International Conference on Machine Learning for Astrophysics 2nd Ed. - ML4ASTRO2

Session

FlashTalks: Other

11 Jul 2024, 11:50

Conveners

FlashTalks: Other

• Filomena Bufano (Istituto Nazionale di Astrofisica (INAF))

270. Predicting the Ages of Galaxies with an Artificial Neural Network

Laura Hunt (University of Hull)

11/07/2024, 11:50

Poster Presentation

We present a new method of predicting the ages of galaxies using an artificial neural network (ANN) with an evaluation of the methods such that future work can be derived for other properties of galaxies with ANNs. Ideally, we would be able to quickly and accurately determine individual properties of galaxies such as age on a large scale for use in studies. However, we currently rely on time...

272. Machine learning-based photometric classification of galaxies, quasars and stars

Donato Cascio (University of Palermo)

11/07/2024, 11:55

Poster Presentation

The vastness of astronomical datasets and the intricate nature of celestial objects create a pressing demand for robust classification techniques. Machine learning emerges as a powerful solution in this context, capable of discerning complex patterns and classifications. The work we will present delves into the application of machine learning techniques for classifying astronomical sources...

273. Analysis of Velocity Distribution Functions using the Gaussian Mixture Model

Beniamino Sanò (Università di Trento)

11/07/2024, 12:00

Poster Presentation

Velocity distribution functions (VDFs) measured by the Magnetospheric Multiscale (MMS) mission are complex 3D datasets that can be represented as a superposition of multiple beams (Goldman et al., 2020). Recent papers (Dupuis et al., 2020; De Marco et al., 2023) proposed the use of the Gaussian Mixture Model (GMM) to identify different populations. The method is applied systematically to MMS...

280. Probing AGN dynamics through a natural language processor lens

Ben Rouse (Pontificia Universidad Catolica de Chile)

11/07/2024, 12:05

We employ natural language processing algorithms with attention, repurposed to receive QSO spectra to predict unseen spectra, broad lines, and super massive black hole masses. We find that the trained algorithm is able to reproduce with high significance masked broad lines and/or continua in QSO spectra, highlighting an ability to learn from and leverage physical information imprinted amongst...

274. FlashTalks Q&A

269. TBC

Poster Presentation

271. Unmasking the Hidden: Anomaly Detection in ASKAP’s Monitoring

Zhuowei Wang

Poster Presentation

This study explores the intersection of astronomy and human-machine collaboration, focusing on their transformative role in our exploration of the universe. We examine the Australian Square Kilometre Array Pathfinder (ASKAP) and its monitoring data's crucial role in advancing astronomical discoveries. However, ASKAP's success in charting unprecedented numbers of galaxies presents the challenge...