Articles | Volume 41, issue 1
https://doi.org/10.5194/angeo-41-69-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/angeo-41-69-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Machine learning detection of dust impact signals observed by the Solar Orbiter
Department of Physics and Technology, UiT The Arctic University of Norway, 9037 Tromsø, Norway
Kristoffer Wickstrøm
Department of Physics and Technology, UiT The Arctic University of Norway, 9037 Tromsø, Norway
Samuel Kociscak
Department of Physics and Technology, UiT The Arctic University of Norway, 9037 Tromsø, Norway
Jakub Vaverka
Department of Surface and Plasma Science, Charles University Prague, 18000 Prague, Czech Republic
Libor Nouzak
Department of Surface and Plasma Science, Charles University Prague, 18000 Prague, Czech Republic
Arnaud Zaslavsky
LESIA – Observatoire de Paris, Université PSL, CNRS, Sorbonne Université, Université de Paris,
5 place Jules Janssen, 92195 Meudon, France
Kristina Rackovic Babic
LESIA – Observatoire de Paris, Université PSL, CNRS, Sorbonne Université, Université de Paris,
5 place Jules Janssen, 92195 Meudon, France
Department of Astronomy, Faculty of Mathematics, University of
Belgrade, Studentski trg 16, 11000 Belgrade, Serbia
Amalie Gjelsvik
Department of Physics and Technology, UiT The Arctic University of Norway, 9037 Tromsø, Norway
David Pisa
Department of Space Physics, Institute of Atmospheric Physics, Czech Academy of Sciences, Bocni II/1401, 14100 Prague, Czech Republic
Jan Soucek
Department of Space Physics, Institute of Atmospheric Physics, Czech Academy of Sciences, Bocni II/1401, 14100 Prague, Czech Republic
Ingrid Mann
Department of Physics and Technology, UiT The Arctic University of Norway, 9037 Tromsø, Norway
Related authors
Samuel Kočiščák, Andreas Kvammen, Ingrid Mann, Nicole Meyer-Vernet, David Píša, Jan Souček, Audun Theodorsen, Jakub Vaverka, and Arnaud Zaslavsky
Ann. Geophys., 42, 191–212, https://doi.org/10.5194/angeo-42-191-2024, https://doi.org/10.5194/angeo-42-191-2024, 2024
Short summary
Short summary
In situ observations are crucial for understanding interplanetary dust, yet not every spacecraft has a dedicated dust detector. Dust encounters happen at great speeds, leading to high energy density at impact, which leads to ionization and charge release, which is detected with electrical antennas. Our work looks at how the transient charge plume interacts with Solar Orbiter spacecraft. Our findings are relevant for the design of future experiments and the understanding of present data.
Devin Huyghebaert, Björn Gustavsson, Juha Vierinen, Andreas Kvammen, Matthew Zettergren, John Swoboda, Ilkka Virtanen, Spencer Hatch, and Karl M. Laundal
EGUsphere, https://doi.org/10.5194/egusphere-2024-802, https://doi.org/10.5194/egusphere-2024-802, 2024
Short summary
Short summary
The EISCAT_3D radar is a new ionospheric radar under construction in the Fennoscandia region. The radar will make measurements of plasma characteristics at altitudes above approximately 60 km. The capability of the system to make these measurements on spatial scales of less than 100 m using the multiple digitised signals from each of the radar antenna panels is highlighted. There are many ionospheric small-scale processes that will be further resolved using the techniques discussed here.
Theresa Rexer, Björn Gustavsson, Juha Vierinen, Andres Spicher, Devin Ray Huyghebaert, Andreas Kvammen, Robert Gillies, and Asti Bhatt
Geosci. Instrum. Method. Data Syst. Discuss., https://doi.org/10.5194/gi-2023-18, https://doi.org/10.5194/gi-2023-18, 2024
Preprint under review for GI
Short summary
Short summary
We present a second-level calibration method for electron density measurements from multi-beam incoherent scatter radars. It is based on the well-known Flat field correction method used in imaging and photography. The methods improve data quality and useability as they account for unaccounted, and unpredictable variations in the radar system. This is valuable for studies where inter-beam calibration is important such as studies of polar cap patches, plasma irregularities and turbulence.
Derek McKay and Andreas Kvammen
Geosci. Instrum. Method. Data Syst., 9, 267–273, https://doi.org/10.5194/gi-9-267-2020, https://doi.org/10.5194/gi-9-267-2020, 2020
Short summary
Short summary
Researchers are making increasing use of machine learning to improve accuracy, efficiency and consistency. During such a study of the aurora, it was noted that biases or distortions had crept into the data because of the conditions (or ergonomics) of the human trainers. As using machine-learning techniques in auroral research is relatively new, it is critical that such biases are brought to the attention of the academic and citizen science communities.
