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Edmond – the Open Research Data Repository of the Max Planck Society

Edmond is a research data repository for Max Planck researchers. It is the place to store completed datasets of research data with open access. Edmond serves the publication of research data from all disciplines and offers scientists the ability to create citable research objects.

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1 to 10 of 205,154 Results

Realtime identification of Dispersed Radio signals using ML - A Case Study on the Crab Pulsar May 5, 2025 Kazantsev, Andrei; Karuppusamy, Ramesh, 2025, "Realtime identification of Dispersed Radio signals using ML - A Case Study on the Crab Pulsar", https://doi.org/10.17617/3.HQYC8O, Edmond, V1 Robust realtime identification of dispersed radio astronomical signals that last much less than a second is challenging. Here we explore the utility of machine learning techniques to identify such signals and use data taken on the Crab pulsar using the Effelsberg 100m Radio Teles...
AAA_README.txt May 5, 2025 - Realtime identification of Dispersed Radio signals using ML - A Case Study on the Crab Pulsar Plain Text - 2.0 KB - MD5: c9e5a026a375996def26e79cf1cd026c
B0531+21_59000_48386.fil May 5, 2025 - Realtime identification of Dispersed Radio signals using ML - A Case Study on the Crab Pulsar Unknown - 3.1 GB - MD5: 3cf053e6bc46911eada8d19bcb591bf5 Datafilterbank This file contains radio observations of the Crab pulsar (PSR B0531+21) in SIGPROC's filterbank format, recorded with the Effelsberg radio telescope. The data represents rapidly sampled radio spectra spanning the 1210-1530 MHz frequency range. The file contains a minimal metadata...
B0531+21_59000_48386_DM_time_dataset_realbased_labels_training.npy May 5, 2025 - Realtime identification of Dispersed Radio signals using ML - A Case Study on the Crab Pulsar Unknown - 7.3 MB - MD5: 558f7f3005fedc747fc43db226b95ab6 Data Numpy array containing labels for the DM-time training dataset. Each label identifies a sample as either a true pulse or an artefact (e.g., broadband RFI or just noise).
B0531+21_59000_48386_DM_time_dataset_realbased_training.npy May 5, 2025 - Realtime identification of Dispersed Radio signals using ML - A Case Study on the Crab Pulsar Unknown - 14.5 GB - MD5: 20075c6fe8cce8fb86e5535d1ac6084a Datadm_time Full-resolution DM-time training dataset (256×256) derived from Crab Pulsar observations.
B0531+21_59000_48386_DM_time_dataset_realbased_training_128x128.npy May 5, 2025 - Realtime identification of Dispersed Radio signals using ML - A Case Study on the Crab Pulsar Unknown - 3.6 GB - MD5: 32bad569b51a032250f7882aa0a75d13 Datadm_time DM-time training dataset downsampled to 128×128 resolution.
B0531+21_59000_48386_DM_time_dataset_realbased_training_32x32.npy May 5, 2025 - Realtime identification of Dispersed Radio signals using ML - A Case Study on the Crab Pulsar Unknown - 232.4 MB - MD5: a34392cf601f0768506dee1b21eeb6f8 Datadm_time DM-time training dataset downsampled to 32×32 resolution.
B0531+21_59000_48386_DM_time_dataset_realbased_training_64x64.npy May 5, 2025 - Realtime identification of Dispersed Radio signals using ML - A Case Study on the Crab Pulsar Unknown - 929.6 MB - MD5: 051da3b28302da65eed0f4a1a8b97b58 Datadm_time DM-time training dataset downsampled to 64×64 resolution.
pulsarx_dada_20240723.sif May 5, 2025 - Realtime identification of Dispersed Radio signals using ML - A Case Study on the Crab Pulsar Unknown - 494.0 MB - MD5: a6153f73969376cc87c92ed47558e3ee singularity_image Singularity image includes TransientX configured to work with a ringbuffer.
tensorflow_psrdada.sif May 5, 2025 - Realtime identification of Dispersed Radio signals using ML - A Case Study on the Crab Pulsar Unknown - 2.7 GB - MD5: c31dfc0ca3582d31bc09c060ec32f704 singularity_image The Singularity image includes the necessary Python libraries for the ML pipeline (TensorFlow/Keras, Matplotlib, NumPy, scikit-learn), as well as the psrdada library for working with a ringbuffer.

