Courses

The aim of this course is to give a general and practical introduction to Deep Learning tools and data treatment with a focus on Astronomy and physics examples. We plan 4 classes, and in addition, we plan to offer time for tutoring and hands-on.

To be approved in this course, the student will have 30 days from Oct 5 to present a report with an application of AI techniques presented in the course. All students will also make a 5-10 minutes video describing the project and results. We will organize a virtual meeting on a date to be determined for presentation of the videos and discussion of all projects.

Postdocs as well as PhD and undergraduate students of Astronomy, Geophysics and Meteorology are welcome to attend this course. Researchers are also welcome to attend but priority will be given to postdocs and students.

If you are interested in attending this course, please contact Dr. Analía Smith Castelli (asmith@fcaglp.unlp.edu.ar).

Software y Requerimientos Informáticos

Se utilizará Google Colab por lo que no se requiere de la descarga de ningun software específico, por el momento. Para las clases prácticas se recomienda contar con laptop, aunque no es imprescindible. Se planea que el material de las clases teóricas y prácticas se haga disponible a través de esta página.

The objective of this course is to give a general and practical introduction to data science in Astronomy, with focus on machine learning tools. We want to give some context on statistical techniques used by astronomers and present machine learning procedures useful for the analysis in astronomy. We plan 4 classes, with one hour of "theory" and one hour of applications using Python.

To be approved in this course, after the classes the student will have 10 days to present a report with an application of ML techniques. All students will also make a 5-10 minutes video describing the project and results. We will organize a virtual meeting on a date to be determined for presentation of the videos and discussion of all projects.

Postdocs as well as PhD and undergraduate students of Astronomy, Geophysics and Meteorology are welcome to attend this course. Researchers are also welcome to attend but priority will be given to postdocs and students. 

If you are interested in attending this course, please contact Dr. Analía Smith Castelli (asmith@fcaglp.unlp.edu.ar).

Software

Si no se dispone de conda, primero instalarlo (si ya se encuentra instalado, continuar con las instrucciones (*) a partir de conda create...):

Descargar desde https://docs.conda.io/en/latest/miniconda.html#latest-miniconda-installer-links la versión apropiada para su sistema operativo.

Para iniciar el instalador, desde una terminal hacer:

bash ~/Descargas/Miniconda3-py39_4.12.0-Linux-x86_64.sh

Seguir las instrucciones (si surgen dudas, elegir la opciones por defecto). Cuando pregunte si desea inicializar conda (Do you wish the installer to initialize [...]) responder que si.

Una vez finalizada la instalación continuar en una nueva terminal.

Para evitar que conda inicie con el entorno base activado, hacer:

conda config --set auto_activate_base false

(*)Luego, para crear un entorno con el software del curso:

conda create -n cursoML  python=3.9 scikit-learn tensorflow matplotlib seaborn pandas joblib notebook

Y finalmente para poder usarlo:

conda activate cursoML

jupyter notebook

NOTA 1

La lista de paquetes que figuran en la instrucción conda create es la enviada por los docentes del curso. Si llegara a necesitarse algo más, se resolverá al inicio del mismo.

Para sumar paquetes al entorno creado:

conda install -n cursoML lista de paquetes

NOTA 2

Para instalar xgboost hacer:

conda activate cursoML

pip install xgboost

Consultas: juanirod@fcaglp.unlp.edu.ar

Papers for ML Project

Context: Any scientific problem starts with a scientific question and the identification of one or more strategies to try to answer it, in our case by using machine learning (ML). In this course we will ask you to develop a small ML project, within a subject of your interest. Notice that in general (but not always!) ML requires lots of data to provide sensible answers, so take this into account. To help you to identify a ML project, we present bellow a list of papers that uses ML, recently posted in arXiv (September 22 - November 24; incomplete list). Notice that some of them adopt techniques that we have never worked with! We hope this helps!

