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Found 13 papers, 7 papers with code
Constructing Impactful Machine Learning Research for Astronomy: Best Practices for Researchers and Reviewers
With this paper, we aim to provide a primer to the astronomical community, including authors, reviewers, and editors, on how to implement machine learning models and report their results in a way that ensures the accuracy of the results, reproducibility of the findings, and usefulness of the method.
DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection
This algorithm performs semi-supervised domain adaptation and can be applied to datasets with different data distributions and class overlaps.
Robustness of deep learning algorithms in astronomy -- galaxy morphology studies
Deep learning models are being increasingly adopted in wide array of scientific domains, especially to handle high-dimensionality and volume of the scientific data.
DeepMerge II: Building Robust Deep Learning Algorithms for Merging Galaxy Identification Across Domains
Here we employ domain adaptation techniques$-$ Maximum Mean Discrepancy (MMD) as an additional transfer loss and Domain Adversarial Neural Networks (DANNs)$-$ and demonstrate their viability to extract domain-invariant features within the astronomical context of classifying merging and non-merging galaxies.
Domain adaptation techniques for improved cross-domain study of galaxy mergers
In astronomy, neural networks are often trained on simulated data with the prospect of being applied to real observations.
DeepMerge: Classifying High-redshift Merging Galaxies with Deep Neural Networks
The test set classification accuracy of the CNN is $79\%$ for pristine and $76\%$ for noisy.
Response to NITRD, NCO, NSF Request for Information on "Update to the 2016 National Artificial Intelligence Research and Development Strategic Plan"
Thus far the US has been a leader in AI technologies, and we believe as a national Laboratory it is crucial to help maintain and extend this leadership.
Petabytes to Science
A Kavli foundation sponsored workshop on the theme \emph{Petabytes to Science} was held 12$^{th}$ to 14$^{th}$ of February 2019 in Las Vegas.
Instrumentation and Methods for Astrophysics
Dark Energy Survey Year 1 Results: Constraints on Extended Cosmological Models from Galaxy Clustering and Weak Lensing
We consider four extensions of the minimal dark energy-dominated scenarios: 1) nonzero curvature $\Omega_k$, 2) number of relativistic species $N_{\rm eff}$ different from the standard value of 3. 046, 3) time-varying equation-of-state of dark energy described by the parameters $w_0$ and $w_a$ (alternatively quoted by the values at the pivot redshift, $w_p$, and $w_a$), and 4) modified gravity described by the parameters $\mu_0$ and $\Sigma_0$ that modify the metric potentials.
Cosmology and Nongalactic Astrophysics General Relativity and Quantum Cosmology High Energy Physics - Phenomenology
Knowledge transfer of Deep Learning for galaxy morphology from one survey to another
A key question for using DL classifications in future Big Data surveys is how much of the knowledge acquired from an existing survey can be exported to a new dataset, i. e. if the features learned by the machines are meaningful for different data.
Astrophysics of Galaxies
Dark Energy Survey Year 1 Results: Weak Lensing Mass Calibration of redMaPPer Galaxy Clusters
Our analysis accounts for the following sources of systematic error: shear and photometric redshift errors, cluster miscentering, cluster member dilution of the source sample, systematic uncertainties in the modeling of the halo--mass correlation function, halo triaxiality, and projection effects.
Cosmology and Nongalactic Astrophysics
Dark Energy Survey Year 1 Results: Cosmological Constraints from Galaxy Clustering and Weak Lensing
We present cosmological results from a combined analysis of galaxy clustering and weak gravitational lensing, using 1321 deg$^2$ of $griz$ imaging data from the first year of the Dark Energy Survey (DES Y1).
Cosmology and Nongalactic Astrophysics
Automated Transient Identification in the Dark Energy Survey
We describe an algorithm for identifying point-source transients and moving objects on reference-subtracted optical images containing artifacts of processing and instrumentation.
Instrumentation and Methods for Astrophysics