Dorota Jozwicki, Puneet Sharma, Devin Huyghebaert, and Ingrid Mann
Ann. Geophys., 42, 431–453, https://doi.org/10.5194/angeo-42-431-2024, https://doi.org/10.5194/angeo-42-431-2024, 2024
Short summary
Short summary
We investigated the relationship between polar mesospheric summer echo (PMSE) layers and the solar cycle. Our results indicate that the average altitude of PMSEs, the echo power in the PMSEs and the thickness of the layers are, on average, higher during the solar maximum than during the solar minimum. We infer that higher electron densities at ionospheric altitudes might be necessary to observe multilayered PMSEs. We observe that the thickness decreases as the number of multilayers increases.
Adrien Pineau, Henriette Trollvik, Herman Greaker, Sveinung Olsen, Yngve Eilertsen, and Ingrid Mann
Atmos. Meas. Tech., 17, 3843–3861, https://doi.org/10.5194/amt-17-3843-2024, https://doi.org/10.5194/amt-17-3843-2024, 2024
Short summary
Short summary
The mesosphere, part of the upper atmosphere, contains small solid dust particles, mostly made up of material from interplanetary space. We are preparing an experiment to collect such particles during a rocket flight. A new instrument has been designed and numerical simulations have been performed to investigate the airflow nearby as well as its dust collection efficiency. The collected dust particles will be further analyzed in the laboratory in order to study their chemical composition.
Tinna L. Gunnarsdottir, Ingrid Mann, Wuhu Feng, Devin R. Huyghebaert, Ingemar Haeggstroem, Yasunobu Ogawa, Norihito Saito, Satonori Nozawa, and Takuya D. Kawahara
Ann. Geophys., 42, 213–228, https://doi.org/10.5194/angeo-42-213-2024, https://doi.org/10.5194/angeo-42-213-2024, 2024
Short summary
Short summary
Several tons of meteoric particles burn up in our atmosphere each day. This deposits a great deal of material that binds with other atmospheric particles and forms so-called meteoric smoke particles. These particles are assumed to influence radar measurements. Here, we have compared radar measurements with simulations of a radar spectrum with and without dust particles and found that dust influences the radar spectrum in the altitude range of 75–85 km.
Samuel Kočiščák, Andreas Kvammen, Ingrid Mann, Nicole Meyer-Vernet, David Píša, Jan Souček, Audun Theodorsen, Jakub Vaverka, and Arnaud Zaslavsky
Ann. Geophys., 42, 191–212, https://doi.org/10.5194/angeo-42-191-2024, https://doi.org/10.5194/angeo-42-191-2024, 2024
Short summary
Short summary
In situ observations are crucial for understanding interplanetary dust, yet not every spacecraft has a dedicated dust detector. Dust encounters happen at great speeds, leading to high energy density at impact, which leads to ionization and charge release, which is detected with electrical antennas. Our work looks at how the transient charge plume interacts with Solar Orbiter spacecraft. Our findings are relevant for the design of future experiments and the understanding of present data.
Devin Huyghebaert, Björn Gustavsson, Juha Vierinen, Andreas Kvammen, Matthew Zettergren, John Swoboda, Ilkka Virtanen, Spencer Hatch, and Karl M. Laundal
EGUsphere, https://doi.org/10.5194/egusphere-2024-802, https://doi.org/10.5194/egusphere-2024-802, 2024
Short summary
Short summary
The EISCAT_3D radar is a new ionospheric radar under construction in the Fennoscandia region. The radar will make measurements of plasma characteristics at altitudes above approximately 60 km. The capability of the system to make these measurements on spatial scales of less than 100 m using the multiple digitised signals from each of the radar antenna panels is highlighted. There are many ionospheric small-scale processes that will be further resolved using the techniques discussed here.
Theresa Rexer, Björn Gustavsson, Juha Vierinen, Andres Spicher, Devin Ray Huyghebaert, Andreas Kvammen, Robert Gillies, and Asti Bhatt
Geosci. Instrum. Method. Data Syst. Discuss., https://doi.org/10.5194/gi-2023-18, https://doi.org/10.5194/gi-2023-18, 2024
Preprint under review for GI
Short summary
Short summary
We present a second-level calibration method for electron density measurements from multi-beam incoherent scatter radars. It is based on the well-known Flat field correction method used in imaging and photography. The methods improve data quality and useability as they account for unaccounted, and unpredictable variations in the radar system. This is valuable for studies where inter-beam calibration is important such as studies of polar cap patches, plasma irregularities and turbulence.
