Phase Diversity Electro-optic Sampling: A new approach to single-shot terahertz waveform recording

Recording electric field evolution in single-shot with THz bandwidth is needed in science including spectroscopy, plasmas, biology, chemistry, Free-Electron Lasers, accelerators, and material inspection. However, the potential application range depends on the possibility to achieve sub-picosecond resolution over a long time window, which is a largely open problem for single-shot techniques. To solve this problem, we present a new conceptual approach for the so-called spectral decoding technique, where a chirped laser pulse interacts with a THz signal in a Pockels crystal, and is analyzed using a grating optical spectrum analyzer. By borrowing mathematical concepts from photonic time stretch theory and radio-frequency communication, we deduce a novel dual-output electro-optic sampling system, for which the input THz signal can be numerically retrieved—with unprecedented resolution—using the so-called phase diversity technique. We show numerically and experimentally that this approach enables the recording of THz waveforms in single-shot over much longer durations and/or higher bandwidth than previous spectral decoding techniques. We present and test the proposed DEOS (Diversity Electro-Optic Sampling) design for recording 1.5 THz bandwidth THz pulses, over 20 ps duration, in single-shot. Then we demonstrate the potential of DEOS in accelerator physics by recording, in two successive shots, the shape of 200 fs RMS relativistic electron bunches at European X-FEL, over 10 ps recording windows. The designs presented here can be used directly for accelerator diagnostics, characterization of THz sources, and single-shot Time-Domain Spectroscopy.

Screening of Hybrid Jatropha curcas L. Genotypes for Drought Tolerant Abilities as a Source of Superior Variety Characteristics

<p>The scarcity of fuel oil in the future needs to be anticipated by preparing alternative energy. One of the alternative energy sources developed is <em>Jatropha</em>. However, the development of <em>Jatropha</em> should use marginal land as a cultivation area, like a dry land, because the food crops and horticulture use productive land for cultivation. Furthermore, <em>Jatropha</em> development through breeding is an effort to obtain superior varieties as biodiesel producers. This study aimed to determine the best phenotype characteristics of hybrid<em> Jatropha</em> plants as superior candidate trees. This research applied a descriptive qualitative method to determine the characteristics of <em>Jatropha</em> crossing results. The <em>Jatropha</em> characterization was conducted at the experimental garden of Kedung Pengaron Village, Kejayan Sub-district, Pasuruan Regency. The experimental garden was included dry land for <em>Jatropha </em>cultivation. The result showed ten selected genotypes of hybrid<em> Jatropha</em> were found, including 05.01.01, 05.01.02, 05.02.02, 05.02.09, 05.03.17, 05.03.16, 05.04.16, 05.04.15, 06.01.02, 06.01.15. These were based on the two-phase observations, consisting of vegetative and generative observations. The plants’ morphological characters were observed, comprising the stems, leaves, flowers, fruits and seeds. In the vegetative phase, diversity was found in the number of leaves, leaf length and age of flowering, while in the qualitative phase, the shoot colors were more diverse. Moreover, less variations were figured out in the color character of the petiole and the number of shoots. These ten genotypes of hybrid <em>Jatropha</em> are the best genotypes that are resistant to drought with selected agronomic characters.</p>

A Machine Learning Approach to Correcting Atmospheric Seeing in Solar Flare Observations

Current post-processing techniques for the correction of atmospheric seeing in solar observations – such as Speckle interferometry and Phase Diversity methods – have limitations when it comes to their reconstructive capabilities of solar flare observations. This, combined with the sporadic nature of flares meaning observers cannot wait until seeing conditions are optimal before taking measurements, means that many ground-based solar flare observations are marred with bad seeing. To combat this, we propose a method for dedicated flare seeing correction based on training a deep neural network to learn to correct artificial seeing from flare observations taken during good seeing conditions. This model uses transfer learning, a novel technique in solar physics, to help learn these corrections. Transfer learning is when another network already trained on similar data is used to influence the learning of the new network. Once trained, the model has been applied to two flare datasets: one from AR12157 on 2014/09/06 and one from AR12673 on 2017/09/06. The results show good corrections to images with bad seeing with a relative error assigned to the estimate based on the performance of the model. Further discussion takes place of improvements to the robustness of the error on these estimates.