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IceCube Search for Neutrino Emission from X-Ray Bright Seyfert Galaxies
Get PDFThe recent IceCube detection of TeV neutrino emission from the nearby active galaxy NGC 1068 suggests that active galactic nuclei (AGNs) could make a sizable contribution to the diffuse flux of astrophysical neutrinos. The absence of TeV γ-rays from NGC 1068 indicates neutrino production in the vicinity of the supermassive black hole, where the high radiation density leads to γ-ray attenuation. Therefore, any potential neutrino emission from similar sources is not expected to correlate with high-energy γ-rays. Disk-corona models predict neutrino emission from Seyfert galaxies to correlate with keV X-rays because they are tracers of coronal activity. Using through-going track events from the Northern Sky recorded by IceCube between 2011 and 2021, we report results from a search for individual and aggregated neutrino signals from 27 additional Seyfert galaxies that are contained in the Swift\u27s Burst Alert Telescope AGN Spectroscopic Survey. Besides the generic single power law, we evaluate the spectra predicted by the disk-corona model assuming stochastic acceleration parameters that match the measured flux from NGC 1068. Assuming all sources to be intrinsically similar to NGC 1068, our findings constrain the collective neutrino emission from X-ray bright Seyfert galaxies in the northern sky, but, at the same time, show excesses of neutrinos that could be associated with the objects NGC 4151 and CGCG 420-015. These excesses result in a 2.7σ significance with respect to background expectations
Studying thermal radiation with T matrices
Get PDFWe introduce a basic formalism for computing thermal radiation by combining Waterman\u27s T-matrix method with an algebraic approach to light-matter interactions. The formalism applies to nanoparticles, clusters thereof, and also molecules. In exemplary applications, we explore how a chiral structure can induce an imbalance in the circular polarization of thermal radiation. While the imbalance is rather small for a chiral molecule such as R-BINOL, a much larger imbalance is observed for an optimized silver helix of approximately 200 nm in size. Besides the directional Kirchhoff law used in this article, the formalism is suitable for implementing more nuanced theories, and also provides a straightforward path to the computation of thermal radiation spectra of astronomical objects moving at relativistic speeds with respect to the measurement devices
Temporal sequence-based object detection and action recognition for mobile machinery on construction sites
Get PDFAutomation of mobile machinery is critical in the construction industry to improve efficiency and ensure safety. Perception technologies, particularly for detecting and monitoring the actions of construction machinery, are essential for optimizing workflows and mitigating accident risks. However, the complex nature of construction environments, the variety of machines, and the dynamic interactions at construction sites pose significant challenges for reliable object detection and action recognition. This study introduces a deep learning approach using temporal vision information for object detection and action recognition of mobile machinery in construction environments. In particular, a novel strategy called Integrated YL-SF is proposed, which integrates the YOLOv8 framework with the SlowFast model enhanced by Transformers to achieve robust action recognition and motion analysis of construction machinery. The proposed method is evaluated on a custom dataset with a variety of machine types and real-world operating environments, and it is benchmarked against the standard YOLOv8 model. The results show that the Integrated YL-SF framework outperforms existing methods and effectively addresses challenges such as dynamic scenarios, object occlusion, and multi-machine interactions in complex environments
Comparison of Machine Learning and MPC Methods for Control of Home Battery Storage Systems in Distribution Grids
Get PDFFlexible Positionierung von seismischen Quellen bei der FD-Modellierung
Get PDFIn dieser Arbeit wird die 2D-Finite-Differenzen-Modellierung auf einem geschachtelten
Gitter und die Möglichkeiten der flexiblen Positionierung von Quellen zwischen Gitter-
knoten, sowie deren Genauigkeit im Vergleich zu anderen auftretenden Unsicherheiten
betrachtet. Die Berechnungen erfolgen anhand eines homogenes und elastischen Modells
und werden anschließend mit einer analytisch berechneten Referenzlösung verglichen. Nach
einem Konvergenztest, folgt eine Dispersionskorrektur, da dies der größte Verursacher von
Abweichungen von der Referenzlösung ist und somit sonst kein Vergleich verschiedener
Quellinterpolationsmethoden möglich ist. Der Vergleich der Sinc-Funktion mit der FD-
konsistenten Methode ergibt nur geringfügige Unterschiede, die in einem komplizierteren
Fall, in dem die Dispersionskorrektur nicht einfach möglich ist, kaum noch relevanten
Einfluss auf die Ergebnisse hat