Realtime identification of Dispersed Radio signals using ML - A Case Study on the Crab Pulsar

May 5, 2025

Kazantsev, Andrei; Karuppusamy, Ramesh, 2025, "Realtime identification of Dispersed Radio signals using ML - A Case Study on the Crab Pulsar", https://doi.org/10.17617/3.HQYC8O, Edmond, V1

Robust realtime identification of dispersed radio astronomical signals that last much less than a second is challenging. Here we explore the utility of machine learning techniques to identify such signals and use data taken on the Crab pulsar using the Effelsberg 100m Radio Teles...

AAA_README.txt

May 5, 2025 - Realtime identification of Dispersed Radio signals using ML - A Case Study on the Crab Pulsar

Plain Text - 2.0 KB -

B0531+21_59000_48386.fil

May 5, 2025 - Realtime identification of Dispersed Radio signals using ML - A Case Study on the Crab Pulsar

Unknown - 3.1 GB -

Datafilterbank

This file contains radio observations of the Crab pulsar (PSR B0531+21) in SIGPROC's filterbank format, recorded with the Effelsberg radio telescope. The data represents rapidly sampled radio spectra spanning the 1210-1530 MHz frequency range. The file contains a minimal metadata...

B0531+21_59000_48386_DM_time_dataset_realbased_labels_training.npy

May 5, 2025 - Realtime identification of Dispersed Radio signals using ML - A Case Study on the Crab Pulsar

Unknown - 7.3 MB -

Data

Numpy array containing labels for the DM-time training dataset. Each label identifies a sample as either a true pulse or an artefact (e.g., broadband RFI or just noise).

B0531+21_59000_48386_DM_time_dataset_realbased_training.npy

May 5, 2025 - Realtime identification of Dispersed Radio signals using ML - A Case Study on the Crab Pulsar

Unknown - 14.5 GB -

Datadm_time

Full-resolution DM-time training dataset (256×256) derived from Crab Pulsar observations.

B0531+21_59000_48386_DM_time_dataset_realbased_training_128x128.npy

May 5, 2025 - Realtime identification of Dispersed Radio signals using ML - A Case Study on the Crab Pulsar

Unknown - 3.6 GB -

Datadm_time

DM-time training dataset downsampled to 128×128 resolution.

B0531+21_59000_48386_DM_time_dataset_realbased_training_32x32.npy

May 5, 2025 - Realtime identification of Dispersed Radio signals using ML - A Case Study on the Crab Pulsar

Unknown - 232.4 MB -

Datadm_time

DM-time training dataset downsampled to 32×32 resolution.

B0531+21_59000_48386_DM_time_dataset_realbased_training_64x64.npy

May 5, 2025 - Realtime identification of Dispersed Radio signals using ML - A Case Study on the Crab Pulsar

Unknown - 929.6 MB -

Datadm_time

DM-time training dataset downsampled to 64×64 resolution.

pulsarx_dada_20240723.sif

May 5, 2025 - Realtime identification of Dispersed Radio signals using ML - A Case Study on the Crab Pulsar

Unknown - 494.0 MB -

singularity_image

Singularity image includes TransientX configured to work with a ringbuffer.

tensorflow_psrdada.sif

May 5, 2025 - Realtime identification of Dispersed Radio signals using ML - A Case Study on the Crab Pulsar

Unknown - 2.7 GB -

singularity_image

The Singularity image includes the necessary Python libraries for the ML pipeline (TensorFlow/Keras, Matplotlib, NumPy, scikit-learn), as well as the psrdada library for working with a ringbuffer.

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