Search Results for author: Kevin Schawinski

Found 12 papers, 3 papers with code

Sparks of Science: Hypothesis Generation Using Structured Paper Data

no code implementations17 Apr 2025 Charles O'Neill, Tirthankar Ghosal, Roberta Răileanu, Mike Walmsley, Thang Bui, Kevin Schawinski, Ioana Ciucă

We demonstrate that framing hypothesis generation as conditional language modelling, with the model fine-tuned on Bit-Flip-Spark and the Chain-of-Reasoning (and where, at inference, we only provide the Bit), leads to improvements in the overall quality of the hypotheses.

Language Modelling Text Generation

Automatic Machine Learning Framework to Study Morphological Parameters of AGN Host Galaxies within $z < 1.4$ in the Hyper Supreme-Cam Wide Survey

1 code implementation27 Jan 2025 Chuan Tian, C. Megan Urry, Aritra Ghosh, Daisuke Nagai, Tonima T. Ananna, Meredith C. Powell, Connor Auge, Aayush Mishra, David B. Sanders, Nico Cappelluti, Kevin Schawinski

We present a composite machine learning framework to estimate posterior probability distributions of bulge-to-total light ratio, half-light radius, and flux for Active Galactic Nucleus (AGN) host galaxies within $z<1. 4$ and $m<23$ in the Hyper Supreme-Cam Wide survey.

Transfer Learning

Galaxy Morphology Network: A Convolutional Neural Network Used to Study Morphology and Quenching in $\sim 100,000$ SDSS and $\sim 20,000$ CANDELS Galaxies

1 code implementation25 Jun 2020 Aritra Ghosh, C. Megan Urry, Zhengdong Wang, Kevin Schawinski, Dennis Turp, Meredith C. Powell

This inferred difference in quenching mechanism is in agreement with previous studies that used other morphology classification techniques on much smaller samples at $z\sim0$ and $z\sim1$.

Astrophysics of Galaxies Instrumentation and Methods for Astrophysics

Continuous Integration of Machine Learning Models with ease.ml/ci: Towards a Rigorous Yet Practical Treatment

no code implementations1 Mar 2019 Cedric Renggli, Bojan Karlaš, Bolin Ding, Feng Liu, Kevin Schawinski, Wentao Wu, Ce Zhang

Continuous integration is an indispensable step of modern software engineering practices to systematically manage the life cycles of system development.

2k BIG-bench Machine Learning

Exploring galaxy evolution with generative models

no code implementations3 Dec 2018 Kevin Schawinski, M. Dennis Turp, Ce Zhang

Methods: By learning a latent space representation of the data, we can use this network to forward model and explore hypotheses in a data-driven way.

Using transfer learning to detect galaxy mergers

no code implementations25 May 2018 Sandro Ackermann, Kevin Schawinski, Ce Zhang, Anna K. Weigel, M. Dennis Turp

We investigate the use of deep convolutional neural networks (deep CNNs) for automatic visual detection of galaxy mergers.

General Classification Transfer Learning

PSFGAN: a generative adversarial network system for separating quasar point sources and host galaxy light

no code implementations23 Mar 2018 Dominic Stark, Barthelemy Launet, Kevin Schawinski, Ce Zhang, Michael Koss, M. Dennis Turp, Lia F. Sartori, Hantian Zhang, Yiru Chen, Anna K. Weigel

We test the method using Sloan Digital Sky Survey (SDSS) r-band images with artificial AGN point sources added which are then removed using the GAN and with parametric methods using GALFIT.

Astrophysics of Galaxies Data Analysis, Statistics and Probability

Generative Adversarial Networks recover features in astrophysical images of galaxies beyond the deconvolution limit

no code implementations1 Feb 2017 Kevin Schawinski, Ce Zhang, Hantian Zhang, Lucas Fowler, Gokula Krishnan Santhanam

Observations of astrophysical objects such as galaxies are limited by various sources of random and systematic noise from the sky background, the optical system of the telescope and the detector used to record the data.

Generative Adversarial Network

Cannot find the paper you are looking for? You can Submit a new open access paper.