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

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

Inter- versus intramodal integration in sensorimotor synchronization: a combined behavioral and magnetoencephalographic study

Although the temporal occurrence of the pacing signal is predictable in sensorimotor synchronization tasks, normal subjects perform on-the-beat-tapping to an isochronous auditory metronome with an anticipatory error. This error originates from an intermodal task, that is, subjects have to bring information from the auditory and tactile modality to coincide. The aim of the present study was to illuminate whether the synchronization error is a finding specific to an intermodal timing task and whether the underlying cortical mechanisms are modality-specific or supramodal. We collected behavioral data and cortical evoked responses by magneto-encephalography (MEG) during performance of cross- and unimodal tapping-tasks. As expected, subjects showed negative asynchrony in performing an auditorily paced tapping task. However, no asynchrony emerged during tactile pacing, neither during pacing at the opposite finger nor at the toe. Analysis of cortical signals resulted in a three dipole model best explaining tap-contingent activity in all three conditions. The temporal behavior of the sources was similar between the conditions and, thus, modality independent. The localization of the two earlier activated sources was modality-independent as well whereas location of the third source varied with modality. In the auditory pacing condition it was localized in contralateral primary somatosensory cortex, during tactile pacing it was localized in contralateral posterior parietal cortex. In previous studies with auditory pacing the functional role of this third source was contradictory: A special temporal coupling pattern argued for involvement of the source in evaluating the temporal distance between tap and click whereas subsequent data gave no evidence for such an interpretation. Present data shed new light on this question by demonstrating differences between modalities in the localization of the third source with similar temporal behavior

Signal-background separation and energy reconstruction of gamma rays using pattern spectra and convolutional neural networks for the Small-Sized Telescopes of the Cherenkov Telescope Array

Imaging Atmospheric Cherenkov Telescopes (IACTs) detect very high-energy gamma rays from ground level by capturing the Cherenkov light of the induced particle showers. Convolutional neural networks (CNNs) can be trained on IACT camera images of such events to differentiate the signal from the background and to reconstruct the energy of the initial gamma ray. Pattern spectra provide a 2-dimensional histogram of the sizes and shapes of features comprising an image and they can be used as an input for a CNN to significantly reduce the computational power required to train it. In this work, we generate pattern spectra from simulated gamma-ray and proton images to train a CNN for signal-background separation and energy reconstruction for the Small-Sized Telescopes (SSTs) of the Cherenkov Telescope Array (CTA). A comparison of our results with a CNN directly trained on CTA images shows that the pattern spectra-based analysis is about a factor of three less computationally expensive but not able to compete with the performance of the CTA images-based analysis. Thus, we conclude that the CTA images must be comprised of additional information not represented by the pattern spectra.Comment: 10 pages, 9 figures, submitted to Nuclear Instruments and Methods in Physics Research - section

Action–effect anticipation in infant action control

There is increasing evidence that action effects play a crucial role in action understanding and action control not only in adults but also in infants. Most of the research in infants focused on the learning of action–effect contingencies or how action effects help infants to infer goals in other persons’ actions. In contrast, the present research aimed at demonstrating that infants control their own actions by action–effect anticipation once they know about specific action–effect relations. About 7 and 9-month olds observed an experimenter demonstrating two actions that differed regarding the action–effect assignment. Either a red-button press or a blue-button press or no button press elicited interesting acoustical and visual effects. The 9-month olds produced the effect action at first, with shorter latency and longer duration sustaining a direct impact of action–effect anticipation on action control. In 7-month olds the differences due to action–effect manipulation were less profound indicating developmental changes at this age