List of papers:

arXiv:2012.05820 - Hybrid analytic and machine-learned baryonic property insertion into galactic dark matter haloes. Authors: Ben Moews, Romeel Davé, Sourav Mitra, Sultan Hassan, Weiguang Cui

arXiv:2107.02304 - The GIGANTES Data Set: Precision Cosmology from Voids in the Machine-Learning Era. Authors: Christina D. Kreisch, Alice Pisani, Francisco Villaescusa-Navarro, David N. Spergel, Benjamin D. Wandelt, Nico Hamaus, and Adrian E. Bayer

arXiv:2107.05771 - Deep learning reconstruction of the large scale structure of the Universe from luminosity distance observations. Authors: Cristhian García, Camilo Santa, Antonio Enea Romano

arXiv:2108.07749 - AGNet: Weighing Black Holes with Deep Learning. Authors: Joshua Yao-Yu Lin, Sneh Pandya, Devanshi Pratap, Xin Liu, Matias Carrasco Kind, Volodymyr Kindratenko

arXiv:2111.02422 - Modeling the galaxy-halo connection with machine learning. Authors: Ana Maria Delgado, Digvijay Wadekar, Boryana Hadzhiyska, Sownak Bose, Lars Hernquist, Shirley Ho

arXiv:2201.05734 - Fast and Flexible Analysis of Direct Dark Matter Search Data with Machine Learning. Authors: LUX Collaboration: D.S. Akerib, S. Alsum, H.M. Araújo, X. Bai, et al.

arXiv:2203.03651 - Deep learning and Bayesian inference of gravitational-wave populations: Hierarchical black-hole mergers. Authors: Matthew Mould, Davide Gerosa, Stephen R. Taylor

arXiv:2203.12702 - Modelling the galaxy-halo connection with semi-recurrent neural networks. Authors: Harry George Chittenden, Rita Tojeiro

arXiv:2203.16607 - A Self-Learning Neural Network Approach for RFI Detection and Removal in Radio Astronomy. Authors: Benjamin R. B. Saliwanchik and Anvze Slosar

arXiv:2204.10751 - Machine Learning methods to estimate observational properties of galaxy clusters in large volume cosmological N-body simulations. Authors: Daniel de Andres, Gustavo Yepes, Federico Sembolini, Gonzalo Martínez-Muñoz, Weiguang Cui, Francisco Robledo, Chia-Hsun Chuang and Elena Rasia

arXiv:2205.01677 - ASTROMER: A transformer-based embedding for the representation of light curves. Authors: C. Donoso-Oliva, I. Becker, P. Protopapas, G. Cabrera-Vives, Vishnu M., Harsh Vardhan

arXiv:2205.05307 - The AGEL Survey: Spectroscopic Confirmation of Strong Gravitational Lenses in the DES and DECaLS Fields Selected Using Convolutional Neural Networks. Authors: Kim-Vy H. Tran, Anishya Harshan, Karl Glazebrook, G. C. Keerthi Vasan, Tucker Jones, Colin Jacobs, Glenn G. Kacprzak, Tania M. Barone, Thomas E. Collett, Anshu Gupta, Astrid Henderson, Lisa J. Kewley, Sebastian Lopez, Themiya Nanayakkara, Ryan L. Sanders, and Sarah M. Sweet

arXiv:2205.05952 - A method for approximating optimal statistical significances with machine-learned likelihoods. Authors: Ernesto Arganda, Xabier Marcano, Víctor Martín Lozano, Anibal D. Medina, Andres D. Perez, Manuel Szewc, Alejandro Szynkman

arXiv:2205.08733 - Identification of Grand-design and Flocculent Spirals from SDSS using Convolutional Neural network. Authors: Suman Sarkar, Ganesh Narayanan, Arunima Banerjee, Prem Prakash

arXiv:2207.05107 - GaMPEN: A Machine-learning Framework for Estimating Bayesian Posteriors of Galaxy Morphological Parameters. Authors: Aritra Ghosh, C. Megan Urry, Amrit Rau, Laurence Perreault-Levasseur, Miles Cranmer, Kevin Schawinski, Dominic Stark, Chuan Tian, Ryan Ofman, Tonima Tasnim Ananna, Connor Auge, Nico Cappelluti, David B. Sanders, Ezequiel Treister

arXiv:2207.14324 - A Machine Learning Approach to Enhancing eROSITA Observations. Authors: John Soltis, Michelle Ntampaka, John Wu, John ZuHone, August Evrard, Arya Farahi, Matthew Ho and Daisuke Nagai

arXiv:2209.02817 - Deducing Neutron Star Equation of State Parameters Directly From Telescope Spectra with Uncertainty-Aware Machine Learning. Authors: Delaney Farrell, Pierre Baldi, Jordan Ott, Aishik Ghosh, Andrew W.   Steiner, Atharva Kavitkar, Lee Lindblom, Daniel Whiteson, Fridolin Weber

arXiv:2209.03042 - Graph Neural Networks for Low-Energy Event Classification & Reconstruction in IceCube. Authors: R. Abbasi, M. Ackermann, J. Adams, N. Aggarwal, J. A. Aguilar, et al.

