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We present a detailed spectroscopic and timing analysis of X-ray observations of the bright pulsar PSR B0656+14. The observations were obtained simultaneously with eROSITA and XMM-Newton during the calibration and performance verification phase of the Spektrum-Roentgen-Gamma mission (SRG). The analysis of the 100 ks deep observation of eROSITA is supported by archival observations of the source, including XMM-Newton, NuSTAR, and NICER. Using XMM-Newton and NICER, we first established an X-ray ephemeris for the time interval 2015 to 2020, which connects all X-ray observations in this period without cycle count alias and phase shifts. The mean eROSITA spectrum clearly reveals an absorption feature originating from the star at 570 eV with a Gaussian sigma of about 70 eV that was tentatively identified in a previous long XMM-Newton observation. A second previously discussed absorption feature occurs at 260-265 eV and is described here as an absorption edge. It could be of atmospheric or of instrumental origin. These absorption features are superposed on various emission components that are phenomenologically described here as the sum of hot (120 eV) and cold (65 eV) blackbody components, both of photospheric origin, and a power law with photon index Gamma = 2 from the magnetosphere. We created energy-dependent light curves and phase-resolved spectra with a high signal-to-noise ratio. The phase-resolved spectroscopy reveals that the Gaussian absorption line at 570 eV is clearly present throughout similar to 60% of the spin cycle, but it is otherwise undetected. Likewise, its parameters were found to be dependent on phase. The visibility of the line strength coincides in phase with the maximum flux of the hot blackbody. If the line originates from the stellar surface, it nevertheless likely originates from a different location than the hot polar cap. We also present three families of model atmospheres: a magnetized atmosphere, a condensed surface, and a mixed model. They were applied to the mean observed spectrum, whose continuum fit the observed data well. The atmosphere model, however, predicts distances that are too short. For the mixed model, the Gaussian absorption may be interpreted as proton cyclotron absorption in a field as high as 10(14) G, which is significantly higher than the field derived from the moderate observed spin-down.
A panoply of new tools for tracking single particles and molecules has led to an explosion of experimental data, leading to novel insights into physical properties of living matter governing cellular development and function, health and disease. In this Perspective, we present tools to investigate the dynamics and mechanics of living systems from the molecular to cellular scale via single-particle techniques. In particular, we focus on methods to measure, interpret, and analyse complex data sets that are associated with forces, materials properties, transport, and emergent organisation phenomena within biological and soft-matter systems. Current approaches, challenges, and existing solutions in the associated fields are outlined in order to support the growing community of researchers at the interface of physics and the life sciences. Each section focuses not only on the general physical principles and the potential for understanding living matter, but also on details of practical data extraction and analysis, discussing limitations, interpretation, and comparison across different experimental realisations and theoretical frameworks. Particularly relevant results are introduced as examples. While this Perspective describes living matter from a physical perspective, highlighting experimental and theoretical physics techniques relevant for such systems, it is also meant to serve as a solid starting point for researchers in the life sciences interested in the implementation of biophysical methods.
Modern single-particle-tracking techniques produce extensive time-series of diffusive motion in a wide variety of systems, from single-molecule motion in living-cells to movement ecology. The quest is to decipher the physical mechanisms encoded in the data and thus to better understand the probed systems. We here augment recently proposed machine-learning techniques for decoding anomalous-diffusion data to include an uncertainty estimate in addition to the predicted output. To avoid the Black-Box-Problem a Bayesian-Deep-Learning technique named Stochastic-Weight-Averaging-Gaussian is used to train models for both the classification of the diffusionmodel and the regression of the anomalous diffusion exponent of single-particle-trajectories. Evaluating their performance, we find that these models can achieve a wellcalibrated error estimate while maintaining high prediction accuracies. In the analysis of the output uncertainty predictions we relate these to properties of the underlying diffusion models, thus providing insights into the learning process of the machine and the relevance of the output.
Modern single-particle-tracking techniques produce extensive time-series of diffusive motion in a wide variety of systems, from single-molecule motion in living-cells to movement ecology. The quest is to decipher the physical mechanisms encoded in the data and thus to better understand the probed systems. We here augment recently proposed machine-learning techniques for decoding anomalous-diffusion data to include an uncertainty estimate in addition to the predicted output. To avoid the Black-Box-Problem a Bayesian-Deep-Learning technique named Stochastic-Weight-Averaging-Gaussian is used to train models for both the classification of the diffusion model and the regression of the anomalous diffusion exponent of single-particle-trajectories. Evaluating their performance, we find that these models can achieve a well-calibrated error estimate while maintaining high prediction accuracies. In the analysis of the output uncertainty predictions we relate these to properties of the underlying diffusion models, thus providing insights into the learning process of the machine and the relevance of the output. <br /> Diffusive motions in complex environments such as living biological cells or soft matter systems can be analyzed with single-particle-tracking approaches, where accuracy of output may vary. The authors involve a machine-learning technique for decoding anomalous-diffusion data and provide an uncertainty estimate together with predicted output.
