astronomy-astrophysics
There are 7 repositories under astronomy-astrophysics topic.
- mocpyLanguage:Python 53
onekiloparsec / aa-js
The most comprehensive collection of accurate astronomical algorithms in JavaScript (TypeScript).
Language:TypeScript 38lortordermur / sfcalcsheet
The science fiction calculation spreadsheet
HSE-LAMBDA / fulu
Fulu is a python library of supernova light curves approximation methods based on machine learning.
Language:Jupyter Notebook 23punch-mission / regularizepsf
A Python package for manipulating and correcting variable point spread functions. Maintainer: @jmbhughes
Language:Python 18spacetelescope / jwst_mast_query
Command line tool for querying MAST and downloading JWST data products
Language:Python 13NuSpaceSim / nuSpaceSim
The main simulator repository for nuSpaceSim
Language:Python 10- wakeflowLanguage:Python 10
heidiawhite / Astrodigenous
A database structure for resources hosted on the Astrodigenous website
Language:HTML 9miguelcarcamov / snow
SNOW: caSa pythoN self-calibratiOn frameWork
Language:Python 7privong / papers-in-markdown
Templates and pandoc invocations for preparing astronomy papers in Markdown
Language:TeX 7jdnichollsc / solar-systems
Create Solar Systems easily in pure WAAPI! 🌌
Language:JavaScript 6takafumi291 / ESSENCE
A python package for evaluating statistical significance of image analysis and signal detection under correlated noise in interferometric images (e.g., ALMA, NOEMA: Tsukui et al. 2023).
Language:Jupyter Notebook 6anandprabhakar0507 / Assignments-Data-Driven-Astronomy-from-University-of-sydney-on-coursera-
This repositorie contain all the assignment that i am completing during the lessons from course Data driven Astronomy that i am learning form university of sydney on coursera see here https://www.coursera.org/learn/data-driven-astronomy
Language:Jupyter Notebook 5cyschneck / Star-Chart-Spherical-Projection
A Python package to generate astronomy star charts that corrects for distortions with stereographic projection (v1.5.0)
Language:Python 5micbia / SegU-Net
Segmentation 2D Convolutional U-Network for Identification of HI regions during Reionization in 21-cm 3D Tomography Observations
Language:Jupyter Notebook 5benjaminpope / k2halo
The K2 Halo Campaign Data Release and Paper
Language:Jupyter Notebook 4brkent / datacube-display
Short tutorial on displaying an astronomical FITS datacube
Language:Python 4mgomezAstro / staralt
A program to obtain the visibility plot of an astrophysical object in a determined world-location.
Language:Python 4Compressive Sensing and Optimization Framework to reconstruct Faraday Depth signals
Language:Python 4StavrosAkras / SATELLITE
Spectroscopic Analysis Tool for intEgraL FieLd unIt daTacubEs
Language:Python 4abhisrkckl / mikkola
An implementation of Mikkola's method to solve Kepler's equation
Language:C++ 3adamparkosidis / modeling-protoplanetary-dust-disk
Modeling Protoplanetary dust disk in the presence of a Giant Planet
Language:TeX 3KnoxAstro / sonification-project
A repo for the Knox College Visualization of Sound and Sonification of Light's Sonification Project!
Language:JavaScript 3Kosmik123 / Sun-Movement
Realistic sun movement script for Unity game. Rotation of directional light depends on several factors: time, latitude, tilt of the Earths rotation axis & season.
Language:C# 3A solar polarization resolver. Maintainer: @s0larish
Language:Python 3srinadh99 / Cosmic-Ray-Detection-via-Dictionary-Learning
In this work, we propose a novel Dictionary Learning (DL) based framework to detect Cosmic Ray (CR) hits that contaminate the astronomical images obtained through optical photometric surveys. The unique and distinguishable spatial signatures of CR hits compared to other actual astrophysical sources present in the image motivated us to characterize the CR patches uniquely via their sparse representations obtained from a learned dictionary. Specifically, the dictionary is trained on images acquired from the Dark Energy Camera (DECam) observations. Next, the learned dictionary is used to represent the CR and Non-CR patches (e.g., each patch is with $11 \times 11$ pixel resolution) extracted from the original images. A Machine Learning (ML) classifier is then trained to classify the CR and Non-CR patches. Empirically, we demonstrate that the proposed DL based method can detect the CR hits at patch level and provide approximately $83\%$ detection rates on test data from multiple photometric bands of the DECam with Random Forest (RF) algorithm. Further, we used the coarse segmentation maps obtained from the classifier output to guide the deep-learning-based CR segmentation models. The coarse maps are fed through a separate channel along with the contaminated image to detect the CR induced pixels more accurately. We evaluated the performance of proposed DL guided deep segmentation models over the baseline on test data from DECam. We demonstrate that the proposed method provides additional guidance to the baseline models in terms of faster convergence rate and improves CR detection performance by $2\%$ in the case of shallow models.
autonomous019 / star_classifier_flask
ML Classifier using xgBoost and Flask, gives star type by adding new data to trained set
Language:Python 2- Language:Python 2
- hrhasson.github.io
hrhasson / hrhasson.github.io
My personal website for my science and other creative endeavors. Template courtesy of http://html5up.net/.
Language:CSS 2 javiserna / TESSExtractor-webapp
Web application to search, process, and visualizes the TESS data of any target cataloged in astronomical databases like SIMBAD.
Magritte-code / pomme
Bayesian model reconstruction based on astronomical spectral line observations.
Language:Python 2BrsDincer / Gravitational_Wave_Analysis
This research file includes Gravitational Wave Analysis approaches, signal processing methods and new experimental approaches.
Language:Jupyter Notebook 1gszasz / true-ecs
Calculates valid ECS coordinates (RA, DEC) from the image coordinates (X, Y) using corrected WCS parameters.
Language:Python 1- Language:Python 1
olozhika / ArXivDaily_StarFormation
Auto update Star Formation & Molecular Cloud papers at about 2:30am UTC (10:30am Beijing time) every weekday.
Language:Python 1