arXiv:2209.04430 - Investigation of a Machine learning methodology for the SKA pulsar search pipeline. Authors: Shashank Sanjay Bhat, Thiagaraj Prabu, Ben Stappers, Atul Ghalame, Snehanshu Saha, T.S.B Sudarshan, Zafiirah Hosenie

arXiv:2209.07257 - The probabilistic random forest applied to the QUBRICS survey: improving the selection of high-redshift quasars with synthetic data. Authors: Francesco Guarneri, Giorgio Calderone, Stefano Cristiani, Matteo Porru, Fabio Fontanot, Konstantina Boutsia, Guido Cupani, Andrea Grazian, Valentina D'Odorico, Michael T. Murphy, Angela Bongiorno, Ivano Saccheo, Luciano Nicastro

arXiv:2209.08940 - Gaussian Process regression for astronomical time-series. Authors: Suzanne Aigrain and Daniel Foreman-Mackey. Source code:  https://github.com/dfm/araa-gps

arXiv:2209.09957 - Semi-Supervised Classification and Clustering Analysis for Variable Stars. Authors: R. Pantoja, M. Catelan, K. Pichara, P. Protopapas

arXiv:2209.10161 - The PAU Survey & Euclid: Improving broad-band photometric redshifts with multi-task learning. Authors: L. Cabayol, M. Eriksen, J. Carretero, R. Casas, F.J. Castander, E. Fernández, J. Garcia-Bellido, E. Gaztanaga, H. Hildebrandt, H. Hoekstra, B. Joachimi, R. Miquel, C.Padilla, A. Pocino, E. Sanchez, S. Serrano, I., et al.

arXiv:2209.10333 - A Deep Learning Approach to Infer Galaxy Cluster Masses from Planck Compton-y parameter maps. Authors: Daniel de Andres, Weiguang Cui, Florian Ruppin, Marco De Petris, Gustavo Yepes, Giulia Gianfagna, Ichraf Lahouli, Gianmarco Aversano, Romain Dupuis, Mahmoud Jarraya, and Jesús Vega-Ferrero

arXiv:2209.11146 - MLGWSC-1: The first Machine Learning Gravitational-Wave Search Mock Data Challenge. Authors: Marlin B. Sch\"afer, Ondvrej Zelenka, Alexander H. Nitz, He Wang, Shichao Wu, Zong-Kuan Guo, Zhoujian Cao, Zhixiang Ren, Paraskevi Nousi, Nikolaos Stergioulas, Panagiotis Iosif, Alexandra E. Koloniari, Anastasios Tefas, Nikolaos Passalis, Francesco Salemi, Gabriele Vedovato, Sergey Klimenko, Tanmaya Mishra, Bernd Br\"ugmann, Elena Cuoco, E. A. Huerta, Chris Messenger, Frank Ohme

arXiv:2209.11248 - Comparing simulated Milky Way satellite galaxies with observations using unsupervised clustering. Authors: Li-Hsin Chen, Tilman Hartwig, Ralf S. Klessen, and Simon C. O. Glover

arXiv:2209.12194 - Machine learning technique for morphological classification of galaxies from the SDSS. III. Image-based inference of detailed features. Authors: V. Khramtsov, I.B. Vavilova, D.V. Dobrycheva, M.Yu. Vasylenko, O.V. Melnyk, A.A. Elyiv, V.S. Akhmetov, A.M. Dmytrenko

arXiv:2209.12799 - Machine learning constraints on deviations from general relativity from the large scale structure of the Universe. Authors: George Alestas, Lavrentios Kazantzidis and Savvas Nesseris

arXiv:2209.13592 - DVGAN: Stabilize Wasserstein GAN training for time-domain Gravitational Wave physics. Authors: Tom Dooney, Stefano Bromuri, Lyana Curier

arXiv:2209.13603 - Scalable and Equivariant Spherical CNNs by Discrete-Continuous (DISCO) Convolutions. Authors: Jeremy Ocampo, Matthew A. Price, Jason D. McEwen