Sprache
Englisch
Modern single-particle-tracking techniques produce extensive time-series of diffusive motion in a wide variety of systems, from single-molecule motion in living-cells to movement ecology. The quest is to decipher the physical mechanisms encoded in the data and thus to better understand the probed systems. We here augment recently proposed machine-learning techniques for decoding anomalous-diffusion data to include an uncertainty estimate in addition to the predicted output. To avoid the Black-Box-Problem a Bayesian-Deep-Learning technique named Stochastic-Weight-Averaging-Gaussian is used to train models for both the classification of the diffusionmodel and the regression of the anomalous diffusion exponent of single-particle-trajectories. Evaluating their performance, we find that these models can achieve a wellcalibrated error estimate while maintaining high prediction accuracies. In the analysis of the output uncertainty predictions we relate these to properties of the underlying diffusion models, thus providing insights into the learning process of the machine and the relevance of the output.
In the present work, electron backscatter diffraction was used to determine the microscopic dislocation structures generated during creep (with tests interrupted at the steady state) in pure 99.8% aluminium. This material was investigated at two different stress levels, corresponding to the power-law and power-law breakdown regimes. The results show that the formation of subgrain cellular structures occurs independently of the crystallographic orientation. However, the density of these cellular structures strongly depends on the grain crystallographic orientation with respect to the tensile axis direction, with (111) grains exhibiting the highest densities at both stress levels. It is proposed that this behaviour is due to the influence of intergranular stresses, which is different in (111) and (001) grains.
Here we show that microgels trapped at a solid wall can issue liquid flow and transport over distances several times larger than the particle size.
The microgel consists of cross-linked poly(N-isopropylacrylamide-co-acrylic acid) (PNIPAM-AA) polymer chains loaded with cationic azobenzene-containing surfactant, which can assume either a trans-or a cis-state depending on the wavelength of the applied irradiation. The microgel, being a selective absorber of trans-isomers, responds by changing its volume under irradiation with light of appropriate wavelength at which the cis-isomers of the surfactant molecules diffuse out of the particle interior.
Together with the change in particle size, the expelled cis-isomers form an excess of the concentration and subsequent gradient in osmotic pressure generating a halo of local light-driven diffusioosmotic (l-LDDO) flow. The direction and the strength of the l-LDDO depends on the intensity and irradiation wavelength, as well as on the amount of surfactant absorbed by the microgel.
The flow pattern around a microgel is directed radially outward and can be maintained quasi-indefinitely under exposure to blue light when the trans-/cis-ratio is 2/1, establishing a photostationary state.
Irradiation with UV light, on the other hand, generates a radially transient flow pattern, which inverts from inward to outward over time at low intensities.
By measuring the displacement of tracer particles around neutral microgels during a temperature-induced collapse, we can exclude that a change in particle shape itself causes the flow, i.e., just by expulsion or uptake of water.
Ultimately, it is its ability to selectively absorb two isomers of photosensitive surfactant under different irradiation conditions that leads to an effective pumping caused by a self-induced diffusioosmotic flow.
We report generation of ultra-broadband longitudinal acoustic coherent phonon wavepackets in SrTiO3 (STO) with frequency components extending throughout the first Brillouin zone. The wavepackets are efficiently generated in STO using femtosecond infrared laser excitation of an atomically flat 1.6 nm-thick epitaxial SrRuO3 film. We use femtosecond x-ray diffraction at the European X-Ray Free Electron Laser Facility to study the dispersion and damping of phonon wavepackets. The experimentally determined damping constants for multi-THz frequency phonons compare favorably to the extrapolation of a simple ultrasound damping model over several orders of magnitude.
We have analysed an archival XMM-Newton EPIC observation that serendipitously covered the sky position of a variable X-ray source AX J1714.1-3912, previously suggested to be a Supergiant Fast X-ray Transient (SFXT). During the XMM-Newton observation the source is variable on a timescale of hundred seconds and shows two luminosity states, with a flaring activity followed by unflared emission, with a variability amplitude of a factor of about 50. We have discovered an intense iron emission line with a centroid energy of 6.4 keV in the power law-like spectrum, modified by a large absorption (N-H similar to 10(24) cm(-2)), never observed before from this source. This X-ray spectrum is unusual for an SFXT, but resembles the so-called 'highly obscured sources', high mass X-ray binaries (HMXBs) hosting an evolved B[e] supergiant companion (sgB[e]). This might suggest that AX J1714.1-3912 is a new member of this rare type of HMXBs, which includes IGR J16318-4848 and CI Camelopardalis. Increasing this small population of sources would be remarkable, as they represent an interesting short transition evolutionary stage in the evolution of massive binaries. Nevertheless, AX J1714.1-3912 appears to share X-ray properties of both kinds of HMXBs (SFXT versus sgB[e] HMXB). Therefore, further investigations of the companion star are needed to disentangle the two hypothesis.
We address the effect of stochastic resetting on diffusion and subdiffusion process. For diffusion we find that mean square displacement relaxes to a constant only when the distribution of reset times possess finite mean and variance. In this case, the leading order contribution to the probability density function (PDF) of a Gaussian propagator under resetting exhibits a cusp independent of the specific details of the reset time distribution. For subdiffusion we derive the PDF in Laplace space for arbitrary resetting protocol. Resetting at constant rate allows evaluation of the PDF in terms of H function. We analyze the steady state and derive the rate function governing the relaxation behavior. For a subdiffusive process the steady state could exist even if the distribution of reset times possesses only finite mean.