal regions in the
6D parameters space in less than a minute, hence providing the optimal starting
point for further and more detailed forward asteroseismic modeling in a
high-dimensional context. We test and apply the method to seven pulsating stars
that were previously modeled asteroseismically by classical grid-based forward
modeling based on a statistic and obtain good agreement with past
results. Our deep learning methodology opens up the application of
asteroseismic modeling in +6D parameter space for thousands of stars pulsating
in coherent modes with long lifetimes observed by the space telescope
and to be discovered with the TESS and PLATO space missions, while applications
so far were done star-by-star for only a handful of cases. Our method is open
source and can be used by anyone freely.Comment: Accepted for publication in PASP Speciale Volume on Machine Learnin
Comparing Galactic Center MSSM dark matter solutions to the Reticulum II gamma-ray data
Observations with the Fermi Large Area Telescope (LAT) indicate a possible
small photon signal originating from the dwarf galaxy Reticulum II that exceeds
the expected background between 2 GeV and 10 GeV. We have investigated two
specific scenarios for annihilating WIMP dark matter within the
phenomenological Minimal Supersymmetric Standard Model (pMSSM) framework as a
possible source for these photons. We find that the same parameter ranges in
pMSSM as reported by an earlier paper to be consistent with the Galactic center
excess, is also consistent with the excess observed in Reticulum II, resulting
in a J-factor of . This J-factor is consistent with
GeVcm,
which is derived using an optimized spherical Jeans analysis of kinematic data
obtained from the Michigan/Magellan Fiber System (M2FS).Comment: 4 pages, 2 figures, accepted in JCA
Analyzing {\gamma}-rays of the Galactic Center with Deep Learning
We present a new method to interpret the -ray data of our inner
Galaxy as measured by the Fermi Large Area Telescope (Fermi LAT). We train and
test convolutional neural networks with simulated Fermi-LAT images based on
models tuned to real data. We use this method to investigate the origin of an
excess emission of GeV -rays seen in previous studies. Interpretations
of this excess include rays created by the annihilation of dark matter
particles and rays originating from a collection of unresolved point
sources, such as millisecond pulsars. Our new method allows precise
measurements of the contribution and properties of an unresolved population of
-ray point sources in the interstellar diffuse emission model.Comment: 24 pages, 11 figure
SPOT: Open Source framework for scientific data repository and interactive visualization
SPOT is an open source and free visual data analytics tool for
multi-dimensional data-sets. Its web-based interface allows a quick analysis of
complex data interactively. The operations on data such as aggregation and
filtering are implemented. The generated charts are responsive and OpenGL
supported. It follows FAIR principles to allow reuse and comparison of the
published data-sets. The software also support PostgreSQL database for
scalability
A description of the Galactic Center excess in the Minimal Supersymmetric Standard Model
Observations with the Fermi Large Area Telescope (LAT) indicate an excess in
gamma rays originating from the center of our Galaxy. A possible explanation
for this excess is the annihilation of Dark Matter particles. We have
investigated the annihilation of neutralinos as Dark Matter candidates within
the phenomenological Minimal Supersymmetric Standard Model (pMSSM). An
iterative particle filter approach was used to search for solutions within the
pMSSM. We found solutions that are consistent with astroparticle physics and
collider experiments, and provide a fit to the energy spectrum of the excess.
The neutralino is a Bino/Higgsino or Bino/Wino/Higgsino mixture with a mass in
the range ~GeV or ~GeV annihilating into W bosons. A third
solutions is found for a neutralino of mass ~GeV annihilating into top
quarks. The best solutions yield a Dark Matter relic density . These pMSSM solutions make clear forecasts for LHC, direct and indirect
DM detection experiments. If the MSSM explanation of the excess seen by
Fermi-LAT is correct, a DM signal might be discovered soon.Comment: Large extension of previous paper: 2 more solutions found in the MSSM
(Bino-Higgsino, Bino-Wino-Higgsino into WW and Bino into ttbar), added
description on extra fit uncertainties, added description on flavor
observables, added discussion on dwarf limit
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
Understanding phase contrast artefacts in micro computed absorption tomography
Phase contrast imaging is a technique which captures objects with little or no light absorption. This is possible due to the wave nature of light, i.e., diffraction. In computerised tomography, the aim is most often to reconstruct the light absorption property of objects but many objects can not be imaged without obtaining a mix of both absorption and phase, this is especially true for weakly absorbing objects at high resolution.
Hence, phase contrast is usually considered an unwanted artefact which should be removed. Traditionally this is done directly on the projection data prior to the filtered back projection algorithm and the filter settings are derived from the physical setup of the imaging device.
In this paper we show how these operations can be carried out on the reconstructed data, without access to the projection images, which yields much flexibility over previous approaches. Especially, filtering can be applied to small regions of interest which simplifies fine tuning of parameters, and some low pass filtering can be avoided which is inherent in previous methods. We will also show the filter parameters can be estimated from step edges in the reconstructed images
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