Mathew J. Owens

Bio: Mathew J. Owens is an academic researcher from University of Reading. The author has contributed to research in topics: Solar wind & Coronal mass ejection. The author has an hindex of 38, co-authored 216 publications receiving 5121 citations. Previous affiliations of Mathew J. Owens include Imperial College London & Boston University.

The Heliospheric Magnetic Field

Abstract: The heliospheric magnetic field (HMF) is the extension of the coronal magnetic field carried out into the solar system by the solar wind. It is the means by which the Sun interacts with planetary magnetospheres and channels charged particles propagating through the heliosphere. As the HMF remains rooted at the solar photosphere as the Sun rotates, the large-scale HMF traces out an Archimedean spiral. This pattern is distorted by the interaction of fast and slow solar wind streams, as well as the interplanetary manifestations of transient solar eruptions called coronal mass ejections. On the smaller scale, the HMF exhibits an array of waves, discontinuities, and turbulence, which give hints to the solar wind formation process. This review aims to summarise observations and theory of the small- and large-scale structure of the HMF. Solar-cycle and cycle-to-cycle evolution of the HMF is discussed in terms of recent spacecraft observations and pre-spaceage proxies for the HMF in geomagnetic and galactic cosmic ray records.

The open flux problem.

Abstract: The heliospheric magnetic field is of pivotal importance in solar and space physics. The field is rooted in the Sun's photosphere, where it has been observed for many years. Global maps of the solar magnetic field based on full-disk magnetograms are commonly used as boundary conditions for coronal and solar wind models. Two primary observational constraints on the models are (1) the open field regions in the model should approximately correspond to coronal holes (CHs) observed in emission and (2) the magnitude of the open magnetic flux in the model should match that inferred from in situ spacecraft measurements. In this study, we calculate both magnetohydrodynamic and potential field source surface solutions using 14 different magnetic maps produced from five different types of observatory magnetograms, for the time period surrounding 2010 July. We have found that for all of the model/map combinations, models that have CH areas close to observations underestimate the interplanetary magnetic flux, or, conversely, for models to match the interplanetary flux, the modeled open field regions are larger than CHs observed in EUV emission. In an alternative approach, we estimate the open magnetic flux entirely from solar observations by combining automatically detected CHs for Carrington rotation 2098 with observatory synoptic magnetic maps. This approach also underestimates the interplanetary magnetic flux. Our results imply that either typical observatory maps underestimate the Sun's magnetic flux, or a significant portion of the open magnetic flux is not rooted in regions that are obviously dark in EUV and X-ray emission.

Multispacecraft observation of magnetic cloud erosion by magnetic reconnection during propagation

Abstract: During propagation, Magnetic Clouds (MC) interact with their environment and, in particular, may reconnect with the solar wind around it, eroding away part of its initial magnetic flux. Here we quantitatively analyze such an interaction using combined, multipoint observations of the same MC flux rope by STEREO A, B, ACE, WIND and THEMIS on November 19-20, 2007. Observation of azimuthal magnetic flux imbalance inside a MC flux rope has been argued to stem from erosion due to magnetic reconnection at its front boundary. The present study adds to such analysis a large set of signatures expected from this erosion process. (1) Comparison of azimuthal flux imbalance for the same MC at widely separated points precludes the crossing of the MC leg as a source of bias in flux imbalance estimates. (2) The use of different methods, associated errors and parametric analyses show that only an unexpectedly large error in MC axis orientation could explain the azimuthal flux imbalance. (3) Reconnection signatures are observed at the MC front at all spacecraft, consistent with an ongoing erosion process. (4) Signatures in suprathermal electrons suggest that the trailing part of the MC has a different large-scale magnetic topology, as expected. The azimuthal magnetic flux erosion estimated at ACE and STEREO A corresponds respectively to 44% and 49% of the inferred initial azimuthal magnetic flux before MC erosion upon propagation. The corresponding average reconnection rate during transit is estimated to be in the range 0.12-0.22 mV/m, suggesting most of the erosion occurs in the inner parts of the heliosphere. Future studies ought to quantify the influence of such an erosion process on geo-effectiveness. ©2012. American Geophysical Union. All Rights Reserved.

