1,720,966 research outputs found
Deep neural network ensembles for THz-TDS refractive index extraction exhibiting resilience to experimental and analytical errors
No full textTerahertz time-domain spectroscopy (THz-TDS) achieves excellent signal-to-noise ratios by measuring the amplitude of the electric field in the time-domain, resulting in the full, complex, frequency-domain information of materials’ optical parameters, such as the refractive index. However the data extraction process is non-trivial and standardization of practices are still yet to be cemented in the field leading to significant variation in sample measurements. One such contribution is low frequency noise offsetting the phase reconstruction of the Fourier transformed signal. Additionally, experimental errors such as fluctuations in the power of the laser driving the spectrometer (laser drift) can heavily contribute to erroneous measurements if not accounted for. We show that ensembles of deep neural networks trained with synthetic data extract the frequency-dependent complex refractive index, whereby required fitting steps are automated and show resilience to phase unwrapping variations and laser drift. We show that training with synthetic data allows for flexibility in the functionality of networks yet the produced ensemble supersedes current extraction techniques.</p
Artificial neural networks for material parameter extraction in terahertz time-domain spectroscopy
No full textTerahertz time-domain spectroscopy (THz-TDS) is a proven technique whereby the complex refractive indices of materials can be obtained without requiring the use of the Kramers-Kronig relations, as phase and amplitude information can be extracted from the measurement. However, manual pre-processing of the data is still required and the material parameters require iterative fitting, resulting in complexity, loss of accuracy and inconsistencies between measurements. Alternatively approximations can be used to enable analytical extraction but with a considerable sacrifice of accuracy. We investigate the use of machine learning techniques for interpreting spectroscopic THz-TDS data by training with large data sets of simulated light-matter interactions, resulting in a computationally efficient artificial neural network for material parameter extraction. The trained model improves on the accuracy of analytical methods that need approximations while being easier to implement and faster to run than iterative root-finding methods. We envisage neural networks can alleviate many of the common hurdles involved in analyzing THz-TDS data such as phase unwrapping, time domain windowing, slow computation times, and extraction accuracy at the low frequency range.</p
Optically Defined Reconfigurable THz Metasurfaces using Graphene on Iron‐Doped Lithium Niobate
No full textGraphene plasmonic devices have been demonstrated to show great potential for reconfigurable metasurfaces due to the tuneable electronic charge transport properties of graphene in response to electrostatic gating. Iron-doped lithium niobate is proposed as a platform for patterning-free optically reconfigurable graphene metasurfaces in the THz spectral region. Under structured illumination, the lithium niobate undergoes charge migration in the bulk, where carriers migrate away from illuminated regions, forming spatially patterned charge distributions capable of electrostatic tuning of graphene. These charge distributions are stable in the dark, however, can be redefined by subsequent illumination. Through the use of numerical simulations, it is demonstrated that optically defined charge distributions in lithium niobate can tune locally the graphene Fermi level allowing for plasmonic resonances at THz frequencies
THz spectroscopy of photogenerated carriers in Fe:LiNbO<sub>3</sub> for optical control of 2D materials
No full textWe report on significant photo-induced changes in the transmission of THz radiation through iron doped lithium niobate (Fe:LiNbO3). The effect is attributed to photo-excited charge carriers, originating from the Fe dopant energy levels in the band gap of the crystal. In previous work, we have demonstrated that Fe:LiNbO3 substrates allow control of graphene electronics and plasmonics through photo-induced electrostatic fields in the crystal [1]. THz time domain spectroscopy (TDS) measurements of Fe:LiNbO3 provide the means to calculate photoexcited charge carrier densities within the crystal, thereby providing further insight into Fe:LiNbO3 as a versatile platform for the transient photo-induced electrostatic doping of 2D materials
THz-TDS: extracting complex conductivity of two-dimensional materials via neural networks trained on synthetic and experimental data
No full textTerahertz time-domain spectroscopy (TDS) has proved immensely useful for probing 2D materials such as graphene. Unlike in the visible regime, the optical properties at terahertz frequencies are highly dependant on charge carrier mobility and scattering time. However, extracting the material properties from the terahertz waveform is a non-trivial process, which can be prone to producing erroneous results. Artificial neural networks have recently been demonstrated as useful tools to extract complex refractive index from terahertz time domain data. Here, we propose the use of artificial neural networks to interpret terahertz spectra of graphene monolayers to extract the charge carrier mobility and scattering time. We demonstrate improved performance on out-of-distribution data by using a combination of synthetically generated spectra and experimental data during training.</p
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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