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       This portal provides a new entry for the repositories and documentation of this project.

Welcome to ML-PPA
=================

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..  commentout   about


The Machine Learning-based Pipeline for Pulsar Analysis (ML-PPA) software framework 
for extracting pulsar signals in data streams from radio astronomical antennas.

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..  commentout-------
..  commentoutA :mlppa:`ML-PPA` example.
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..  commentout            About :mlppa:`ML-PPA`

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..  commentout                `see about <about.html>`_




Pipelines and Notebooks
-----------------------

The software framework provides Python and C++ written pipelines.
Most of the pipeline can easily be analyzed using jupyter notebooks.
The following sub-projects are currently available.

All projects provide a link to a repository and some of them also a link to its documentation.

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            PulsarSA

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            Tools to segment and analyse pulsar signals from noisy frequency-time data.

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                `GitLab <https://gitlab.dzastro.de/punch/ml-ppa/pulsarsa>`__
                
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                `Doku <https://doc.pulsarsa.ml-ppa.dzastro.de>`__


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            PulsarDT

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            Simulation of pulsars with customisable emissions to generate training data for AI detection.

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                `GitLab <https://gitlab.dzastro.de/punch/ml-ppa/pulsardt>`__

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                 `Doku <https://doc.pulsardt.ml-ppa.dzastro.de>`__


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            PulsarDT++

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            A C++ version of PulsarDT.

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                `GitLab <https://gitlab.dzastro.de/punch/ml-ppa/pulsardtpp>`__

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                `Doku <https://doc.pulsardtpp.ml-ppa.dzastro.de>`__


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            Telescope Noise DT

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            Simulation of radio telescope noise based on modular libraries.

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                `GitLab <https://gitlab.dzastro.de/punch/ml-ppa/telescope-noise_dt>`__

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        ..  commentout        `Doku <https://doc.telescope-noise-dt.ml-ppa.int.dzastro.de>`__

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            PulsarRFI_NN

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             A wrapper around PulsarRFI_Gen, creating a simulated data pipeline for training and testing.

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                `GitLab <https://gitlab.dzastro.de/punch/ml-ppa/pulsarrfi_nn>`__

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        ..  commentout        `Doku <https://doc.pulsarrfinn.ml-ppa.int.dzastro.de>`__

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            PulsarRFI_Gen

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            A tool for generating synthetic radio pulses as well as primitive broadband and narrowband RFIs.

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                `GitLab <https://gitlab.dzastro.de/punch/ml-ppa/pulsarrfi_gen>`__

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        ..  commentout        `Doku <https://doc.pulsarrfigen.ml-ppa.int.dzastro.de>`__

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            DADA Noise Analysis

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            A Jupyter notebook for reading and analysing data from the DADA radio telescope.

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                `GitLab <https://gitlab.dzastro.de/punch/ml-ppa/dada_noise_analysis>`__

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        ..  commentout        `Doku <https://doc.dada-noise-analysis.ml-ppa.int.dzastro.de>`__

Publications
------------

Discover research papers, reports, posters, presentations and publications related to this project.

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            Dynamization of big data workflows

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            A presentation at the ADASS 2025 conference in Görlitz, Germany (Heßling et al., 2025).

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                `Publication <https://indico.dzastro.de/event/4/contributions/160/>`__

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            Multi-messaging: How can uniform access to astronomical archives be achieved?

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            A Bird of a Feather Session at the ADASS 2025 conference in Görlitz, Germany (Heßling et al., 2025).

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                `Publication <https://indico.dzastro.de/event/4/contributions/148/>`__

    
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            Software framework for Pulsar Detection using Machine Learning and Digital Twin

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            A poster at the ADASS 2025 conference in Görlitz, Germany (Saha et al., 2025).

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                `Publication <https://indico.dzastro.de/event/4/contributions/365/>`__
    
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            DZA HPC-Cloud

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            A poster at the ADASS 2025 conference in Görlitz, Germany (Haupt et al., 2025).

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                `Publication <https://indico.dzastro.de/event/4/contributions/200/>`__

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            Memory-based Computing: First Results

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            A poster at the ADASS 2025 conference in Görlitz, Germany (Buchholz et al., 2025).

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                `Publication <https://indico.dzastro.de/event/4/contributions/189/>`__

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             ML-PPA

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            A document explaining the ML-PPA software framework (Kazantsev et al., 2025).

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                `see PDF <PUNCH_interTwin_project.pdf>`_