993 research outputs found

    Fundamental Performance of a Dispersed Fixed Delay Interferometer In Searching For Planets Around M Dwarfs

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    We present a new method to calculate fundamental Doppler measurement limits with a dispersed fixed-delay interferometer (DFDI) in the near infrared wavelength region for searching for exoplanets around M dwarfs in the coming decade. It is based on calculating the Q factor, a measure of flux-normalized Doppler sensitivity in the fringing spectra created with DFDI. We calculate the Q factor as a function of spectral resolution R, stellar projected rotational velocity V sini, stellar effective temperature T_eff and optical path difference (OPD) of the interferometer. We also compare the DFDI Q factor to that for the popular cross-dispersed echelle spectrograph method (the direct echelle (DE) method). Given the IR Doppler measurement is likely to be detector-limited for a while, we introduce new merit functions, which is directly related to photon-limited RV uncertainty, to evaluate Doppler performance with the DFDI and DE methods. We find that DFDI has strength in wavelength coverage and multi-object capability over the DE for a limited detector resource. We simulate the performance of the InfraRed Exoplanet Tracker (IRET) based on the DFDI design, being considered for the next generation IR Doppler measurements. The predicted photon-limited RV uncertainty suggests that IRET is capable of detecting Earth-like exoplanets in habitable zone around nearby bright M dwarfs if they exist. A new method is developed to quantitatively estimate the influence of telluric lines on RV uncertainty. Our study shows that photon-limited RV uncertainty can be reached if 99% of the strength of telluric lines can be removed from the measured stellar spectra. At low to moderate levels of telluric line strength removal (50% to 90%), the optimal RV uncertainty is typically a factor of 2-3 times larger than photon-limited RV uncertainty.Comment: 43 pages, 20 figures, 6 tables. Accepted by Ap

    Legacy radionuclides in cryoconite and proglacial sediment on Orwell Glacier, Signy Island, Antarctica

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    Cryoconite is a specific type of material found on the surface of glaciers and icesheets. Samples of cryoconite were collected from the Orwell Glacier and its moraines, together with suspended sediment from the proglacial stream on Signy Island, part of the South Orkney Islands, Antarctica. The activity concentrations of certain fallout radionuclides were determined in the cryoconite, moraine and suspended sediment, in addition to particle size composition and %C and %N. For cryoconite samples (n = 5), mean activity concentrations (±1SD) of 137Cs, 210Pbun and 241Am were 13.2 ± 20.9, 66.1 ± 94.0 and 0.32 ± 0.64 Bq kg−1, respectively. Equivalent values for the moraine samples (n = 7) were 2.56 ± 2.75, 14.78 ± 12.44 and <1.0 Bq kg−1, respectively. For the composite suspended sediment sample, collected over 3 weeks in the ablation season, the values (± counting uncertainty) for 137Cs, 210Pbun and 241Am were 2.64 ± 0.88, 49.2 ± 11.9 and <1.0 Bq kg−1, respectively. Thus, fallout radionuclide activity concentrations were elevated in cryoconite relative to moraine and suspended sediment. In the case of 40K, the highest value was for the suspended sediment (1423 ± 166 Bq kg−1). The fallout radionuclides in cryoconite were 1–2 orders of magnitude greater than values in soils collected from other locations in Antarctica. This work further demonstrates that cryoconite likely scavenges fallout radionuclides (dissolved and particulate) in glacial meltwater. In the case of 40K, the greater value in suspended sediment implies a subglacial source. These results are amongst the relatively few that demonstrate the presence of fallout radionuclides in cryoconites at remote locations in the Southern Hemisphere. This work adds to the growing contention that elevated activities of fallout radionuclides, and other contaminants, in cryoconites are a global phenomenon and may be a risk to downstream terrestrial and aquatic ecosystems

    Legacy radionuclides in cryoconite and proglacial sediment on Orwell Glacier, Signy Island, Antarctica

    Get PDF
    Cryoconite is a specific type of material found on the surface of glaciers and icesheets. Samples of cryoconite were collected from the Orwell Glacier and its moraines, together with suspended sediment from the proglacial stream on Signy Island, part of the South Orkney Islands, Antarctica. The activity concentrations of certain fallout radionuclides were determined in the cryoconite, moraine and suspended sediment, in addition to particle size composition and %C and %N. For cryoconite samples (n = 5), mean activity concentrations (±1SD) of 137Cs, 210Pbun and 241Am were 13.2 ± 20.9, 66.1 ± 94.0 and 0.32 ± 0.64 Bq kg−1, respectively. Equivalent values for the moraine samples (n = 7) were 2.56 ± 2.75, 14.78 ± 12.44 and <1.0 Bq kg−1, respectively. For the composite suspended sediment sample, collected over 3 weeks in the ablation season, the values (± counting uncertainty) for 137Cs, 210Pbun and 241Am were 2.64 ± 0.88, 49.2 ± 11.9 and <1.0 Bq kg−1, respectively. Thus, fallout radionuclide activity concentrations were elevated in cryoconite relative to moraine and suspended sediment. In the case of 40K, the highest value was for the suspended sediment (1423 ± 166 Bq kg−1). The fallout radionuclides in cryoconite were 1–2 orders of magnitude greater than values in soils collected from other locations in Antarctica. This work further demonstrates that cryoconite likely scavenges fallout radionuclides (dissolved and particulate) in glacial meltwater. In the case of 40K, the greater value in suspended sediment implies a subglacial source. These results are amongst the relatively few that demonstrate the presence of fallout radionuclides in cryoconites at remote locations in the Southern Hemisphere. This work adds to the growing contention that elevated activities of fallout radionuclides, and other contaminants, in cryoconites are a global phenomenon and may be a risk to downstream terrestrial and aquatic ecosystems

