51 citations · 141 across the 13 of their papers we have counts for
13 papers
Toward Complete Merger Identification at Cosmic Noon with Deep Learning
Aimee Schechter, Aleksandra Ciprijanovic, Rebecca Nevin +4
As we enter the era of large imaging surveys such as , Rubin, and , a deeper understanding of potential biases and selection effects in optical ast…
The Importance of Being Adaptable: An Exploration of the Power and Limitations of Domain Adaptation for Simulation-Based Inference with Galaxy Clusters
Michelle Ntampaka, A. Ciprijanovic, Ana Maria Delgado +4
The application of deep machine learning methods in astronomy has exploded in the last decade, with new models showing remarkably improved performance on benchmark tasks. Not nearl…
The LSST AGN Data Challenge: Selection methods
Đorđe V. Savić, Isidora Jankov, Weixiang Yu +12
Development of the Rubin Observatory Legacy Survey of Space and Time (LSST) includes a series of Data Challenges (DC) arranged by various LSST Scientific Collaborations (SC) that a…
Neural Inference of Gaussian Processes for Time Series Data of Quasars
Egor Danilov, Aleksandra Ćiprijanović, Brian Nord
The study of quasar light curves poses two problems: inference of the power spectrum and interpolation of an irregularly sampled time series. A baseline approach to these tasks is…
DIGS: Deep Inference of Galaxy Spectra with Neural Posterior Estimation
Gourav Khullar, Brian Nord, Aleksandra Ciprijanovic +2
With the advent of billion-galaxy surveys with complex data, the need of the hour is to efficiently model galaxy spectral energy distributions (SEDs) with robust uncertainty quanti…
Estimating Cosmological Constraints from Galaxy Cluster Abundance using Simulation-Based Inference
Moonzarin Reza, Yuanyuan Zhang, Brian Nord +3
Inferring the values and uncertainties of cosmological parameters in a cosmology model is of paramount importance for modern cosmic observations. In this paper, we use the simulati…