arXiv:2209.14113 - Improving ANAIS-112 sensitivity to DAMA/LIBRA signal with machine learning techniques. Authors: I. Coarasa, J. Apilluelo, J. Amaré, S. Cebrián, D. Cintas, E. García, M. Martínez, M. A. Oliván, Y. Ortigoza, A. Ortiz de Solórzano, T. Pardo, J. Puimedón, A. Salinas, M. L. Sarsa, P. Villar

arXiv:2209.14226 - Radio source-component association for the LOFAR Two-metre Sky Survey with region-based convolutional neural networks. Authors: Rafa\"el I.J. Mostert, Kenneth J. Duncan, Lara Alegre, Huub J.A. R\"ottgering, Wendy L. Williams, Philip N. Best, Martin J. Hardcastle, Raffaella Morganti

arXiv:2209.14323 - Neural Stellar Population Synthesis Emulator for the DESI PROVABGS. Authors: K. J. Kwon, ChangHoon Hahn and Justin Alsing.

arXiv:2209.15112 - Machine-learning classification of astronomical sources: estimating F1-score in the absence of ground truth. Authors: A. Humphrey, W. Kuberski, J. Bialek, N. Perrakis, W. Cools, N. Nuyttens, H. Elakhrass, P.A.C. Cunha

arXiv:2210.00632 - A deep learning approach for focal-plane wavefront sensing using vortex phase diversity. Authors: M. Quesnel, G. Orban de Xivry, G. Louppe, O. Absil

arXiv:2210.00869 - Explainable classification of astronomical uncertain time series. Authors: Michael Franklin Mbouopda, Emille E O Ishida, Engelbert Mephu Nguifo, Emmanuel Gangler

arXiv:2210.01431 - Machine Learning based search for Cataclysmic Variables within Gaia Science Alerts. Authors: D. Mistry, C. M. Copperwheat, M. J. Darnley, I. Olier

arXiv:2210.01666 - Neural Network Based Point Spread Function Deconvolution For Astronomical Applications. Authors: Hong Wang, Sreevarsha Sreejith, Yuewei Lin, Nesar Ramachandra, Anvze Slosar and Shinjae Yoo

arXiv:2210.01813 - The DAWES review 10: The impact of deep learning for the analysis of galaxy surveys. Authors: Marc Huertas-Company and Francois Lanusse

arXiv:2210.01827 - An Interpretable Machine Learning Framework for Modeling High-Resolution Spectroscopic Data. Authors: Michael A. Gully-Santiago and Caroline V. Morley

arXiv:2210.04143 - Strong Gravitational Lensing Parameter Estimation with Vision Transformer. Authors: Kuan-Wei Huang, Geoff Chih-Fan Chen, Po-Wen Chang, Sheng-Chieh Lin, Chia-Jung Hsu, Vishal Thengane, Joshua Yao-Yu Lin

arXiv:2210.04168 - Galaxy Spin Classification I: Z-wise vs S-wise Spirals With Chirality Equivariant Residual Network. Authors: He Jia, Hong-Ming Zhu, Ue-Li Pen

arXiv:2210.04228 - Deep learning method in testing the cosmic distance duality relation. Authors: Li Tang, Hai-Nan Lin, Liang Liu

arXiv:2210.04588 - A convolutional neural network to distinguish glitches from minute-long gravitational wave transients. Authors: Vincent Boudart

arXiv:2210.04896 - Discovering Ca II Absorption Lines With a Neural Network. Authors: Iona Xia, Jian Ge, Kevin Willis and Yinan Zhao

arXiv:2210.05686 - Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference. Authors: Maximilian Dax, Stephen R. Green, Jonathan Gair, Michael P\"urrer, Jonas Wildberger, Jakob H. Macke, Alessandra Buonanno, Bernhard Sch\"olkopf

arXiv:2210.06170 - Contrastive Neural Ratio Estimation. Authors: Benjamin Kurt Miller, Christoph Weniger, Patrick Forré

arXiv:2210.07391 - CMB Delensing with Neural Network Based Lensing Reconstruction in the Presence of Primordial Tensor Perturbations. Authors: Chen Heinrich, Trey Driskell and Chris Heinrich

arXiv:2210.08382 - Machine-Learning Love: classifying the equation of state of neutron stars with Transformers. Authors: Gon\c{c}alo Gon\c{c}alves, Márcio Ferreira, Jo\~ao Aveiro, Antonio Onofre, Felipe F. Freitas, Constan\c{c}a Providencia, José A. Font

