Escape-OSSR
2024_domain_adaptation_methods_lst_results
Results from the study presented in the article entilted _Comparative study of unsupervised domain adaptation techniques applied to gamma-ray astronomy with the CTAO Large Sized Telescope-1_
- Astroparticle Physics
- CTA
- Deep learning
- + 1
2025-stereograph
This repository contains pre-trained models, computed results, and analysis code for evaluating machine learning approaches (Random Forests, FCNs, and GNNs) on gamma-ray event reconstruction tasks.
- Astroparticle Physics
- CTA
- Deep learning
- + 2
- ("Jupyter Notebook")
- (Shell)
agataselector
This selector is meant to analyze the data of AGATA+$Ancillary, producing various histograms and performing kinematic calculations and other operations useful for Doppler correction and other analysis tasks. The starting point of the selector are the ROOT files produced by femul. The code is a wo...
- Agata
- gamma-ray spectroscopy
- Nuclear Physics
- + 1
- (C)
- (C++)
- (CMake)
- + 2
agnpy
agnpy is a python package focusing on the computation of the radiative processes of relativistic particles accelerated in the jets of Active Galactic Nuclei (AGN). It includes classes describing the galaxy components responsible for line and thermal emission and calculates the absorption due to g...
- active galactic nuclei
- Astroparticle Physics
- blazars
- + 5
- (C)
- ("Jupyter Notebook")
- (Python)
Aladin Lite
An astronomical HiPS visualizer in the browser.
- Astronomy
- IVOA
- (CSS)
- (GLSL)
- (JavaScript)
- + 4
ATLAS Open Data 13 TeV analysis C++ framework
A repository with 12 high energy physics analysis examples using the ATLAS Open Data 13 TeV dataset released in 2020. It is written in C++ and some bash scripts. * Documentation of the code: http://opendata.atlas.cern/release/2020/documentation/frameworks/cpp.html * Documentation of the analysis:...
cds-escape-tutorials
Jupyter Notebook tutorials using astronomical databases and Virtual Observatory tools
- virtual-observatory databases python
- ("Jupyter Notebook")
- (Just)
CTLearn: Deep learning for imaging atmospheric Cherenkov telescopes event reconstruction
CTLearn is a high-level Python package providing a backend for training deep learning models for the reconstruction of imaging atmospheric Cherenkov telescope events using TensorFlow.
- Deep learning
- Event reconstruction
- High energy physics
- + 1
- (Dockerfile)
- ("Jupyter Notebook")
- (Python)
Dark matter constraints from dwarf galaxies: a data-driven LAT analysis
Python code to derive data-driven upper limits on the thermally averaged, velocity-weighted pair-annihilation cross-section (velocity-independent) of a user-defined particle dark matter model using the expected differential gamma-ray spectrum of pair-annihilation events (provided by the user) as ...
- ESCAPE
- jupyter-notebook
- ("Jupyter Notebook")
- (Python)
Dockerfile to extract Gravitational Wave data from the ESCAPE datalake
This is a container to extract Gravitational Wave (GW) data from the datalake using Rucio and feed 1 second GW frames to the GW pipelines.
- EGO-VIRGO
- (Dockerfile)
- (Python)
- (Shell)
eossr
<p><img alt="eossr_logo" src="docs/images/eossr_logo_200x100.png" /></p><h1>The ESCAPE OSSR library</h1><p>The eOSSR is the Python library to programmatically manage the ESCAPE OSSR.In particular, it includes:</p><ul><li>an API to access the Zenodo and the OSSR, retrieve records and publish conte...
- jupyter-notebook
- Zenodo
- (Dockerfile)
- (Python)
ESCAPE template project
An example of software project template for the ESCAPE 2020 European project
- ESCAPE
- jupyter-notebook
- (Dockerfile)
- ("Jupyter Notebook")
- (Python)
- + 1