13 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Northwestern UniversityUS10 papers
- Massachusetts Institute of TechnologyUS5 papers
- University of ChicagoUS5 papers
- California Institute of TechnologyUS4 papers
- Institute of AstronomyRU4 papers
- Canadian Institute for Theoretical AstrophysicsCA3 papers
- Monash UniversityAU3 papers
- Simons FoundationUS3 papers
- The NSF AI Institute for Artificial Intelligence and Fundamental InteractionsUS3 papers
- University of CambridgeGB3 papers
- University of TorontoCA3 papers
- AlbaNovaSE2 papers
11 papers · 1 filter
Foreground Mitigation for CMB Lensing with the Global Minimum Variance Quadratic Estimator
Yuka Nakato, W. L. Kimmy Wu, Ana Carolina Silva Oliveira +2
Weak gravitational lensing of the cosmic microwave background (CMB) is a powerful probe of cosmology, providing insight into structure formation and the evolution of the universe.…
Modelling the Future of Gaia Neutron Star-Main Sequence Binaries: From Eccentric Orbits to Millisecond Pulsar-White Dwarfs
Debatri Chattopadhyay, Kyle A. Rocha, Seth Gossage +3
We model the evolution of 21 Gaia neutron star (NS)-main-sequence binaries (orbital period -- days, eccentricity ) using binary evolutio…
Hierarchical Triples vs. Globular Clusters: Binary black hole merger eccentricity distributions compete and evolve with redshift
Andris Dorozsmai, Isobel M. Romero-Shaw, Aditya Vijaykumar +5
The formation mechanisms of merging binary black holes (BBHs) observed by the LIGO-Virgo-KAGRA collaboration remain uncertain. Detectable eccentricity provides a powerful diagnosti…
Characterization of the Cherenkov Photon Background for Low-Noise Silicon Detectors in Space
Manuel E. Gaido, Javier Tiffenberg, Alex Drlica-Wagner +7
Future space observatories that seek to perform imaging and spectroscopy of faint astronomical sources will require ultra-low-noise detectors that are sensitive over a broad wavele…
A Morphological Model to Separate Resolved-Unresolved Sources in the DESI Legacy Surveys: Application in the LS4 Alert Stream
Chang Liu, Adam A. Miller, Joshua S. Bloom +2
Separating resolved and unresolved sources in large imaging surveys is a fundamental step to enable downstream science, such as searching for extragalactic transients in wide-field…
Inferring CSM Properties of Type II SNe Using a Magnitude-Limited ZTF Sample
K-Ryan Hinds, Daniel Perley, Jesper Sollerman +20
Although all Type II supernovae (SNe) originate from massive stars possessing a hydrogen-rich envelope, their light curve morphology is diverse, reflecting poorly characterised het…
Trial by FIRE: Probing the dark matter density profile of dwarf galaxies with GraphNPE
Tri Nguyen, Justin Read, Lina Necib +4
The Dark Matter (DM) distribution in dwarf galaxies provides crucial insights into both structure formation and the particle nature of DM. GraphNPE (Graph Neural Posterior Estimato…
Transfer Learning for Transient Classification: From Simulations to Real Data and ZTF to LSST
Rithwik Gupta, Daniel Muthukrishna, Nabeel Rehemtulla +1
Machine learning has become essential for automated classification of astronomical transients, but current approaches face significant limitations: classifiers trained on simulatio…
SIDDA: SInkhorn Dynamic Domain Adaptation for Image Classification with Equivariant Neural Networks
Sneh Pandya, Purvik Patel, Brian D. Nord +2
Modern neural networks (NNs) often do not generalize well in the presence of a "covariate shift"; that is, in situations where the training and test data distributions differ, but…
Structure and Skewness of the Effective Inspiral Spin Distribution of Binary Black Hole Mergers
Sharan Banagiri, Thomas A. Callister, Christian Adamcewicz +2
The detection of gravitational waves has brought to light a population of binary black holes that merge within a Hubble time. Multiple formation channels can contribute to this pop…
ORACLE: A Real-Time, Hierarchical, Deep-Learning Photometric Classifier for the LSST
Ved G. Shah, Alex Gagliano, Konstantin Malanchev +3
We present ORACLE, the first hierarchical deep-learning model for real-time, context-aware classification of transient and variable astrophysical phenomena. ORACLE is a recurrent n…