In scientific research, it is natural to rely on graphs (time series, correlations, matrices, etc.) to convey results. To some extent, it is normal for the narrative of research to have focused particularly on the visual modality, taking advantage of the cognitive and explanatory potential of images. As the saying goes, "a picture is worth a thousand words."

Graphs or data visualizations help build narratives and identify patterns, aiding in the understanding of dynamics, seasonality, trends, mutual information, entropy, among others. However, there are at least a couple of considerations that data visualization brings. The first is that communication is not accessible to visually impaired individuals. The second is that visualization alone may not be sufficient to detect patterns using only the senses.

Considering this, data sonification can serve not only as an inclusive method in scientific outreach but also as an additional mechanism for pattern recognition or identifying regularities in records. It goes without saying that data sonification is already present in scientific research, such as in the study of salmon migration patterns (Hegg et al., 2018) or the fluctuations of brain waves (Parvizi et al., 2018).

In astrophysics, for instance, Edward Morgan from the Massachusetts Institute of Technology (MIT) took X-ray emission data from the black hole GRS 1915 + 105 and translated them into audio signals, allowing us to "listen" to its accretion disk (Masseti, M. 2013).

Following this approach, in the present work, we have taken data recorded (or, if necessary, reduced) by the observatories The 40 M Telescope at the Owens Valley Radio Observatory (OVRO) in radio emission, The American Association of Variable Star Observers (AAVSO) in optical, The Neil Gehrels Swift Observatory in X-rays, and The Fermi Gamma-ray Space Telescope in gamma rays. In addition to visualizing the data, we have sonified them to disseminate our findings to an even broader segment of society while also investigating auditory events that may help us distinguish periodicities.

We are developing statistical and computational methods for the inference of binary black holes. In addition to data visualization, we propose sonification as a method for disseminating our results. The exercise presented here focuses on the multifrequency analysis of the blazar Mrk 501.

The sonification was achieved at a tempo of 80 bpm, C3 octave, using a d (minor) relative minor scale. The programming was carried out using the package MIDItime 1.1.3 with the Python3 programming language.

Finally, we believe that scientific communication should reach more sectors of society. Therefore, in addition to presenting our findings visually and auditorily, we are also interested in exploring tactile communication in the future.

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