Detection of extended γ -ray emission around the Geminga pulsar with H.E.S.S.

Geminga is an enigmatic radio-quiet γ-ray pulsar located at a mere 250 pc distance from Earth. Extended very-high-energy γ-ray emission around the pulsar was discovered by Milagro and later confirmed by HAWC, which are both water Cherenkov detector-based experiments. However, evidence for the Geminga pulsar wind nebula in gamma rays has long evaded detection by imaging atmospheric Cherenkov telescopes (IACTs) despite targeted observations. The detection of γ-ray emission on angular scales ≳2° poses a considerable challenge for the background estimation in IACT data analysis. With recent developments in understanding the complementary background estimation techniques of water Cherenkov and atmospheric Cherenkov instruments, the H.E.S.S. IACT array can now confirm the detection of highly extended γ-ray emission around the Geminga pulsar with a radius of at least 3° in the energy range 0.5-40 TeV. We find no indications for statistically significant asymmetries or energy-dependent morphology. A flux normalisation of (2.8 ± 0.7)×10-12 cm-2 s-1 TeV-1 at 1 TeV is obtained within a 1° radius region around the pulsar. To investigate the particle transport within the halo of energetic leptons around the pulsar, we fitted an electron diffusion model to the data. The normalisation of the diffusion coefficient obtained of D0 = 7.6-1.2+1.5×1027 cm2 s-1, at an electron energy of 100 TeV, is compatible with values previously reported for the pulsar halo around Geminga, which is considerably below the Galactic average.</p

HESS J1809−193: A halo of escaped electrons around a pulsar wind nebula?

Context. HESS J1809−193 is an unassociated very-high-energy γ-ray source located on the Galactic plane. While it has been connected to the nebula of the energetic pulsar PSR J1809−1917, supernova remnants and molecular clouds present in the vicinity also constitute possible associations. Recently, the detection of γ-ray emission up to energies of ∼100 TeV with the HAWC observatory has led to renewed interest in HESS J1809−193. Aims: We aim to understand the origin of the γ-ray emission of HESS J1809−193. Methods: We analysed 93.2 h of data taken on HESS J1809−193 above 0.27 TeV with the High Energy Stereoscopic System (H.E.S.S.), using a multi-component, three-dimensional likelihood analysis. In addition, we provide a new analysis of 12.5 yr of Fermi-LAT data above 1 GeV within the region of HESS J1809−193. The obtained results are interpreted in a time-dependent modelling framework. Results: For the first time, we were able to resolve the emission detected with H.E.S.S. into two components: an extended component (modelled as an elongated Gaussian with a 1-σ semi-major and semi-minor axis of ∼0.62° and ∼0.35°, respectively) that exhibits a spectral cutoff at ∼13 TeV, and a compact component (modelled as a symmetric Gaussian with a 1-σ radius of ∼0.1°) that is located close to PSR J1809−1917 and shows no clear spectral cutoff. The Fermi-LAT analysis also revealed extended γ-ray emission, on scales similar to that of the extended H.E.S.S. component. Conclusions: Our modelling indicates that based on its spectrum and spatial extent, the extended H.E.S.S. component is likely caused by inverse Compton emission from old electrons that form a halo around the pulsar wind nebula. The compact component could be connected to either the pulsar wind nebula or the supernova remnant and molecular clouds. Due to its comparatively steep spectrum, modelling the Fermi-LAT emission together with the H.E.S.S. components is not straightforward

A semiparametric approach to estimating reference price effects in sales response models

It is well known that store-level brand sales may not only depend on contemporaneous influencing factors like current own and competitive prices or other marketing activities, but also on past prices representing customer response to price dynamics. On the other hand, non- or semiparametric regression models have been proposed in order to accommodate potential nonlinearities in price response, and related empirical findings for frequently purchased consumer goods indicate that price effects may show complex nonlinearities, which are difficult to capture with parametric models. In this contribution, we combine nonparametric price response modeling and behavioral pricing theory. In particular, we propose a semiparametric approach to flexibly estimating price-change or reference price effects based on store-level sales data. We compare different representations for capturing symmetric vs. asymmetric and proportional vs. disproportionate price-change effects following adaptation-level and prospect theory, and further compare our flexible autoregressive model specifications to parametric benchmark models. Functional flexibility is accommodated via P-splines, and all models are estimated within a fully Bayesian framework. In an empirical study, we demonstrate that our semiparametric dynamic models provide more accurate sales forecasts for most brands considered compared to competing benchmark models that either ignore price dynamics or just include them in a parametric way

Report on the Joint Autumn Meeting of the GfKl Working Marketing and AG DANK

This article reports on the joint working group meeting of the AG MARKETING and AG DANK within the GfKl Data Science Society. The meeting was held from October 7 to 8, 2022, hosted by the Clausthal University of Technology. The presented talks included topics from a great variety of fields from quantitative marketing and data analytics and numerical classification

Development of Spatial Preferences for Counting and Picture Naming

The direction of object enumeration reflects children’s enculturation but previous work on the development of such spatial preferences has been inconsistent. Therefore, we documented directional preferences in finger counting, object counting, and picture naming for children (4 groups from 3 to 6 years, N = 104) and adults (N = 56). We found a right-side preference for finger counting in 3- to 6-year-olds and a left-side preference for counting objects and naming pictures by 6 years of age. Children were consistent in their special preferences when comparing object counting and picture naming, but not in other task pairings. Finally, spatial preferences were not related to cardinality comprehension. These results, together with other recent work, suggest a gradual development of spatial-numerical associations from early non-directional mappings into culturally constrained directional mappings

