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Found 11 papers, 5 papers with code
AGNet: Weighing Black Holes with Deep Learning
We find a 1$\sigma$ scatter of 0. 37 dex between the predicted SMBH mass and the fiducial virial mass estimate based on SDSS single-epoch spectra, which is comparable to the systematic uncertainty in the virial mass estimate.
AGNet: Weighing Black Holes with Machine Learning
Supermassive black holes (SMBHs) are ubiquitously found at the centers of most galaxies.
Survey2Survey: A deep learning generative model approach for cross-survey image mapping
We trained two types of neural networks to map images from the Sloan Digital Sky Survey (SDSS) to corresponding images from the Dark Energy Survey (DES).
Candidate Periodically Variable Quasars from the Dark Energy Survey and the Sloan Digital Sky Survey
We present a systematic search for periodic light curves in 625 spectroscopically confirmed quasars with a median redshift of 1. 8 in a 4. 6 deg$^2$ overlapping region of the Dark Energy Survey Supernova (DES-SN) fields and the Sloan Digital Sky Survey Stripe 82 (SDSS-S82).
High Energy Astrophysical Phenomena Astrophysics of Galaxies
GHOST: Using Only Host Galaxy Information to Accurately Associate and Distinguish Supernovae
We present GHOST, a database of 16, 175 spectroscopically classified supernovae and the properties of their host galaxies.
Dimensionality Reduction
Astrophysics of Galaxies
Cosmology and Nongalactic Astrophysics
Instrumentation and Methods for Astrophysics
Dark Energy Survey Year 1 Results: Cosmological Constraints from Cluster Abundances and Weak Lensing
We perform a joint analysis of the counts and weak lensing signal of redMaPPer clusters selected from the Dark Energy Survey (DES) Year 1 dataset.
Cosmology and Nongalactic Astrophysics
Probabilistic cosmic web classification using fast-generated training data
These measurements were used to train a random forest algorithm with, which was used to assign class probabilities to each particle in a $\Lambda$CDM, dark matter-only N-body simulation with $256^3$ particles, as well as on another toy model data set.
Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies Instrumentation and Methods for Astrophysics
Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era
We discuss key aspects to realize this endeavor, namely (i) the design and exploitation of scalable and computationally efficient AI algorithms for Multi-Messenger Astrophysics; (ii) cyberinfrastructure requirements to numerically simulate astrophysical sources, and to process and interpret Multi-Messenger Astrophysics data; (iii) management of gravitational wave detections and triggers to enable electromagnetic and astro-particle follow-ups; (iv) a vision to harness future developments of machine and deep learning and cyberinfrastructure resources to cope with the scale of discovery in the Big Data Era; (v) and the need to build a community that brings domain experts together with data scientists on equal footing to maximize and accelerate discovery in the nascent field of Multi-Messenger Astrophysics.
The Buzzard Flock: Dark Energy Survey Synthetic Sky Catalogs
We show that the weak-lensing shear catalog, redMaGiC galaxy catalogs and redMaPPer cluster catalogs provide plausible realizations of the same catalogs in the DES Y1 data by comparing their magnitude, color and redshift distributions, angular clustering, and mass-observable relations, making them useful for testing analyses that use these samples.
Cosmology and Nongalactic Astrophysics
Extended Isolation Forest
This extension, named Extended Isolation Forest (EIF), resolves issues with assignment of anomaly score to given data points.
A Hybrid Ensemble Learning Approach to Star-Galaxy Classification
There exist a variety of star-galaxy classification techniques, each with their own strengths and weaknesses.
Instrumentation and Methods for Astrophysics
