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TeV gamma-ray observations of the young synchrotron-dominated SNRs G1.9+0.3 and G330.2+1.0 with HESS
(2014)
The non-thermal nature of the X-ray emission from the shell-type supernova remnants (SNRs) G1.9+0.3 and G330.2+1.0 is an indication of intense particle acceleration in the shock fronts of both objects. This suggests that the SNRs are prime candidates for very-high-energy (VHE; E > 0.1 TeV) gamma-ray observations. G1.9+0.3, recently established as the youngest known SNR in the Galaxy, also offers a unique opportunity to study the earliest stages of SNR evolution in the VHE domain. The purpose of this work is to probe the level of VHE gamma-ray emission from both SNRs and use this to constrain their physical properties. Observations were conducted with the H. E. S. S. (High Energy Stereoscopic System) Cherenkov Telescope Array over a more than six-year period spanning 2004-2010. The obtained data have effective livetimes of 67 h for G1.9+0.3 and 16 h for G330.2+1.0. The data are analysed in the context of the multiwavelength observations currently available and in the framework of both leptonic and hadronic particle acceleration scenarios. No significant gamma-ray signal from G1.9+0.3 or G330.2+1.0 was detected. Upper limits (99 per cent confidence level) to the TeV flux from G1.9+0.3 and G330.2+1.0 for the assumed spectral index Gamma = 2.5 were set at 5.6 x 10(-1)3 cm(-2) s(-1) above 0.26 TeV and 3.2 x 10(-12) cm(-2) s(-1) above 0.38 TeV, respectively. In a one-zone leptonic scenario, these upper limits imply lower limits on the interior magnetic field to B-G1.9 greater than or similar to 12 mu G for G1.9+0.3 and to B-G330 greater than or similar to 8 mu G for G330.2+1.0. In a hadronic scenario, the low ambient densities and the large distances to the SNRs result in very low predicted fluxes, for which the H.E.S.S. upper limits are not constraining.
The gamma-ray spectrum of the low-frequency-peaked BL Lac (LBL) object AP Librae is studied, following the discovery of very-high-energy (VHE; E > 100 GeV) gamma-ray emission up to the TeV range by the H.E.S.S. experiment. Thismakes AP Librae one of the few VHE emitters of the LBL type. The measured spectrum yields a flux of (8.8 +/- 1.5(stat) +/- 1.8(sys)) x 10(-12) cm(-2) s(-1) above 130 GeV and a spectral index of Gamma = 2.65 +/- 0.19(stat) +/- 0.20(sys). This study also makes use of Fermi-LAT observations in the high energy (HE, E > 100 MeV) range, providing the longest continuous light curve (5 years) ever published on this source. The source underwent a flaring event between MJD 56 306-56 376 in the HE range, with a flux increase of a factor of 3.5 in the 14 day bin light curve and no significant variation in spectral shape with respect to the low-flux state. While the H.E.S.S. and (low state) Fermi-LAT fluxes are in good agreement where they overlap, a spectral curvature between the steep VHE spectrum and the Fermi-LAT spectrum is observed. The maximum of the gamma-ray emission in the spectral energy distribution is located below the GeV energy range.
Axionlike particles (ALPs) are hypothetical light (sub-eV) bosons predicted in some extensions of the Standard Model of particle physics. In astrophysical environments comprising high-energy gamma rays and turbulent magnetic fields, the existence of ALPs can modify the energy spectrum of the gamma rays for a sufficiently large coupling between ALPs and photons. This modification would take the form of an irregular behavior of the energy spectrum in a limited energy range. Data from the H. E. S. S. observations of the distant BL Lac object PKS 2155 - 304 (z = 0.116) are used to derive upper limits at the 95% C. L. on the strength of the ALP coupling to photons, g(gamma a) < 2.1 x 10(-11) GeV-1 for an ALP mass between 15 and 60 neV. The results depend on assumptions on the magnetic field around the source, which are chosen conservatively. The derived constraints apply to both light pseudoscalar and scalar bosons that couple to the electromagnetic field.
HESS J1640-465 - an exceptionally luminous TeV gamma-ray supernova remnant (vol 439, pg 2828, 2014)
(2014)
Entity resolution on-demand
(2022)
Entity Resolution (ER) aims to identify and merge records that refer to the same real-world entity.
ER is typically employed as an expensive cleaning step on the entire data before consuming it. Yet, determining which entities are useful once cleaned depends solely on the user's application, which may need only a fraction of them.
For instance, when dealing with Web data, we would like to be able to filter the entities of interest gathered from multiple sources without cleaning the entire, continuously-growing data.
Similarly, when querying data lakes, we want to transform data on-demand and return the results in a timely manner-a fundamental requirement of ELT (Extract-Load-Transform) pipelines.
We propose BrewER, a framework to evaluate SQL SP queries on dirty data while progressively returning results as if they were issued on cleaned data. BrewER tries to focus the cleaning effort on one entity at a time, following an ORDER BY predicate. Thus, it inherently supports top-k and stop-and-resume execution.
For a wide range of applications, a significant amount of resources can be saved. We exhaustively evaluate and show the efficacy of BrewER on four real-world datasets.
"Bad" data has a direct impact on 88% of companies, with the average company losing 12% of its revenue due to it.
Duplicates - multiple but different representations of the same real-world entities are among the main reasons for poor data quality, so finding and configuring the right deduplication solution is essential.
Existing data matching benchmarks focus on the quality of matching results and neglect other important factors, such as business requirements. Additionally, they often do not support the exploration of data matching results.
To address this gap between the mere counting of record pairs vs. a comprehensive means to evaluate data matching solutions, we present the Frost platform.
It combines existing benchmarks, established quality metrics, cost and effort metrics, and exploration techniques, making it the first platform to allow systematic exploration to understand matching results.
Frost is implemented and published in the open-source application Snowman, which includes the visual exploration of matching results, as shown in Figure 1.
Proceedings of the HPI Research School on Service-oriented Systems Engineering 2020 Fall Retreat
(2021)
Design and Implementation of service-oriented architectures imposes a huge number of research questions from the fields of software engineering, system analysis and modeling, adaptability, and application integration. Component orientation and web services are two approaches for design and realization of complex web-based system. Both approaches allow for dynamic application adaptation as well as integration of enterprise application.
Service-Oriented Systems Engineering represents a symbiosis of best practices in object-orientation, component-based development, distributed computing, and business process management. It provides integration of business and IT concerns.
The annual Ph.D. Retreat of the Research School provides each member the opportunity to present his/her current state of their research and to give an outline of a prospective Ph.D. thesis. Due to the interdisciplinary structure of the research school, this technical report covers a wide range of topics. These include but are not limited to: Human Computer Interaction and Computer Vision as Service; Service-oriented Geovisualization Systems; Algorithm Engineering for Service-oriented Systems; Modeling and Verification of Self-adaptive Service-oriented Systems; Tools and Methods for Software Engineering in Service-oriented Systems; Security Engineering of Service-based IT Systems; Service-oriented Information Systems; Evolutionary Transition of Enterprise Applications to Service Orientation; Operating System Abstractions for Service-oriented Computing; and Services Specification, Composition, and Enactment.
This vision article outlines the main building blocks of what we term AI Compliance, an effort to bridge two complementary research areas: computer science and the law.
Such research has the goal to model, measure, and affect the quality of AI artifacts, such as data, models, and applications, to then facilitate adherence to legal standards.