International Conference on Machine Learning for Astrophysics - ML4Astro

Session

Poster Session Day 1

30 May 2022, 14:50

Catania

99. Planetary Markers in Stellar Spectra: Jupiter-host Star Classification

Miguel Andrea Zammit (University of Malta, Institute of Space Sciences & Astronomy)

30/05/2022, 14:50

Poster Presentation

Decades of observational & theoretical research has explored the relation between a star’s chemical, physical and galactic properties with the presence of orbiting planetary companions. Certain sources suggest that observed correlations are indicators of the environment of the system’s protoplanetary disc, and subsequently its proclivity to facilitate planetary formation. This project aims to...

106. Using Convolutional Neural Networks to Detect and Confirm Exoplanets

Amelia Yu (PAUSD (Palo Alto Unified School District))

30/05/2022, 14:53

Poster Presentation

Using deep learning (DL), I developed a Python software program with convolutional neural network (CNN) modules from TensorFlow to detect exoplanets through their transit signals in the National Aeronautics and Space Administration (NASA) Kepler space telescope data. My program first utilizes normalization of the light curves, trains deep learning models, tests and evaluates the models with...

113. Classification of Evolved Stars with (Unsupervised) Machine Learning

Jamie Welsh (Student)

30/05/2022, 14:56

Poster Presentation

The next-generation of observational astronomy instrumentation is expected to generate massively large and high complexity data volumes (big data) at rates of several gigabytes per second. Such enormous volumes impose extremely challenging demands on traditional approaches for data processing and analysis. Machine learning algorithms are playing an increasingly important role in detecting and...

117. Identification of ultracool dwarfs in J-PLUS DR2 using Virtual Observatory tools and Machine Learning techniques

Pedro Mas Buitrago (Centro de Astrobiologia (INTA-CSIC))

30/05/2022, 14:59

Poster Presentation

The Javalambre Photometric Local Universe Survey Data Release 2 (J-PLUS DR2) covers 2\,176 deg$^2$ using a unique filter system of 12 optical bands. This large coverage of the electromagnetic spectrum allows a more accurate determination of physical parameters such as, for instance, effective temperatures.

Current surveys like J-PLUS, and others to come in the near future, are causing a...

57. Are large training samples always necessary for machine learning classification models?

Stavros Akras (Institute for Astronomy, Astrophysics, Space Applications and Remote Sensing - National Observatory of Athens)

30/05/2022, 15:02

Poster Presentation

Over the last 2 decades machine learning (ML) algorithms have become increasingly popular in astronomy. Several photometric sky-surveys have been conducted and even more are planned for the near future covering a large spectral range. To explore this large amount of data is necessary to apply automatic techniques.
In this talk, I will present the results of the application of ML algorithms...

158. Machine learning applied to X-ray spectra: separating stars from active galactic nuclei

Pavan Hebbar (University of Alberta)

30/05/2022, 15:05

Poster Presentation

Modern X-ray telescopes have detected hundreds of thousands of X-ray sources in the universe. However, current methods to classify these X-ray sources using the X-ray data themselves suffer problems — detailed X-ray spectroscopy of individual sources is too tedious, while hardness ratios often lack accuracy, and can be difficult to use effectively. These methods fail to use the power of X-ray...

156. Using Machine Learning to Identify Young Stars with TESS Data

Carlos Santiago (University of California, Irvine)

30/05/2022, 15:08

Poster Presentation

Observations of young stars are essential to developing our understanding of planet formation. Many young stars are found together in well-studied groups, clusters and associations. We aim to identify new candidate young stars, not necessarily in well-studied associations, which may otherwise be missed. While most studies use stellar colors to find young stars, we measure the variability...

137. Obtaining a classification of A-F stars through clustering analyzing the morphology of the light curves

Jose Ramon Rodon (IAA-CSIC)

30/05/2022, 15:11

Poster Presentation

Asteroseismology is experiencing a revolution thanks to high-precision asteroseismic space missions (Kepler, K2 and TESS) and their large ground-based monitoring programs. Those instruments have provided an unprecedented wealth of information which allows us to study statistical properties and search for hidden relationships between pulsation and/or physical observables.

Obtaining a large...