Astroinformatics 2023

Self-Supervised Learning for Understanding Text, Imaging, Equations and Everything Else

Yann LeCun, New York University

DOI: https://doi.org/10.36116/VIDEOMEM_4.2023.1

Developments in Fast Machine Learning for Science

Matthew J. Graham, California Institute of Technology

DOI: https://doi.org/10.36116/VIDEOMEM_4.2023.2

Solving Differential Equations Using NN

Pavlos Protopapas, Harvard John A. Paulson School of Engineering and Applied Sciences

DOI: https://doi.org/10.36116/VIDEOMEM_4.2023.3

Deep Focus and ALMASim

Michele Delli Veneri, INFN-Sezione Napoli

DOI: https://doi.org/10.36116/VIDEOMEM_4.2023.4

GWA Gravitational-Wave Anomalous Knowledge with Recurrent Autoencoders

Ekaterina Govorkova, MIT

DOI: https://doi.org/10.36116/VIDEOMEM_4.2023.5

Unleashing the Power of Transformers for the Analysis of Cosmic Streams

Guillermo Cabrera, Universidad de Concepción

DOI: https://doi.org/10.36116/VIDEOMEM_4.2023.6

From Photometric Redshifts to Improved Weather Forecasts: an interdisciplinary view on machine learning in astronomy

Kai Polsterer, Heidelberg Institute for Theoretical Studies

DOI: https://doi.org/10.36116/VIDEOMEM_4.2023.7

Improvements to the ATLAS Real-Bogus Classifier

Joshua Weston, QUEENS UNIVERSITY BELFAST

DOI: https://doi.org/10.36116/VIDEOMEM_4.2023.8

Unsupervised learning for agnostic knowledge discovery from simulations

Sebastian Trujillo-Gomez, Heidelberg Institute for Theoretical Studies

DOI: https://doi.org/10.36116/VIDEOMEM_4.2023.9

Enabling New Discoveries with Machine Learning

Michelle Lochner, University of the Western Cape/South African Radio Astronomy Observatory

DOI: https://doi.org/10.36116/VIDEOMEM_4.2023.10

Reliable QSO classification in ZTF and KiDS surveys

Szymon Nakoneczny, California Institute of Technology

DOI: https://doi.org/10.36116/VIDEOMEM_4.2023.11

SOM MAPS FOR OUTLIER. CLASSIFICATION IN GAIA

Lara Pallas Quintela, University of A Coruña

DOI: https://doi.org/10.36116/VIDEOMEM_4.2023.12

From HST to JWST and Euclid: new algorithms for the study of the first galaxies

Emiliano Merlin, INAF - OAR

DOI: https://doi.org/10.36116/VIDEOMEM_4.2023.13

The Importance of Being Interpretable: ML as a partner in cosmological discovery

Michelle Ntampaka, SPACE TELESCOPE SCIENCE INSTITUTE

DOI: https://doi.org/10.36116/VIDEOMEM_4.2023.14

Discovering black hole mass scaling relations with Symbolic Regression

Zeaho Jin, New York University Abu Dhabi

DOI: https://doi.org/10.36116/VIDEOMEM_4.2023.15

Novel Computer Vision Applications for Radio Astronomy with-ASKAP

Nikhel Gupta, CSIRO Space & Astronomy ML/AI Future Science Platform (MLAI FSP)

DOI: https://doi.org/10.36116/VIDEOMEM_4.2023.16

Machine Learning the Universe with upcoming Large Sky Surveys

Nicola Napolitano, Department of Physics, University of Naples

DOI: https://doi.org/10.36116/VIDEOMEM_4.2023.17

Deep learning techniques to analyse high energy data of the AGILE space mission

Nicolò Parmiggiani, INAF-OAS

DOI: https://doi.org/10.36116/VIDEOMEM_4.2023.18

Cluster member detection with Faster Region. Convolution Neural Networks in HST Galaxy Clusters

Giuseppe Angora, University of Ferrara

DOI: https://doi.org/10.36116/VIDEOMEM_4.2023.19

Detection, instance segmentation, and classification with deep learning (DeepDISC)

Grant Merz, University of Illinois

DOI: https://doi.org/10.36116/VIDEOMEM_4.2023.20

Cavity Detection Tool. CADET

Martin Topinka, INAF-Osservatorio Astronomico di Cagliari

DOI: https://doi.org/10.36116/VIDEOMEM_4.2023.21

Coupling Exploratory Data Analysis and mL-Ops

Gennaro Zanfardino, Virtualitics, Inc.

