Contributions > By Session


Machine learning within the THREEHUNDERD simulation project Daniel de Andres, Gustavo Yepes, Weiguang Cui, Marco De Petris, Florian Ruppin sciencesconf.org:ml-iap2021:365878
Automated methods to find high-redshift quasars Lena Lenz, Daniel Mortlock, Boris Leistedt, Stephen Warren, Rhys Barnett sciencesconf.org:ml-iap2021:366348
Connecting the morphologies and dynamics of galaxies to their merger histories with deep learning Connor Bottrell, Maan Hani, Sara Ellison, David Patton, Hossen Teimoorinia sciencesconf.org:ml-iap2021:367438
A Method to Distinguish Quiescent and Dusty Star-forming Galaxies with t-SNE John Weaver, Charles Steinhardt, Jack Maxfield sciencesconf.org:ml-iap2021:367795
Using Machine Learning to Identify Periodicity. Niall Miller, Phil Lucas, Yi Sun sciencesconf.org:ml-iap2021:367797
Bayes vs. machine learning in large-scale survey demographics with eROSITA Johannes Buchner sciencesconf.org:ml-iap2021:367804
The miniJPAS survey: emission lines properties and SFR in theAEGIS field for galaxies withz<0.35 Ginés Martínez Solaeche sciencesconf.org:ml-iap2021:367849
Applying neural networks for the chemical analysis of star forming regions. Vital Fernández sciencesconf.org:ml-iap2021:367861
On the potential of machine learning in the classification of ultracool dwarfs with Euclid Eduardo Martin sciencesconf.org:ml-iap2021:367912
Machine Learning Calibration of Cosmic Shear Redshift Distributions Angus Wright sciencesconf.org:ml-iap2021:367923
Capturing the physics of MaNGA galaxies with self-supervised Machine Learning Regina Sarmiento, Marc Huertas-Company, Johan H. Knapen sciencesconf.org:ml-iap2021:367943
Machine Learning for Noise Correction and Classification Strategies for Astronomical Sources Nina Hernitschek, Keivan Stassun sciencesconf.org:ml-iap2021:367957
A Deep Learning Neural Network for Voigt Profile Fitting Quasar Absorption Lines Bryson Stemock, Christopher Churchill, Caitlin Doughty, Rogelio Ochoa, Sultan Hassan sciencesconf.org:ml-iap2021:367958
Single frequency CMB polarized foreground marginalization with a single training image Niall Jeffrey sciencesconf.org:ml-iap2021:367971
STag: Supernova Tagging and Classification William Davison, David Parkinson, Brad E. Tucker sciencesconf.org:ml-iap2021:367977
Tidal stream detection in HSC-SSP with Deep Learning Helena Domínguez Sánchez sciencesconf.org:ml-iap2021:367994
What drives the scatter in the BPT diagrams? A Machine Learning based analysis Mirko Curti sciencesconf.org:ml-iap2021:367995
Reconstructing blended galaxies with Machine Learning Lavanya Nemani, Emiliano Merlin, Adriano Fontana sciencesconf.org:ml-iap2021:368006
Photometric classification of compact galaxies, stars and quasars using multiple neural networks Siddharth Chaini, Atharva Bagul, Anish Deshpande, Rishi Gondkar, M Vivek, Kaushal Sharma, Ajit Kembhavi sciencesconf.org:ml-iap2021:368024
Identifying strong gravitational lenses in current and future large-scale imaging surveys Raoul Canameras sciencesconf.org:ml-iap2021:368092
AstronomicAL: an interactive dashboard for visualisation, integration and classification of data with Active Learning Grant Stevens, Sotiria Fotopoulou, Malcolm Bremer, Oliver Ray sciencesconf.org:ml-iap2021:368106
Machine-learning for real, messy data Sotiria Fotopoulou sciencesconf.org:ml-iap2021:368108
Parameter Estimation with Physics Informed Neural Networks Alex Lague, J. Richard Bond, Renee Hlozek sciencesconf.org:ml-iap2021:368111
The scattering transform in cosmology, or, a CNN without Training Sihao Cheng, Brice Ménard, Yuan-Sen Ting, Joan Bruna sciencesconf.org:ml-iap2021:368116
Mining Metals in DR16 Quasar Catalog with Gaussian Processes Reza Monadi, Simeon Bird sciencesconf.org:ml-iap2021:368121
Deep Neural Networks for detection of sources in radio astronomical maps: main challenges and current achievements. Renato Sortino, Daniel Magro, Carmelo Pino, Eva Sciacca, Simone Riggi, Giuseppe Fiameni sciencesconf.org:ml-iap2021:368150
Quantifying uncertainty in deep-learning classification of radio galaxies Fiona Porter, Anna Scaife sciencesconf.org:ml-iap2021:368195
Using a series Machine Learning models for the detection of high-redshift Radio Galaxy candidates. Rodrigo Carvajal, José Afonso, Israel Matute, Stergios Amarantidis, Davi Barbosa sciencesconf.org:ml-iap2021:368252
Beyond the Hubble Sequence - Exploring galaxy morphology with unsupervised machine learning Ting-Yun Cheng, Marc Huertas-Company, Christopher Conselice, Alfonso Aragon-Salamanca, Brant Robertson, Nesar Ramachandra sciencesconf.org:ml-iap2021:368288
Self-supervised learning for sky surveys George Stein sciencesconf.org:ml-iap2021:368294
Predicting Physical Properties of Lyman Alpha Forest with Deep Learning Ting-Yun Cheng, Ryan Cooke sciencesconf.org:ml-iap2021:368298
Gaia variable star classification: evolution and lessons learned Berry Holl, Lorenzo Rimoldini, Krzysztof Nienartowicz, Marc Audard, Panaiotis Gavras, Laurent Eyer, Nami Mowlavi, Gregory Jeverdat-de-Fombelle, Isabelle Lecoeur-Taibi sciencesconf.org:ml-iap2021:368302
Manifold learning to explore the galaxy parameter space Iary Davidzon, Keerthana Jegatheesan, Olivier Ilbert, Clotilde Laigle sciencesconf.org:ml-iap2021:368317
Mapping the stellar photometric metallicity of the Magellanic Clouds with machine learning Amy Miller, Guillaume Guiglion, Maria-Rosa L. Cioni, David Nidever, Richard de Grijs sciencesconf.org:ml-iap2021:368318
From machine learning to human understanding Daniel Masters sciencesconf.org:ml-iap2021:368330
Studying Morphology & Quenching of Galaxies in the All Sky-Era using Interpretable Bayesian Convolutional Neural Networks Aritra Ghosh, C. M. Urry sciencesconf.org:ml-iap2021:368331
Bridging the gap between synthetic and real data through unsupervised domain adaptation Spencer Bialek, Sébastien Fabbro, Kim Venn, Yuan-Sen Ting, Guillaume Payeur, Teaghan O'Briain sciencesconf.org:ml-iap2021:368332
Simulation and Segmentation with Deep Learning for Euclid Hubert Bretonnière sciencesconf.org:ml-iap2021:369290
An hybrid physics-ML framework to model exoplanetary light curves Mario Morvan, Angelos Tsiaras, Nikolaos Nikolaou, Ingo Waldmann sciencesconf.org:ml-iap2021:369780
Review on Exoplanets and Machine Learning Ingo Waldmann sciencesconf.org:ml-iap2021:383246
Review on Machine Learning for Radio-astronomy Michelle Lochner sciencesconf.org:ml-iap2021:383248

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