| Name | Abs no. | Title |
|---|
| Bekdaulet Shukirgaliyev | 1557 | Measuring the masses of
core-collapse supernova progenitors with gravitational waves |
| Boon Kiat Oh | 824 | Machine-assisted Exploration of
Simulation Parameters |
| Cristiano Sabiu | 1118 | A Deep Learning approach to Large
Scale Structure Cosmology |
| Da Eun Kang | 2486 | Emission-line diagnostics of HII
regions using conditional Invertible Neural Networks |
| Djordje Savic | 384 | LSST AGN Data challenge –
summary |
| Erik Rodrigues de Lima | 890 | Photometric redshifts for
the S-PLUS survey: is machine learning up to the task? |
| Georgios Vernardos | 2143 | Dissecting quasars with
microlensing in the era of LSST |
| Ignacio Ferreras | 720 | The information content of galaxy
spectra |
| Jakob Knollmüller | 755 | Large-scale multi-domain imaging
in complex systems |
| John Yue Han Soo | 3108 | Machine Learning in Photometric
Redshifts: The Performance of ANNz After 18 Years |
| John Suárez-Pérez | 2299 | Assessing the quality of
massive spectroscopic surveys with unsupervised machine learning |
| Juan Rafael Martinez Galarza | 1752 | Machine learning as
a tool for discovery in X-ray datasets: time-domain and beyond |
| Keiya Hirashima | 3322 | Forecasting SN explosions Using
Deep Learning toward High-Resolution Galaxy Simulations |
| Lilianne Nakazono | 1709 | Interpreting machine learning
models: a case of star-quasar-galaxy classification in large-sky
surveys |
| Marc Huertas-Company | 2859 | Deep learning
likelihood-free inference of Star Formation and Accretion Histories of
galaxies |
| Nadejda Blagorodnova Mujortova | 2728 | Machine Learning
projects within the BlackGEM collaboration |
| Priya Shah | 528 | Machine Learning in the study of Star
Clusters with Gaia EDR3 |
| Ryan Keeley | 2378 | On the Distribution of Bayesian
Evidences |
| Sepideh Ghaziasgar | 2767 | Spectral Identification and
Classification of Dusty Stellar Sources Using Spectroscopic and Multiwavelength
Observations Through Machine Learning |
| Shay Zucker | 1332 | New types of periodograms based on
phase distance correlation |
| Stefan Wagner | 1819 | ML in trigger schemes for
time-domain studies |
| Suhyun Shin | 558 | The faint end of the quasar luminosity
function at z~5 based on deep learning and Bayesian statistics |
| Suk Yee Yong | 1429 | Making Unexpected Time-domain
Discoveries in Astronomy with Machine Learning |
| Sungwook E Hong | 560 | Revealing the Local Cosmic Web
from Galaxies by Deep Learning |