arXiv:2210.10052 - Data-driven Cosmology from Three-dimensional Light Cones. Authors: Yun-Ting Cheng, Benjamin D. Wandelt, Tzu-Ching Chang, Olivier Dore

arXiv:2210.10793 - LeMoN: Lens Modelling with Neural networks -- I. Automated modelling of strong gravitational lenses with Bayesian Neural Networks. Authors: Fabrizio Gentile, Crescenzo Tortora, Giovanni Covone, León V.E.   Koopmans, Rui Li, Laura Leuzzi and Nicola R. Napolitano

arXiv:2210.10802 - Identification of Galaxy-Galaxy Strong Lens Candidates in the DECam Local Volume Exploration Survey Using Machine Learning. Authors: E. Zaborowski, A. Drlica-Wagner, F. Ashmead, J. F. Wu, R. Morgan, C. R. Bom, et al.

arXiv:2210.10810 - Identifying Tidal Disruption Events with an Expansion of the FLEET Machine Learning Algorithm. Authors: Sebastian Gomez, V. Ashley Villar, Edo Berger, Suvi Gezari, Sjoert van Velzen, Matt Nicholl, Peter K. Blanchard, and Kate. D. Alexander

arXiv:2210.11236 - Optical turbulence forecast over short timescales using machine learning techniques. Authors: A. Turchi, E. Masciadri, L. Fini

arXiv:2210.11428 - Analysis of Ring Galaxies Detected Using Deep Learning with Real and Simulated Data. Authors: Harish Krishakumar and J. Bryce Kalmbach

arXiv:2210.11487 - New applications of Graph Neural Networks in Cosmology. Authors: Farida Farsian, Federico Marulli, Lauro Moscardini, Carlo Giocoli

arXiv:2210.12264 - A machine learning approach to assessing the presence of substructure in quasar host galaxies using the Hyper Suprime-Cam Subaru Strategic Program. Authors: Chris Nagele, John D. Silverman, Tilman Hartwig, Junyao Li, Connor Bottrell, Xuheng Ding, Yoshiki Toba

arXiv:2210.12791 - O-type Stars Stellar Parameter Estimation Using Recurrent Neural Networks. Authors: Miguel Flores R., Luis J. Corral, Celia R. Fierro-Santill\'an and Silvana G. Navarro

arXiv:2210.12931 - Removing Radio Frequency Interference from Auroral Kilometric Radiation with Stacked Autoencoders. Authors: Allen Chang, Mary Knapp, James LaBelle, John Swoboda, Ryan Volz, Philip J. Erickson

arXiv:2210.13473 - Mangrove: Learning Galaxy Properties from Merger Trees. Authors: Christian Kragh Jespersen, Miles Cranmer, Peter Melchior, Shirley Ho, Rachel S. Somerville, Austen Gabrielpillai

arXiv:2210.14933 - Stokes inversion techniques with neural networks: analysis of uncertainty in parameter estimation. Authors: Lukia Mistryukova, Andrey Plotnikov, Aleksandr Khizhik, Irina Knyazeva, Mikhail Hushchyn and Denis Derkach

arXiv:2210.15888 - Deep Learning Detection and Classification of Gravitational Waves from Neutron Star-Black Hole Mergers. Authors: Richard Qiu, Plamen Krastev, Kiranjyot Gill, Edo Berger

arXiv:2210.16060 - Deep network series for large-scale high-dynamic range imaging. Authors: Amir Aghabiglou, Matthieu Terris, Adrian Jackson, Yves Wiaux

arXiv:2210.16440 - ODNet: A Convolutional Neural Network for Asteroid Occultation Detection. Authors: Dorian Cazeneuve, Franck Marchis, Guillaume Blaclard, Paul A. Dalba, Victor Martin, Joé Asencioa

arXiv:2210.17470 - Deep Learning application for stellar parameters determination: II - Application to observed spectra of AFGK stars. Authors: Marwan Gebran, Frédéric Paletou, Ian Bentley, Rose Brienza, Kathleen Connick

arXiv:2211.00024 - A robust estimator of mutual information for deep learning interpretability. Authors: Davide Piras, Hiranya V. Peiris, Andrew Pontzen, Luisa Lucie-Smith, Ningyuan Guo, Brian Nord

arXiv:2211.00047 - Optimizing machine learning methods to discover strong gravitational lenses in the Deep Lens Survey. Authors: Keerthi Vasan G.C., Stephen Sheng, Tucker Jones, Chi Po Choi and James Sharpnack