Culture shapes preschoolers’ emotion recognition but not emotion comprehension: a cross-cultural study in Germany and Singapore

Contemporary approaches suggest that emotions are shaped by culture. Children growing up in different cultures experience culture-specific emotion socialization practices. As a result, children growing up in Western societies (e.g., US or UK) rely on explicit, semantic information, whereas children from East Asian cultures (e.g., China or Japan) are more sensitive towards implicit, contextual cues when confronted with others’ emotions. The aim of the present study was to investigate two aspects of preschoolers’ emotion understanding (emotion recognition and emotion comprehension) in a cross-cultural setting. To this end, Singaporean and German preschoolers were tested with an emotion recognition task employing European-American and East Asian child’s faces and the Test of Emotion Comprehension (TEC; Pons et al., 2004). In total, 129 German and Singaporean preschoolers (mean age 5.34 years) participated. Results indicate that preschoolers were able to recognize emotions of child’s faces above chance level. In line with previous findings, Singaporean preschoolers were more accurate in recognizing emotions from facial stimuli compared to German preschoolers. Accordingly, Singaporean preschoolers outperformed German preschoolers in the Recognition component of the TEC. The overall performance in TEC did not differ between the two samples. Findings of this study provide further evidence that emotion understanding is culturally shaped in accordance with culture-specific emotion socialization practices

The peer model advantage in infants’ imitation of familiar gestures performed by differently aged models

Infants’ imitation of differently aged models has been predominately investigated with object-related actions and so far has lead to mixed evidence. Whereas some studies reported an increased likelihood of imitating peer models in contrast to adult models, other studies reported the opposite pattern of results. In the present study, 14-month-old infants were presented with four familiar gestures (e.g., clapping) that were demonstrated by differently aged televised models (peer, older child, adult). Results revealed that infants were more likely to imitate the peer model than the older child or the adult. This result is discussed with respect to a social function of imitation and the mechanism of imitating familiar behavior

H.E.S.S. follow-up observations of GRB 221009A

GRB 221009A is the brightest gamma-ray burst (GRB) ever detected. To probe the very-high-energy (VHE; >100 GeV) emission, the High Energy Stereoscopic System (H.E.S.S.) began observations 53 hr after the triggering event, when the brightness of the moonlight no longer precluded observations. We derive differential and integral upper limits using H.E.S.S. data from the third, fourth, and ninth nights after the initial GRB detection, after applying atmospheric corrections. The combined observations yield an integral energy flux upper limit of Φ UL 95 % = 9.7 × 10 − 12 erg cm − 2 s − 1 above E thr = 650 GeV. The constraints derived from the H.E.S.S. observations complement the available multiwavelength data. The radio to X-ray data are consistent with synchrotron emission from a single electron population, with the peak in the spectral energy distribution occurring above the X-ray band. Compared to the VHE-bright GRB 190829A, the upper limits for GRB 221009A imply a smaller gamma-ray to X-ray flux ratio in the afterglow. Even in the absence of a detection, the H.E.S.S. upper limits thus contribute to the multiwavelength picture of GRB 221009A, effectively ruling out an IC-dominated scenario

Application of pattern spectra and convolutional neural networks to the analysis of simulated Cherenkov Telescope Array data

The Cherenkov Telescope Array (CTA) will be the next generation gamma-ray observatory and will be the major global instrument for very-high-energy astronomy over the next decade, offering 5 - 10 x better flux sensitivity than current generation gamma-ray telescopes. Each telescope will provide a snapshot of gamma-ray induced particle showers by capturing the induced Cherenkov emission at ground level. The simulation of such events provides images that can be used as training data for convolutional neural networks (CNNs) to determine the energy of the initial gamma rays. Compared to other state-of-the-art algorithms, analyses based on CNNs promise to further enhance the performance to be achieved by CTA. Pattern spectra are commonly used tools for image classification and provide the distributions of the shapes and sizes of various objects comprising an image. The use of relatively shallow CNNs on pattern spectra would automatically select relevant combinations of features within an image, taking advantage of the 2D nature of pattern spectra. In this work, we generate pattern spectra from simulated gamma-ray events instead of using the raw images themselves in order to train our CNN for energy reconstruction. This is different from other relevant learning and feature selection methods that have been tried in the past. Thereby, we aim to obtain a significantly faster and less computationally intensive algorithm, with minimal loss of performance

Increasing the Energy-Efficiency in Vacuum-Based Package Handling Using Deep Q-Learning

Billions of packages are automatically handled in warehouses every year. The gripping systems are, however, most often oversized in order to cover a large range of different carton types, package masses, and robot motions. In addition, a targeted optimization of the process parameters with the aim of reducing the oversizing requires prior knowledge, personnel resources, and experience. This paper investigates whether the energy-efficiency in vacuum-based package handling can be increased without the need for prior knowledge of optimal process parameters. The core method comprises the variation of the input pressure for the vacuum ejector, compliant to the robot trajectory and the resulting inertial forces at the gripper-object-interface. The control mechanism is trained by applying reinforcement learning with a deep Q-agent. In the proposed use case, the energy-efficiency can be increased by up to 70% within a few hours of learning. It is also demonstrated that the generalization capability with regard to multiple different robot trajectories is achievable. In the future, the industrial applicability can be enhanced by deployment of the deep Q-agent in a decentral system, to collect data from different pick and place processes and enable a generalizable and scalable solution for energy-efficient vacuum-based handling in warehouse automation