11 research outputs found
Testing the Origin of High-Energy Cosmic Rays
Recent accurate measurements of cosmic-ray (CR) protons and nuclei by ATIC-2,
CREAM, and PAMELA reveal: a) unexpected spectral hardening in the spectra of CR
species above a few hundred GeV per nucleon, b) a harder spectrum of He
compared to protons, and c) softening of the CR spectra just below the break
energy. These newly-discovered features may offer a clue to the origin of the
observed high-energy Galactic CRs. We discuss possible interpretations of these
spectral features and make predictions for the secondary CR fluxes and
secondary to primary ratios, anisotropy of CRs, and diffuse Galactic
{\gamma}-ray emission in different phenomenological scenarios. Our predictions
can be tested by currently running or near-future high-energy astrophysics
experiments.Comment: 23 pages, 14 color figures. Accepted for publication in Ap
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
Mind the gap: The discrepancy between simulation and reality drives interpretations of the Galactic Center Excess
The origin of the so-called Galactic Center Excess in GeV gamma rays has been debated for more than 10 years. What makes this excess so interesting is the possibility of interpreting it as additional radiation consistent with that expected from dark matter annihilation. Alternatively, the excess could come from undetected point sources. In this work, we examine the following questions: Since the majority of the previously reported interpretations of this excess are highly dependent on the simulation, how does the model used for the simulation affect the interpretations? Are such uncertainties taken into account? When different models lead to different conclusions, there may be a general gap between these simulations and reality that influences our conclusions. To investigate these questions, we build an ultra-fast and powerful inference pipeline based on convolutional deep ensemble networks and test the interpretations with a wide range of different models to simulate the excess. We find that our conclusions (dark matter or not) strongly depend on the type of simulation and that this is not revealed by systematic uncertainties. Furthermore, we measure whether reality lies in the simulation parameter space and conclude that there is a gap to reality in all simulated models. Our approach offers a means to assess the severity of the reality gap in future works. Our work questions the validity of conclusions (dark matter) drawn about the GCE in other works: Has the reality gap been closed and at the same time is the model correct
Mind the gap: The discrepancy between simulation and reality drives interpretations of the Galactic Center Excess
The origin of the so-called Galactic Center Excess in GeV gamma rays has been debated for more than 10 years. What makes this excess so interesting is the possibility of interpreting it as additional radiation consistent with that expected from dark matter annihilation. Alternatively, the excess could come from undetected point sources. In this work, we examine the following questions: Since the majority of the previously reported interpretations of this excess are highly dependent on the simulation, how does the model used for the simulation affect the interpretations? Are such uncertainties taken into account? When different models lead to different conclusions, there may be a general gap between these simulations and reality that influences our conclusions. To investigate these questions, we build an ultra-fast and powerful inference pipeline based on convolutional deep ensemble networks and test the interpretations with a wide range of different models to simulate the excess. We find that our conclusions (dark matter or not) strongly depend on the type of simulation and that this is not revealed by systematic uncertainties. Furthermore, we measure whether reality lies in the simulation parameter space and conclude that there is a gap to reality in all simulated models. Our approach offers a means to assess the severity of the reality gap in future works. Our work questions the validity of conclusions (dark matter) drawn about the GCE in other works: Has the reality gap been closed and at the same time is the model correct
Identification of point sources in gamma rays using U-shaped convolutional neural networks and a data challenge
International audienceContext. At GeV energies, the sky is dominated by the interstellar emission from the Galaxy. With limited statistics and spatial resolution, accurately separating point sources is therefore challenging.Aims. Here we present the first application of deep learning based algorithms to automatically detect and classify point sources from gamma-ray data. For concreteness we refer to this approach as AutoSourceID.Methods. To detect point sources, we utilized U-shaped convolutional networks for image segmentation and k-means for source clustering and localization. We also explored the Centroid-Net algorithm, which is designed to find and count objects. Using two algorithms allows for a cross check of the results, while a combination of their results can be used to improve performance. The training data are based on 9.5 years of exposure from The Fermi Large Area Telescope (Fermi-LAT) and we used source properties of active galactic nuclei (AGNs) and pulsars (PSRs) from the fourth Fermi-LAT source catalog in addition to several models of background interstellar emission. The results of the localization algorithm are fed into a classification neural network that is trained to separate the three general source classes (AGNs, PSRs, and FAKE sources).Results. We compared our localization algorithms qualitatively with traditional methods and find them to have similar detection thresholds. We also demonstrate the robustness of our source localization algorithms to modifications in the interstellar emission models, which presents a clear advantage over traditional methods. The classification network is able to discriminate between the three classes with typical accuracy of ∼70%, as long as balanced data sets are used in classification training. We published online our training data sets and analysis scripts and invite the community to join the data challenge aimed to improve the localization and classification of gamma-ray point sources.Key words: catalogs / gamma rays: general / astroparticle physics / methods: numerical / methods: data analysis / techniques: image processing⋆ https://github.com/bapanes/AutoSourceI
AutoSourceID-Light. Fast Optical Source Localization via U-Net and Laplacian of Gaussian
. With the ever-increasing survey speed of optical wide-field
telescopes and the importance of discovering transients when they are still
young, rapid and reliable source localization is paramount. We present
AutoSourceID-Light (ASID-L), an innovative framework that uses computer vision
techniques that can naturally deal with large amounts of data and rapidly
localize sources in optical images. . We show that the
AutoSourceID-Light algorithm based on U-shaped networks and enhanced with a
Laplacian of Gaussian filter (Chen et al. 1987) enables outstanding
performances in the localization of sources. A U-Net (Ronneberger et al. 2015)
network discerns the sources in the images from many different artifacts and
passes the result to a Laplacian of Gaussian filter that then estimates the
exact location. . Application on optical images of the
MeerLICHT telescope demonstrates the great speed and localization power of the
method. We compare the results with the widely used SExtractor (Bertin &
Arnouts 1996) and show the out-performances of our method. AutoSourceID-Light
rapidly detects more sources not only in low and mid crowded fields, but
particularly in areas with more than 150 sources per square arcminute