    The SDSS-III APOGEE Radial Velocity Survey of M dwarfs I: Description of Survey and Science Goals

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    We are carrying out a large ancillary program with the SDSS-III, using the fiber-fed multi-object NIR APOGEE spectrograph, to obtain high-resolution H-band spectra of more than 1200 M dwarfs. These observations are used to measure spectroscopic rotational velocities, radial velocities, physical stellar parameters, and variability of the target stars. Here, we describe the target selection for this survey and results from the first year of scientific observations based on spectra that is publicly available in the SDSS-III DR10 data release. As part of this paper we present RVs and vsini of over 200 M dwarfs, with a vsini precision of ~2 km/s and a measurement floor at vsini = 4 km/s. This survey significantly increases the number of M dwarfs studied for vsini and RV variability (at ~100-200 m/s), and will advance the target selection for planned RV and photometric searches for low mass exoplanets around M dwarfs, such as HPF, CARMENES, and TESS. Multiple epochs of radial velocity observations enable us to identify short period binaries, and AO imaging of a subset of stars enables the detection of possible stellar companions at larger separations. The high-resolution H-band APOGEE spectra provide the opportunity to measure physical stellar parameters such as effective temperatures and metallicities for many of these stars. At the culmination of this survey, we will have obtained multi-epoch spectra and RVs for over 1400 stars spanning spectral types of M0-L0, providing the largest set of NIR M dwarf spectra at high resolution, and more than doubling the number of known spectroscopic vsini values for M dwarfs. Furthermore, by modeling telluric lines to correct for small instrumental radial velocity shifts, we hope to achieve a relative velocity precision floor of 50 m/s for bright M dwarfs. We present preliminary results of this telluric modeling technique in this paper.Comment: Submitted to Astronomical Journa

    Recognizing recurrent neural networks (rRNN): Bayesian inference for recurrent neural networks

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    Recurrent neural networks (RNNs) are widely used in computational neuroscience and machine learning applications. In an RNN, each neuron computes its output as a nonlinear function of its integrated input. While the importance of RNNs, especially as models of brain processing, is undisputed, it is also widely acknowledged that the computations in standard RNN models may be an over-simplification of what real neuronal networks compute. Here, we suggest that the RNN approach may be made both neurobiologically more plausible and computationally more powerful by its fusion with Bayesian inference techniques for nonlinear dynamical systems. In this scheme, we use an RNN as a generative model of dynamic input caused by the environment, e.g. of speech or kinematics. Given this generative RNN model, we derive Bayesian update equations that can decode its output. Critically, these updates define a 'recognizing RNN' (rRNN), in which neurons compute and exchange prediction and prediction error messages. The rRNN has several desirable features that a conventional RNN does not have, for example, fast decoding of dynamic stimuli and robustness to initial conditions and noise. Furthermore, it implements a predictive coding scheme for dynamic inputs. We suggest that the Bayesian inversion of recurrent neural networks may be useful both as a model of brain function and as a machine learning tool. We illustrate the use of the rRNN by an application to the online decoding (i.e. recognition) of human kinematics

    Climate Change and invasibility of the Antarctic benthos

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    Benthic communities living in shallow-shelf habitats in Antarctica (&lt;100-m depth) are archaic in their structure and function. Modern predators, including fast-moving, durophagous (skeleton-crushing) bony fish, sharks, and crabs, are rare or absent; slow-moving invertebrates are the top predators; and epifaunal suspension feeders dominate many soft substratum communities. Cooling temperatures beginning in the late Eocene excluded durophagous predators, ultimately resulting in the endemic living fauna and its unique food-web structure. Although the Southern Ocean is oceanographically isolated, the barriers to biological invasion are primarily physiological rather than geographic. Cold temperatures impose limits to performance that exclude modern predators. Global warming is now removing those physiological barriers, and crabs are reinvading Antarctica. As sea temperatures continue to rise, the invasion of durophagous predators will modernize the shelf benthos and erode the indigenous character of marine life in Antarctica

    Markovian Dynamics on Complex Reaction Networks

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    Complex networks, comprised of individual elements that interact with each other through reaction channels, are ubiquitous across many scientific and engineering disciplines. Examples include biochemical, pharmacokinetic, epidemiological, ecological, social, neural, and multi-agent networks. A common approach to modeling such networks is by a master equation that governs the dynamic evolution of the joint probability mass function of the underling population process and naturally leads to Markovian dynamics for such process. Due however to the nonlinear nature of most reactions, the computation and analysis of the resulting stochastic population dynamics is a difficult task. This review article provides a coherent and comprehensive coverage of recently developed approaches and methods to tackle this problem. After reviewing a general framework for modeling Markovian reaction networks and giving specific examples, the authors present numerical and computational techniques capable of evaluating or approximating the solution of the master equation, discuss a recently developed approach for studying the stationary behavior of Markovian reaction networks using a potential energy landscape perspective, and provide an introduction to the emerging theory of thermodynamic analysis of such networks. Three representative problems of opinion formation, transcription regulation, and neural network dynamics are used as illustrative examples.Comment: 52 pages, 11 figures, for freely available MATLAB software, see http://www.cis.jhu.edu/~goutsias/CSS%20lab/software.htm
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