Stellar Activity and its Implications for Exoplanet Detection on GJ 176

We present an in-depth analysis of stellar activity and its effects on radial velocity (RV) for the M2 dwarf GJ 176 based on spectra taken over 10 years from the High Resolution Spectrograph on the Hobby-Eberly Telescope. These data are supplemented with spectra from previous observations with the HIRES and HARPS spectrographs, and V- and R-band photometry taken over 6 years at the Dyer and Fairborn observatories. Previous studies of GJ 176 revealed a super-Earth exoplanet in an 8.8-day orbit. However, the velocities of this star are also known to be contaminated by activity, particularly at the 39-day stellar rotation period. We have examined the magnetic activity of GJ 176 using the sodium I D lines, which have been shown to be a sensitive activity tracer in cool stars. In addition to rotational modulation, we see evidence of a long-term trend in our Na I D index, which may be part of a long-period activity cycle. The sodium index is well correlated with our RVs, and we show that this activity trend drives a corresponding slope in RV. Interestingly, the rotation signal remains in phase in photometry, but not in the spectral activity indicators. We interpret this phenomenon as the result of one or more large spot complexes or active regions which dominate the photometric variability, while the spectral indices are driven by the overall magnetic activity across the stellar surface. In light of these results, we discuss the potential for correcting activity signals in the RVs of M dwarfs.Comment: Accepted for publication in Ap

Surface Encapsulation for Low-Loss Silicon Photonics

Encapsulation layers are explored for passivating the surfaces of silicon to reduce optical absorption in the 1500-nm wavelength band. Surface-sensitive test structures consisting of microdisk resonators are fabricated for this purpose. Based on previous work in silicon photovoltaics, coatings of SiNx and SiO2 are applied under varying deposition and annealing conditions. A short dry thermal oxidation followed by a long high-temperature N2 anneal is found to be most effective at long-term encapsulation and reduction of interface absorption. Minimization of the optical loss is attributed to simultaneous reduction in sub-bandgap silicon surface states and hydrogen in the capping material.Comment: 4 pages, 3 figure

Automated precision alignment of optical components for hydroxide catalysis bonding

We describe an interferometric system that can measure the alignment and separation of a polished face of a optical component and an adjacent polished surface. Accuracies achieved are ∼ 1μrad for the relative angles in two orthogonal directions and ∼ 30μm in separation. We describe the use of this readout system to automate the process of hydroxide catalysis bonding of a fused-silica component to a fused-silica baseplate. The complete alignment and bonding sequence was typically achieved in a timescale of a few minutes, followed by an initial cure of 10 minutes. A series of bonds were performed using two fluids - a simple sodium hydroxide solution and a sodium hydroxide solution with some sodium silicate solution added. In each case we achieved final bonded component angular alignment within 10 μrad and position in the critical direction within 4 μm of the planned targets. The small movements of the component during the initial bonding and curing phases were monitored. The bonds made using the sodium silicate mixture achieved their final bonded alignment over a period of ∼ 15 hours. Bonds using the simple sodium hydroxide solution achieved their final alignment in a much shorter time of a few minutes. The automated system promises to speed the manufacture of precision-aligned assemblies using hydroxide catalysis bonding by more than an order of magnitude over the more manual approach used to build the optical interferometer at the heart of the recent ESA LISA Pathfinder technology demonstrator mission. This novel approach will be key to the time-efficient and low-risk manufacture of the complex optical systems needed for the forthcoming ESA spaceborne gravitational waves observatory mission, provisionally named LISA

Intergalactic Globular Clusters

We confirm and extend our previous detection of a population of intergalactic globular clusters in Abell 1185, and report the first discovery of an intergalactic globular cluster in the nearby Virgo cluster of galaxies. The numbers, colors and luminosities of these objects can place constraints on their origin, which in turn may yield new insights to the evolution of galaxies in dense environments.Comment: 2 pages, no figures. Talk presented at JD6, IAU General Assembly XXV, Sydney, Australia, July 2003, to appear in Highlights of Astronomy, Vol. 1

Basin structure of optimization based state and parameter estimation

Most data based state and parameter estimation methods require suitable initial values or guesses to achieve convergence to the desired solution, which typically is a global minimum of some cost function. Unfortunately, however, other stable solutions (e.g., local minima) may exist and provide suboptimal or even wrong estimates. Here we demonstrate for a 9-dimensional Lorenz-96 model how to characterize the basin size of the global minimum when applying some particular optimization based estimation algorithm. We compare three different strategies for generating suitable initial guesses and we investigate the dependence of the solution on the given trajectory segment (underlying the measured time series). To address the question of how many state variables have to be measured for optimal performance, different types of multivariate time series are considered consisting of 1, 2, or 3 variables. Based on these time series the local observability of state variables and parameters of the Lorenz-96 model is investigated and confirmed using delay coordinates. This result is in good agreement with the observation that correct state and parameter estimation results are obtained if the optimization algorithm is initialized with initial guesses close to the true solution. In contrast, initialization with other exact solutions of the model equations (different from the true solution used to generate the time series) typically fails, i.e. the optimization procedure ends up in local minima different from the true solution. Initialization using random values in a box around the attractor exhibits success rates depending on the number of observables and the available time series (trajectory segment).Comment: 15 pages, 2 figure

Construction of rugged, ultrastable optical assemblies with optical component alignment at the few microradian level

A method for constructing quasimonolithic, precision-aligned optical assemblies is presented. Hydroxide-catalysis bonding is used, adapted to allow optimization of component fine alignment prior to the bond setting. We demonstrate the technique by bonding a fused silica mirror substrate to a fused silica baseplate. In-plane component placement at the submicrometer level is achieved, resulting in angular control of a reflected laser beam at the sub-10-μrad level. Within the context of the LISA Pathfinder mission, the technique has been demonstrated as suitable for use in space-flight applications. It is expected that there will also be applications in a wide range of areas where accuracy, stability, and strength of optical assemblies are important

Application of Artificial Neural Networks to Identify Equilibration in Computer Simulations

Determining which microstates generated by a thermodynamic simulation are representative of the ensemble for which sampling is desired is a ubiquitous, underspecified problem. Artificial neural networks are one type of machine learning algorithm that can provide a reproducible way to apply pattern recognition heuristics to underspecified problems. Here we use the open-source TensorFlow machine learning library and apply it to the problem of identifying which hypothetical observation sequences from a computer simulation are equilibrated and which are not. We generate training populations and test populations of observation sequences with embedded linear and exponential correlations. We train a two-neuron artificial network to distinguish the correlated and uncorrelated sequences. We find that this simple network is good enough for \u3e 98% accuracy in identifying exponentially-decaying energy trajectories from molecular simulations