International Conference on Machine Learning for Astrophysics - ML4Astro

Session

Poster Session Day 2

31 May 2022, 14:30

Catania

139. Star- and planet formation caught-in-the-act

Gábor Marton (Konkoly Observatory, Research Centre for Astronomy and Earth Sciences)

31/05/2022, 14:30

Poster Presentation

Since the beginning of the last decade the analysis of huge amounts of data is a new challenge that researchers in general have to cope with. This is also true for astronomers and luckily the number of infrared facilities and the amount of data collected by them increased several order of magnitudes, leading us to new discoveries through data mining and knowledge discovery in databases using...

88. Classification of system variability using a convolutional neural network.

Jozef Magdolen (Advanced Technology Research Institute, Slovak University of Technology in Bratislava)

31/05/2022, 14:33

Poster Presentation

It is common that a binary system can report during observation some kind of variability. It may be caused by changing the actual luminosity of the observed object or due to changes in light that can reach the detector by blocking the source. Such variations have been observed in nova outbursts. Novae occur in interacting binaries where matter is accumulated from a companion star to the...

95. Time series anomaly detection in a cataclysmic variable using Support Vector Machine

Denis Benka (Advanced Technologies Research Institute)

31/05/2022, 14:36

Poster Presentation

Cataclysmic variable stars (CVs) often show multiple frequency components with a quasi-periodic occurrence. Those can be very subtle and their confidence using standard statistical methods is often of a less significance, e.g., falls under the 1-σ interval. In our study we aim to use a Support Vector Machine (SVM) to train a model to detect those components with a plausible confidence. We used...

87. Background Estimation in Fermi Gamma-ray Burst Monitor lightcurves through a Neural Network

Riccardo Crupi (Istituto Nazionale di Astrofisica (INAF))

31/05/2022, 14:39

Poster Presentation

The aim of this work is to provide a data-driven approach to estimate a background model for the Gamma-Ray Burst Monitor (GBM) of Fermi satellite. We employ a Neural Network (NN) to estimate each detector background signal given the information of the satellite: position, velocity, direction of the detectors, etc.
The estimated background can be employed into a triggering algorithm to...

134. Scope: the Zwicky Transient Facility Variable Source Classification Project

Joannes Van roestel (California Institute of Technology/University of Amsterdam), Zwicky Transient Facility collaboration

31/05/2022, 14:43

Poster Presentation

The Zwicky Transient Facility (ZTF) is an optical survey telescope that observes the northern sky every night. Lightcurves in $g$,$r$, and $i$ have been obtained of than a billion stars down to magnitude 20.5. Identification and classification of all variables in this huge dataset is required for multiple science cases. ZTF's volume of data, multicolour lightcurves, and (highly) irregular...

62. Reconstructing blended galaxies with Machine Learning

Lavanya Nemani (Istituto Nazionale di Astrofisica (INAF))

31/05/2022, 14:47

Poster Presentation

Galaxy blending is a confusion effect created by the projection of photons from galaxies on the same line of sight to the sky 2D plane (Dawson & Schneider 2014). The upcoming deep extragalactic surveys like LSST and EUCLID expect to see a blending fraction of up to 50% in the densest regions (Reiman & Göhre 2018). For standard aperture photometry and for more complex techniques such as...

144. Estimating Bayesian Posteriors for Galaxy Morphological Parameters using Machine Learning

Aritra Ghosh (Yale University)

31/05/2022, 14:50

Poster Presentation

Galaxy morphology is connected to various fundamental properties of a galaxy and its environment, such as galaxy mass, star formation rate, stellar kinematics, merger history, etc. Thus, studying the morphology of large samples of galaxies can be a crucial clue to understanding galaxy formation and evolution.

In the past few years, although machine learning has been increasingly used to...

93. Radio Galaxy detection prediction with ensemble Machine Learning.

Rodrigo Carvajal (Instituto de Astrofísica e Ciências do Espaço, Universidade de Lisboa)

31/05/2022, 14:53

Poster Presentation

The study of Active Galactic Nuclei is fundamental to comprehend the processes regarding the birth and evolution of Super-Massive Black Holes (SMBHs) and its connection with star-formation history and general galaxy evolution.

Up to this moment, only ~300 AGN have been identified in the EoR (z>6) of which a small fraction have radio detections, making it difficult to thoroughly study their...

80. Radio Image Segmentation with Variational Autoencoders

Hattie Stewart (University of Bristol)

31/05/2022, 14:57

Poster Presentation

The Square Kilometre Array (SKA) will be the world’s largest radio telescope, producing data at a rate of about 1Tb per second. Even after conversion to images, traditional methods of source detection and classification will not be sufficient. The pre-construction phase of the SKA project saw the launch of SKA Data Challenge 1 (SDC1), a model dataset released for analysis by the community....