arXiv:2211.00397 - Galaxy classification: a deep learning approach for classifying Sloan Digital Sky Survey images. Authors: Sarvesh Gharat and Yogesh Dandawate

arXiv:2211.00677 - Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection. Authors: Aleksandra Ćiprijanović and Ashia Lewis and Kevin Pedro and Sandeep Madireddy and Brian Nord and Gabriel N. Perdue and Stefan Wild

arXiv:2211.01567 - Galaxy Image Deconvolution for Weak Gravitational Lensing with Physics-informed Deep Learning. Authors: Tianao Li and Emma Alexander

arXiv:2211.02045 - Fast and robust Bayesian Inference using Gaussian Processes with GPry. Authors: Jonas El Gammal, Nils Sch\"oneberg, Jesús Torrado, Christian Fidler

arXiv:2211.02062 - A unique, ring-like radio source with quadrilateral structure detected with machine learning. Authors: Michelle Lochner, Lawrence Rudnick, Ian Heywood, Kenda Knowles and Stanislav S. Shabala

arXiv:2211.03796 - Astronomia ex machina: a history, primer, and outlook on neural networks in astronomy. Authors: Michael J. Smith, James E. Geach

arXiv:2211.04260 - PhotoRedshift-MML: a multimodal machine learning method for estimating photometric redshifts of quasars. Authors: Shuxin Hong, Zhiqiang Zou, A-Li Luo, Xiao Kong, Wenyu Yang and Yanli Chen

arXiv:2211.04291 - Detection is truncation: studying source populations with truncated marginal neural ratio estimation. Authors: Noemi Anau Montel, Christoph Weniger

arXiv:2211.04488 - Deblending Galaxies with Generative Adversarial Networks. Authors: Shoubaneh Hemmati, Eric Huff, Hooshang Nayyeri, Agnés Ferté, Peter Melchior, Bahram Mobasher, Jason Rhodes, Abtin Shahidi, Harry Teplitz

arXiv:2211.05000 - Emulating cosmological multifields with generative adversarial networks. Authors: Sambatra Andrianomena, Francisco Villaescusa-Navarro, Sultan Hassan

arXiv:2211.05064 - Test of Artificial Neural Networks in Likelihood-free Cosmological Constraints: A Comparison of IMNN and DAE. Authors: JieFeng Chen, YuChen Wang, Tingting Zhang, TongJie Zhang

arXiv:2211.05242 - A simulator-based autoencoder for focal plane wavefront sensing. Authors: Maxime Quesnel, Gilles Orban de Xivry, Olivier Absil, Gilles Louppe

arXiv:2211.05556 - Generating astronomical spectra from photometry with conditional diffusion models. Authors: Lars Doorenbos, Stefano Cavuoti, Giuseppe Longo, Massimo Brescia, Raphael Sznitman, Pablo Márquez-Neila

arXiv:2211.06002 - Hierarchical Clustering in Astronomy. Authors: Heng Yu and Xiaolan Hou

arXiv:2211.06393 - Using machine learning to compress the matter transfer function T(k). Authors: J. Bayron Orjuela-Quintana and Savvas Nesseris and Wilmar Cardona

arXiv:2211.06564 - Emulating cosmological growth functions with B-Splines. Authors: Ngai Pok Kwan, Chirag Modi, Yin Li, Shirley Ho

arXiv:2211.06777 - Searching for Barium Stars from the LAMOST Spectra Using the Machine Learning Method: I. Authors: Fengyue Guo, Zhongding Cheng, Xiaoming Kong, Yatao Zhang, Yude Bu, Zhenping Yi, Bing Du, Jingchang Pan

arXiv:2211.07168 - Unsupervised Galaxy Morphological Visual Representation with Deep Contrastive Learning. Authors: Shoulin Wei, Yadi Li, Wei Lu, Nan Li, Bo Liang, Wei Dai, Zhijian Zhang

arXiv:2211.07758 - Classification of local ultraluminous infrared galaxies and quasars with kernel principal component analysis. Authors: Evangelos S. Papaefthymiou, Ioannis Michos, Orestis Pavlou, Vicky Papadopoulou Lesta and Andreas Efstathiou

arXiv:2211.07807 - Hierarchical Inference of the Lensing Convergence from Photometric Catalogs with Bayesian Graph Neural Networks. Authors: Ji Won Park, Simon Birrer, Madison Ueland, Miles Cranmer, Adriano Agnello, Sebastian Wagner-Carena, Philip J. Marshall, Aaron Roodman, and the LSST Dark Energy Science Collaboration

