15 citations · 34 across the 12 of their papers we have counts for
12 papers
Domain Adaptation for Measurements of Strong Gravitational Lenses
Paxson Swierc, Megan Zhao, Aleksandra Ćiprijanović +1
Upcoming surveys are predicted to discover galaxy-scale strong lenses on the order of , making deep learning methods necessary in lensing data analysis. Currently, there is i…
Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data Sets
Andrea Roncoli, Aleksandra Ćiprijanović, Maggie Voetberg +2
Deep learning models have been shown to outperform methods that rely on summary statistics, like the power spectrum, in extracting information from complex cosmological data sets.…
DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection
A. Ćiprijanović, A. Lewis, K. Pedro +4
Artificial intelligence methods show great promise in increasing the quality and speed of work with large astronomical datasets, but the high complexity of these methods leads to t…
Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection
Aleksandra Ćiprijanović, Ashia Lewis, Kevin Pedro +4
In the era of big astronomical surveys, our ability to leverage artificial intelligence algorithms simultaneously for multiple datasets will open new avenues for scientific discove…
Strong Lensing Parameter Estimation on Ground-Based Imaging Data Using Simulation-Based Inference
Jason Poh, Ashwin Samudre, Aleksandra Ćiprijanović +4
Current ground-based cosmological surveys, such as the Dark Energy Survey (DES), are predicted to discover thousands of galaxy-scale strong lenses, while future surveys, such as th…
Inferring Structural Parameters of Low-Surface-Brightness-Galaxies with Uncertainty Quantification using Bayesian Neural Networks
Dimitrios Tanoglidis, Aleksandra Ćiprijanović, Alex Drlica-Wagner
Measuring the structural parameters (size, total brightness, light concentration, etc.) of galaxies is a significant first step towards a quantitative description of different gala…
Machine Learning and Cosmology
Cora Dvorkin, Siddharth Mishra-Sharma, Brian Nord +8
Methods based on machine learning have recently made substantial inroads in many corners of cosmology. Through this process, new computational tools, new perspectives on data colle…
DeepAdversaries: Examining the Robustness of Deep Learning Models for Galaxy Morphology Classification
Aleksandra Ćiprijanović, Diana Kafkes, Gregory Snyder +6
With increased adoption of supervised deep learning methods for processing and analysis of cosmological survey data, the assessment of data perturbation effects (that can naturally…
DeepGhostBusters: Using Mask R-CNN to Detect and Mask Ghosting and Scattered-Light Artifacts from Optical Survey Images
Dimitrios Tanoglidis, Aleksandra Ćiprijanović, Alex Drlica-Wagner +7
Wide-field astronomical surveys are often affected by the presence of undesirable reflections (often known as "ghosting artifacts" or "ghosts") and scattered-light artifacts. The i…
Optical observations of the nearby galaxy NGC 2366 through narrowband H and SII filters. Supernova remnants status
M. M. Vučetić, D. Onić, N. Petrov +2
We present detection of 67 HII regions and two optical supernova remnant (SNR) candidates in the nearby irregular galaxy NGC 2366. The SNR candidates were detected by applying [SII…
Constraining the Collective Radio Emission of Large Scale Accretion Shocks
A. Ćiprijanović, T. Prodanović, M. Z. Pavlović
Accretion of gas onto already virialized structures like galaxy clusters should give rise to accretion shocks which can potentially accelerate cosmic rays. Here, we use the radio e…
Galactic Cosmic-Ray Induced Production of Lithium in the Small Magellanic Cloud
A. Ćiprijanović
Recently, the first lithium detection outside of the Milky Way was made in low-metallicity gas of the Small Magellanic Cloud, which was at the level of the expected primordial valu…