End-to-End Detector Optimization with Diffusion Models: A Case Study in Sampling Calorimeters
No full textRecent advances in machine learning have opened new avenues for optimizing
detector designs in high-energy physics, where the complex interplay of geometry, materi-
als, and physics processes has traditionally posed a significant challenge. In this work, we
introduce the end-to-end. AI Detector Optimization framework (AIDO), which leverages
a diffusion model as a surrogate for the full simulation and reconstruction chain, enabling
gradient-based design exploration in both continuous and discrete parameter spaces. Al-
though this framework is applicable to a broad range of detectors, we illustrate its power
using the specific example of a sampling calorimeter, focusing on charged pions and photons
as representative incident particles. Our results demonstrate that the diffusion model effec-
tively captures critical performance metrics for calorimeter design, guiding the automatic
search for a layer arrangement and material composition that align with known calorimeter
principles. The success of this proof-of-concept study provides a foundation for the future ap-
plications of end-to-end optimization to more complex detector systems, offering a promising
path toward systematically exploring the vast design space in next-generation experiments
Pressure Dependence of Structural Behavior in the Polymorphs of Fe(PM–BiA)2(NCS)2
No full textThe pressure dependence of structural behavior in the orthorhombic (Pccn, PI) and monoclinic (P2₁/c, PII) polymorphs of the compound [Fe(PM-BiA)₂(NCS)₂], where PM–BiA = (N–(2′–pyridylmethylene)–4-amino–bi–pheynyl), is studied with synchrotron single-crystal X-ray diffraction and vibrational spectroscopy. Both polymorphs are stable up to ∼1.5 GPa, with a spin state transition occurring only in polymorph PII under hydrostatic conditions as documented by single-crystal synchrotron diffraction. The diffraction data also provide evidence of the formation of superstructures for both PI, with a doubled c axis, and PII, with a doubled b axis, on applying pressures above 2 GPa. The LS and HS states seem to coexist at high-pressures for both polymorphs studied with synchrotron infrared spectroscopy at quasi-hydrostatic conditions. Such results indicate that the occurrence of spin-crossover transformations in [Fe(PM-BiA)₂(NCS)₂] might strongly depend on the stress in the sample
Gold from copper mining as a case study for allocation in life cycle assessment
No full textAllocation in multi-output life cycle assessment (LCA) systems has been extensively discussed in
literature. Common approaches to address allocation include system subdivision or expansion,
physical cause-and-effect relationships, and distribution based on allocation factors such as
product mass or revenue. In this study, we critically examine these allocation methods using the
example of the Finnish copper–gold mine Kevitsa. We demonstrate that the prioritization of
allocation methods prescribed by ISO 14044 is often inapplicable. We argue that this issue is partly
rooted in the strong association of LCA with natural sciences, such as environmental science,
toxicology, mathematics, and physics. This alignment frequently results in allocation choices that
fail to reflect the benefit of the product system, as benefit is not an inherent property of a material
but rather a subjective preference within the economic system. For illustration, we use CO as a
numerical example in one impact category, though allocation plays an equally important role
across all impact categories. Moreover, we contend that for processes producing a primary product
alongside valuable by-products, a case differentiation instead of a rigid hierarchy should be
considered—a perspective not adequately captured by the current allocation standard. We
advocate for a more transparent and comparable allocation framework in LCA that prioritizes the
benefit of the product system over strict adherence to natural law
Rapid AI-based generation of coverage paths for dispensing applications
No full textCoverage Path Planning of Thermal Interface Materials (TIM) plays a crucial role in the design of power electronics and electronic control units. Up to now, this is done manually by experts or by using optimization approaches with a high computational effort. We propose the novel AI-based approach DeepTIM to generate dispense paths for TIM and similar dispensing applications. It is a drop-in replacement for optimization-based approaches. An Artificial Neural Network (ANN) receives the target cooling area as input and directly outputs the dispense path. Our proposed setup does not require labels and we show its feasibility on multiple target areas. The resulting dispense paths can be directly transferred to automated manufacturing equipment and do not exhibit air entrapments. The approach of using an ANN to predict process parameters for a desired target state in real-time could potentially be transferred to other manufacturing processes