arXiv subjects

J. Lucas

Publications and source records attributed to J. Lucas.

3 recordsLinked to original sources

arXiv2021

Cosmic Ray Induced Mass-Independent Oxygen Isotope Exchange: A Novel Mechanism for Producing $^{16}$O depletions in the Early Solar System

A fundamental puzzle of our solar system's formation is understanding why the terrestrial bodies including the planets,comets,and asteroids are depleted in $^{16}$O compared to the Sun. The most favored mechanism,the selective photodissociation of CO gas to produce $^{16}$O depleted water,requires finely tuned mixing timescales to transport $^{16}$O depleted water from the cold outer solar system to exchange isotopically with dust grains to produce the $^{16}$O depleted planetary bodies observed today. Here we show that energetic particle irradiation of SiO$_2$ (and Al$_2$O$_3$) makes them susceptible to anomalous isotope exchange with H$_2$O ice at temperatures as low as 10 K. The observed magnitude of the anomalous isotope exchange (D$^{17}$O) is sufficient to generate the $^{16}$O depletion characteristic of the terrestrial bodies in the solar system. We calculated the cosmic-ray exposure times needed to produce the observed $^{16}$O depletions in silicate (SiO2) dust in the interstellar medium and early solar system and find that radiation damage induced oxygen isotope exchange could have rapidly (~10-100 yrs) depleted dust grains of $^{16}$O during the Sun's T-Tauri phase. Our model explains whythe oldest and most refractory minerals found in the solar system, the anhydrous Calcium with Aluminum Inclusions (CAIs),are generally $^{16}$O enriched compared to chondrules and the bulk terrestrial solids and provides a mechanism for producing $^{16}$O depleted grains very early in the solar system's history. Our findings have broad implications for the distribution of oxygen isotopes in the solar system, the interstellar medium, the formation of the planets and its building blocks as well as the nature of mass-independent isotope effects.

astro-ph.EP↗

arXiv2017

Nazr-CNN: Fine-Grained Classification of UAV Imagery for Damage Assessment

We propose Nazr-CNN1, a deep learning pipeline for object detection and fine-grained classification in images acquired from Unmanned Aerial Vehicles (UAVs) for damage assessment and monitoring. Nazr-CNN consists of two components. The function of the first component is to localize objects (e.g. houses or infrastructure) in an image by carrying out a pixel-level classification. In the second component, a hidden layer of a Convolutional Neural Network (CNN) is used to encode Fisher Vectors (FV) of the segments generated from the first component in order to help discriminate between different levels of damage. To showcase our approach we use data from UAVs that were deployed to assess the level of damage in the aftermath of a devastating cyclone that hit the island of Vanuatu in 2015. The collected images were labeled by a crowdsourcing effort and the labeling categories consisted of fine-grained levels of damage to built structures. Since our data set is relatively small, a pre- trained network for pixel-level classification and FV encoding was used. Nazr-CNN attains promising results both for object detection and damage assessment suggesting that the integrated pipeline is robust in the face of small data sets and labeling errors by annotators. While the focus of Nazr-CNN is on assessment of UAV images in a post-disaster scenario, our solution is general and can be applied in many diverse settings. We show one such case of transfer learning to assess the level of damage in aerial images collected after a typhoon in Philippines.

cs.CV↗

arXiv2014

Extracting the contribution of conduction in permittivity measurements using the Kramers-Kronig relations

The measurement of the material permittivity is often performed via an impedance measurement. In this case the measured permittivity includes the conduction contribution. Most of the time, the impedance-meters performing such measurement do not perform static measurement even if they can go to low frequencies. When the dipolar relaxation of the material leads to low frequency relaxation, separating the relaxation phenomenon from the conduction phenomenon can be difficult, especially when multiple relaxation phenomena superimpose. In this work we use the Kramers-Kronig relations to perform that separation by considering the frequency response aspect of the permittivity. The practical aspect of the method is presented and demonstrated on real measurements

cond-mat.mtrl-sci↗