Statistical study of magnetic cloud erosion by magnetic reconnection

Abstract: Several recent studies suggest that magnetic reconnection is able to erode substantial amounts of the outer magnetic flux of interplanetary magnetic clouds (MCs) as they propagate in the heliosphere. We quantify and provide a broader context to this process, starting from 263 tabulated interplanetary coronal mass ejections, including MCs, observed over a time period covering 17 years and at a distance of 1 AU from the Sun with Wind (1995–2008) and the two STEREO (2009–2012) spacecraft. Based on several quality factors, including careful determination of the MC boundaries and main magnetic flux rope axes, an analysis of the azimuthal flux imbalance expected from erosion by magnetic reconnection was performed on a subset of 50 MCs. The results suggest that MCs may be eroded at the front or at rear and in similar proportions, with a significant average erosion of about 40% of the total azimuthal magnetic flux. We also searched for in situ signatures of magnetic reconnection causing erosion at the front and rear boundaries of these MCs. Nearly ~30% of the selected MC boundaries show reconnection signatures. Given that observations were acquired only at 1 AU and that MCs are large-scale structures, this finding is also consistent with the idea that erosion is a common process. Finally, we studied potential correlations between the amount of eroded azimuthal magnetic flux and various parameters such as local magnetic shear, Alfven speed, and leading and trailing ambient solar wind speeds. However, no significant correlations were found, suggesting that the locally observed parameters at 1 AU are not likely to be representative of the conditions that prevailed during the erosion which occurred during propagation from the Sun to 1 AU. Future heliospheric missions, and in particular Solar Orbiter or Solar Probe Plus, will be fully geared to answer such questions.

Metrics for solar wind prediction models: Comparison of empirical, hybrid, and physics-based schemes with 8 years of L1 observations

Abstract: [1] Space weather effects on technological systems originate with energy carried from the Sun to the terrestrial environment by the solar wind. In this study, we present results of modeling of solar corona-heliosphere processes to predict solar wind conditions at the L1 Lagrangian point upstream of Earth. In particular we calculate performance metrics for (1) empirical, (2) hybrid empirical/physics-based, and (3) full physics-based coupled corona-heliosphere models over an 8-year period (1995–2002). L1 measurements of the radial solar wind speed are the primary basis for validation of the coronal and heliosphere models studied, though other solar wind parameters are also considered. The models are from the Center for Integrated Space-Weather Modeling (CISM) which has developed a coupled model of the whole Sun-to-Earth system, from the solar photosphere to the terrestrial thermosphere. Simple point-by-point analysis techniques, such as mean-square-error and correlation coefficients, indicate that the empirical coronal-heliosphere model currently gives the best forecast of solar wind speed at 1 AU. A more detailed analysis shows that errors in the physics-based models are predominately the result of small timing offsets to solar wind structures and that the large-scale features of the solar wind are actually well modeled. We suggest that additional “tuning” of the coupling between the coronal and heliosphere models could lead to a significant improvement of their accuracy. Furthermore, we note that the physics-based models accurately capture dynamic effects at solar wind stream interaction regions, such as magnetic field compression, flow deflection, and density buildup, which the empirical scheme cannot.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Optically programmable electron spin memory using semiconductor quantum dots

Abstract: The spin of a single electron subject to a static magnetic field provides a natural two-level system that is suitable for use as a quantum bit, the fundamental logical unit in a quantum computer. Semiconductor quantum dots fabricated by strain driven self-assembly are particularly attractive for the realization of spin quantum bits, as they can be controllably positioned, electronically coupled and embedded into active devices. It has been predicted that the atomic-like electronic structure of such quantum dots suppresses coupling of the spin to the solid-state quantum dot environment, thus protecting the 'spin' quantum information against decoherence. Here we demonstrate a single electron spin memory device in which the electron spin can be programmed by frequency selective optical excitation. We use the device to prepare single electron spins in semiconductor quantum dots with a well defined orientation, and directly measure the intrinsic spin flip time and its dependence on magnetic field. A very long spin lifetime is obtained, with a lower limit of about 20 milliseconds at a magnetic field of 4 tesla and at 1 kelvin.

Near-Earth Interplanetary Coronal Mass Ejections During Solar Cycle 23 (1996 - 2009): Catalog and Summary of Properties

Abstract: In a previous study (Cane and Richardson, J. Geophys. Res. 108(A4), SSH6-1, 2003), we investigated the occurrence of interplanetary coronal mass ejections in the near-Earth solar wind during 1996 – 2002, corresponding to the increasing and maximum phases of solar cycle 23, and provided a “comprehensive” catalog of these events. In this paper, we present a revised and updated catalog of the ≈300 near-Earth ICMEs in 1996 – 2009, encompassing the complete cycle 23, and summarize their basic properties and geomagnetic effects. In particular, solar wind composition and charge state observations are now considered when identifying the ICMEs. In general, these additional data confirm the earlier identifications based predominantly on other solar wind plasma and magnetic field parameters. However, the boundaries of ICME-like plasma based on charge state/composition data may deviate significantly from those based on conventional plasma/magnetic field parameters. Furthermore, the much studied “magnetic clouds”, with flux-rope-like magnetic field configurations, may form just a substructure of the total ICME interval.