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bibliography.bib

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@@ -769,42 +769,32 @@ @article{Ceccarelli2014
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copyright = {arXiv.org perpetual, non-exclusive license}
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}
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@article{terry2023,
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author = {{Terry}, J.~P. and {Hall}, C. and {Abreau}, S. and {Gleyzer}, S.},
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title = {{Kinematic Evidence of an Embedded Protoplanet in HD 142666 Identified by Machine Learning}},
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journal = {\apj},
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keywords = {Protoplanetary disks, Planetary system formation, Hydrodynamical simulations, Astronomical simulations, Radiative transfer simulations, 1300, 1257, 767, 1857, 1967, Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Solar and Stellar Astrophysics, Computer Science - Machine Learning},
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year = 2023,
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month = apr,
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volume = {947},
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number = {2},
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eid = {60},
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pages = {60},
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doi = {10.3847/1538-4357/acc737},
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archiveprefix = {arXiv},
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eprint = {2301.05075},
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primaryclass = {astro-ph.EP},
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adsurl = {https://ui.adsabs.harvard.edu/abs/2023ApJ...947...60T},
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adsnote = {Provided by the SAO/NASA Astrophysics Data System}
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@article{terry2023kinematic,
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title = {Kinematic Evidence of an Embedded Protoplanet in HD 142666 Identified by Machine Learning},
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author = {Terry, JP and Hall, C and Abreau, Sean and Gleyzer, Sergei},
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journal = {The Astrophysical Journal},
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volume = {947},
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number = {2},
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pages = {60},
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year = {2023},
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publisher = {The American Astronomical Society},
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doi = {10.3847/1538-4357/acc737}
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}
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@article{terry2022,
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author = {{Terry}, J.~P. and {Hall}, C. and {Abreau}, S. and {Gleyzer}, S.},
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title = {{Locating Hidden Exoplanets in ALMA Data Using Machine Learning}},
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journal = {\apj},
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keywords = {Exoplanet astronomy, Exoplanet detection methods, Hydrodynamical simulations, Protoplanetary disks, 486, 489, 767, 1300, Astrophysics - Earth and Planetary Astrophysics, Computer Science - Machine Learning},
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year = 2022,
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month = dec,
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volume = {941},
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number = {2},
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eid = {192},
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pages = {192},
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doi = {10.3847/1538-4357/aca477},
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archiveprefix = {arXiv},
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eprint = {2211.09541},
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primaryclass = {astro-ph.EP},
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adsurl = {https://ui.adsabs.harvard.edu/abs/2022ApJ...941..192T},
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adsnote = {Provided by the SAO/NASA Astrophysics Data System}
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@article{terry2022locating,
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title = {Locating hidden exoplanets in ALMA data using machine learning},
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author = {Terry, Jason P and Hall, Cassandra and Abreau, Sean and Gleyzer, Sergei},
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journal = {The Astrophysical Journal},
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volume = {941},
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number = {2},
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pages = {192},
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year = {2022},
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publisher = {The American Astronomical Society},
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doi = {10.3847/1538-4357/aca477}
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}
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content/research/Denoising Astronomical Observations of Protoplanetary Disks.md

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Gas kinematics reveals still-forming embedded planets in images of disks through features called *kinks*.
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# Basic Idea
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@terry2022 use [[Latin Hypercube|latin hypercube]] to distribute randomly-spaced data points evenly across the whole parameter space.
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@terry2022locating use [[Latin Hypercube|latin hypercube]] to distribute randomly-spaced data points evenly across the whole parameter space.

quartz.config.ts

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import { QuartzConfig } from "./quartz/cfg";
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import * as Plugin from "./quartz/plugins";
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import { QuartzConfig } from "./quartz/cfg"
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import * as Plugin from "./quartz/plugins"
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/**
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* Quartz 4 Configuration
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fontOrigin: "googleFonts",
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cdnCaching: true,
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typography: {
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header: {
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name: "Rubik Mono One",
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weights: [400],
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},
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body: "Rubik",
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header: "Major Mono Display",
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body: "Archivo",
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code: "JetBrains Mono",
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},
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colors: {
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},
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}
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export default config
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export default config

quartz/styles/custom.scss

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box-shadow: 0 4px 10px rgba(0, 0, 0, 0.15);
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display: none;
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z-index: 1000;
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}
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.canvas text {
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font-family: system-ui, sans-serif;
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}

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