English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

The next generation of telescopes will yield a substantial increase in theavailability of high-resolution spectroscopic data for thousands of exoplanets.The sheer volume of data and number of planets to be analyzed greatly motivatethe development of new, fast and efficient methods for flagging interestingplanets for reobservation and detailed analysis. We advocate the application ofmachine learning (ML) techniques for anomaly (novelty) detection to exoplanettransit spectra, with the goal of identifying planets with unusual chemicalcomposition and even searching for unknown biosignatures. We successfullydemonstrate the feasibility of two popular anomaly detection methods (LocalOutlier Factor and One Class Support Vector Machine) on a large public databaseof synthetic spectra. We consider several test cases, each with differentlevels of instrumental noise. In each case, we use ROC curves to quantify andcompare the performance of the two ML techniques.

Searching for Novel Chemistry in Exoplanetary Atmospheres using Machine Learning for Anomaly Detection