arXiv:2211.07890 - Autoencoding Galaxy Spectra I: Architecture. Authors: Peter Melchior, Yan Liang, ChangHoon Hahn, Andy Goulding

arXiv:2111.08030 - Fast and Credible Likelihood-Free Cosmology with Truncated Marginal Neural Ratio Estimation. Authors: Alex Cole, Benjamin Kurt Miller, Samuel J. Witte, Maxwell X. Cai, Meiert W. Grootes, Francesco Nattino, Christoph Weniger

arXiv:2211.08388 - Photometric identification of compact galaxies, stars and quasars using multiple neural networks. Authors: Siddharth Chaini, Atharva Bagul, Anish Deshpande, Rishi Gondkar, Kaushal Sharma, M. Vivek, Ajit Kembhavi

arXiv:2211.08971 - Energy Reconstruction in Analysis of Cherenkov Telescopes Images in TAIGA Experiment Using Deep Learning Methods. Authors: E. O. Gres, A. P. Kryukov

arXiv:2211.08990 - Recovering Galaxy Cluster Convergence from Lensed CMB with Generative Adversarial Networks. Authors: Liam Parker, Dongwon Han, Pablo Lemos Portela, Shirley Ho

arXiv:2211.09112 - Reconstruction of full sky CMB E and B modes spectra removing E-to-B leakage from partial sky using deep learning. Authors: Srikanta Pal and Rajib Saha

arXiv:2211.09126 - DIGS: Deep Inference of Galaxy Spectra with Neural Posterior Estimation. Authors: Gourav Khullar, Brian Nord, Aleksandra Ciprijanovic, Jason Poh and Fei Xu

arXiv:2211.09597 - Deep Learning-based galaxy image deconvolution. Authors: Utsav Akhaury, Jean-Luc Starck, Pascale Jablonka, Fr\'ed\'eric Courbin, Kevin Michalewicz

arXiv:2211.10305 - Neural Inference of Gaussian Processes for Time Series Data of Quasars. Authors: Egor Danilov, Aleksandra \'Ciprijanovi\'c and Brian Nord

arXiv:2211.10987 - Finding active galactic nuclei through Fink. Authors: Etienne Russeil, Emille E. O. Ishida, Roman Le Montagner, Julien Peloton, Anais Moller

arXiv:2211.11129 - H-FISTA: A hierarchical algorithm for phase retrieval with application to pulsar dynamic spectra. Authors: Stefan Oslowski, Mark A. Walker

arXiv:2211.11461 - Exhaustive Symbolic Regression. Authors: Deaglan J. Bartlett, Harry Desmond and Pedro G. Ferreira

arXiv:2211.11783 - Reconstructing and Classifying SDSS DR16 Galaxy Spectra with Machine-Learning and Dimensionality Reduction Algorithms. Authors: Felix Pat, Stéphanie Juneau, Vanessa B\"ohm, Ragadeepika Pucha, A. G. Kim, A. S. Bolton, Cleo Lepart, Dylan Green and Adam D. Myers

arXiv:2211.11784 - Quasar Factor Analysis -- An Unsupervised and Probabilistic Quasar Continuum Prediction Algorithm with Latent Factor Analysis. Authors: Zechang Sun, Yuan-Sen Ting and Zheng Cai

arXiv:2211.11792 - Unsupervised classification reveals new evolutionary pathways. Authors: M. Siudek, K. Lisiecki, M. Mezcua, K. Ma{\l}ek, A. Pollo, J. Krywult, A. Karska, Junais

arXiv:2211.12346 - Cosmology from Galaxy Redshift Surveys with PointNet. Authors: Sotiris Anagnostidis, Arne Thomsen, Tomasz Kacprzak, Tilman Tr\"oster, Luca Biggio, Alexandre Refregier, Thomas Hofmann

arXiv:2211.12444 - Can denoising diffusion probabilistic models generate realistic astrophysical fields? Authors: Nayantara Mudur, Douglas P. Finkbeiner

arXiv:2211.12553 - Using conditional variational autoencoders to generate images from atmospheric Cherenkov telescopes. Authors: Stanislav Polyakov, Alexander Kryukov, Andrey Demichev, Julia Dubenskaya, Elizaveta Gres, Anna Vlaskina

Pictures