DOI: https://doi.org/10.36116/VIDEOMEM_4.2023.22

Hybrid approach to improve deblending performance using machine learning along with traditional tools

Fernando Caro, INAF - Osservatorio Astronomico di Roma

DOI: https://doi.org/10.36116/VIDEOMEM_4.2023.23

Multidimensional Faint Object. Detection in Astronomical Image Data

Mohammad H. Faezi, University of Groningen

DOI: https://doi.org/10.36116/VIDEOMEM_4.2023.24

Representation Learning for Unsupervised Learning In Astronomy

Koketso Mohale, UNIVERSITY of the WESTERN CAPE

DOI: https://doi.org/10.36116/VIDEOMEM_4.2023.25

Training on the interface of astronomy and computer science

Johan Knapen, Instituto de Astrofísica de Canarias

DOI: https://doi.org/10.36116/VIDEOMEM_4.2023.26

Navigating Global Policy and Diplomacy through Astroinformatics: Insights from Science20

Pranav Sharma, INDIAN NATIONAL SCIENCE ACADEMY

DOI: https://doi.org/10.36116/VIDEOMEM_4.2023.27

Semi-Supervised Learning for Detecting Gravitationally Lensed Quasars

David Sweeney, THE UNIVERSITY OF SYDNEY

DOI: https://doi.org/10.36116/VIDEOMEM_4.2023.28

A BRAIN STUDY TO TACKLE IMAGING WITH ARTIFICIAL INTELLIGENCE IN THE ALMA 2030 ERA

Fabrizia Guglielmetti, INAF - Osservatorio Astronomico di Capodimonte

DOI: https://doi.org/10.36116/VIDEOMEM_4.2023.29

Anomaly detection: overview and application to ZTF

Ashish Mahabal, California Institute of Technology

DOI: https://doi.org/10.36116/VIDEOMEM_4.2023.30

ASTRONOMALY at Scale: Searching for Anomalies Amongst 4 Million Galaxies

Verlon Etsebeth, Department of Physics and Astronomy, University of the Western Cape, South Africa

DOI: https://doi.org/10.36116/VIDEOMEM_4.2023.31

Probabilistic Cross Correlation for Delay Estimation

Nikos Gianniotis, Heidelberg Institute for Theoretical Studies

DOI: https://doi.org/10.36116/VIDEOMEM_4.2023.32

Connected Morphological Operators in Astronomy: Tools for Object Detection and Pattern Analysis on Vast Data

Michael Wilkinson, University of Groningen, The Netherlands

DOI: https://doi.org/10.36116/VIDEOMEM_4.2023.33

Searching for long faint astronomical high energy transients: a data driven approach

Riccardo Crupi, Università degli Studi di Udine

DOI: https://doi.org/10.36116/VIDEOMEM_4.2023.34

Characterisation of the Hi-GAL clumps parameter space through experiments of Feature Selection

Ylenia Maruccia, INAF - Istituto di Astrofisica e Planetologia Spaziali

DOI: https://doi.org/10.36116/VIDEOMEM_4.2023.35

Quantum Computational Intelligence

Giovanni Acampora, University of Naples Federico Il

DOI: https://doi.org/10.36116/VIDEOMEM_4.2023.36

Quantum computing for natural sciences and machine learning applications

Francesco Tacchini, IBM Quantum, IBM Research Europe - Zurich

DOI: https://doi.org/10.36116/VIDEOMEM_4.2023.37

A Planar Black Hole Mass Scaling Relation

Benjamin Davis, Center for Astrophysics and Space Science, New York University Abu Dhabi

DOI: https://doi.org/10.36116/VIDEOMEM_4.2023.38

Detecting transients in count time series with Poisson-FOCuS

Giuseppe Dilillo, INAF - Istituto di Astrofisica e Planetologia Spaziali

DOI: https://doi.org/10.36116/VIDEOMEM_4.2023.39

Finding Optically Variable AGN with Machine Learning Techniques (an ongoing work)

Demetra De Cicco, Universita degii Studi di Napoii "Federico II"

DOI: https://doi.org/10.36116/VIDEOMEM_4.2023.40

Predicting the redshift of AGNs using SuperLearner

Aditya Narendra, Astronomical Observatory of Jagiellonian University Krakow, Poland

DOI: https://doi.org/10.36116/VIDEOMEM_4.2023.41

Solving Large-scale Data Challenges with ESA Datalabs

Pablo Gómez, Data Science Section, SCI-SAS ESA ESAC

DOI: https://doi.org/10.36116/VIDEOMEM_4.2023.42

From Source Detection to Rare Object Detection

Samira Rezaei, Leiden Institute for Advance Computer Science

DOI: https://doi.org/10.36116/VIDEOMEM_4.2023.43

Explaining the predictions of a machine learning classifier for gravitational wave events

Nayyer Raza, McGill University

DOI: https://doi.org/10.36116/VIDEOMEM_4.2023.44

AIDA. A versatile software framework for astronomical scientific and instrumentation data analysis.

Giuseppe Riccio, INAF - Astronomical Observatory of Capodimorite, Napoli

DOI: https://doi.org/10.36116/VIDEOMEM_4.2023.45

Planet Detection via High-Contrast Imaging Using Deep Learning

Cristobal Donoso-Oliva, INAF - Osservatorio Astronomico di Capodimonte

DOI: https://doi.org/10.36116/VIDEOMEM_4.2023.46

Machine Learning approach to Sunyaev-Zel'dovich cluster radial mass profiles

Antonio Ferragamo, Instituto do Astrofsica da Canarias

DOI: https://doi.org/10.36116/VIDEOMEM_4.2023.47

Joint machine learning and analytic track reconstruction for X-ray polarimetry with Gas Pixel Detectors

Nicolò Cibrario, Università degli Studi di Torino

DOI: https://doi.org/10.36116/VIDEOMEM_4.2023.48