Florian Günzkofer, Dimitry Pokhotelov, Gunter Stober, Ingrid Mann, Sharon L. Vadas, Erich Becker, Anders Tjulin, Alexander Kozlovsky, Masaki Tsutsumi, Njål Gulbrandsen, Satonori Nozawa, Mark Lester, Evgenia Belova, Johan Kero, Nicholas J. Mitchell, and Claudia Borries
Ann. Geophys., 41, 409–428, https://doi.org/10.5194/angeo-41-409-2023, https://doi.org/10.5194/angeo-41-409-2023, 2023
Short summary
Short summary
Gravity waves (GWs) are waves in Earth's atmosphere and can be observed as cloud ripples. Under certain conditions, these waves can propagate up into the ionosphere. Here, they can cause ripples in the ionosphere plasma, observable as oscillations of the plasma density. Therefore, GWs contribute to the ionospheric variability, making them relevant for space weather prediction. Additionally, the behavior of these waves allows us to draw conclusions about the atmosphere at these altitudes.
Tinna L. Gunnarsdottir, Arne Poggenpohl, Ingrid Mann, Alireza Mahmoudian, Peter Dalin, Ingemar Haeggstroem, and Michael Rietveld
Ann. Geophys., 41, 93–114, https://doi.org/10.5194/angeo-41-93-2023, https://doi.org/10.5194/angeo-41-93-2023, 2023
Short summary
Short summary
Temperatures at 85 km around Earth's poles in summer can be so cold that small ice particles form. These can become charged, and, combined with turbulence at these altitudes, they can influence the many electrons present. This can cause large radar echoes called polar mesospheric summer echoes. We use radio waves to heat these echoes on and off when the sun is close to or below the horizon. This allows us to gain some insight into these ice particles and how the sun influences the echoes.
Georg Fischer, Ulrich Taubenschuss, and David Píša
Ann. Geophys., 40, 485–501, https://doi.org/10.5194/angeo-40-485-2022, https://doi.org/10.5194/angeo-40-485-2022, 2022
Short summary
Short summary
The polar light in its various colors and forms has fascinated human beings since ancient times. It is less well known that there are also radio emissions generated in the aurora at higher altitudes. Not just Earth, but some other planets of the solar system also have auroras and corresponding radio emissions. In our publication, we investigate and classify the spectral fine structures of a radio emission called Saturn kilometric radiation to find out more about this radiation process.
Kyoko K. Tanaka, Ingrid Mann, and Yuki Kimura
Atmos. Chem. Phys., 22, 5639–5650, https://doi.org/10.5194/acp-22-5639-2022, https://doi.org/10.5194/acp-22-5639-2022, 2022
Short summary
Short summary
We have investigated the nucleation process of noctilucent clouds observed in the mesosphere using a theoretical approach, where we adopt a more accurate model called the semi-phenomenological model for the nucleation process. We obtained an important result that rejects one of the two dominant nucleation mechanisms that have been proposed. Our results show it is extremely difficult for homogeneous nucleation of water to occur in the mesosphere, while heterogeneous nucleation occurs effectively.
Margaretha Myrvang, Carsten Baumann, and Ingrid Mann
Ann. Geophys., 39, 1055–1068, https://doi.org/10.5194/angeo-39-1055-2021, https://doi.org/10.5194/angeo-39-1055-2021, 2021
Short summary
Short summary
Our model calculations indicate that meteoric smoke particles (MSPs) influence both the magnitude and shape of the electron temperature during artificial heating. Others have found that current theoretical models most likely overestimate heating in the D-region compared to observations. In a future study, we will compare our results to observations of the electron temperature during heating to investigate if the presence of MSPs can explain the discrepancy between model and observations.
Tarjei Antonsen, Ingrid Mann, Jakub Vaverka, Libor Nouzak, and Åshild Fredriksen
Ann. Geophys., 39, 533–548, https://doi.org/10.5194/angeo-39-533-2021, https://doi.org/10.5194/angeo-39-533-2021, 2021
Short summary
Short summary
This paper discusses the charge generation for impacts of nano- to micro-scale dust on metal surfaces at speeds below a few kilometres per second. By introducing a model of capacitive coupling between the dust and the impact surface, we find that at such low speeds, the charge can be dominated by contact charging as opposed to plasma generation.
Joshua Baptiste, Connor Williamson, John Fox, Anthony J. Stace, Muhammad Hassan, Stefanie Braun, Benjamin Stamm, Ingrid Mann, and Elena Besley
Atmos. Chem. Phys., 21, 8735–8745, https://doi.org/10.5194/acp-21-8735-2021, https://doi.org/10.5194/acp-21-8735-2021, 2021
Short summary
Short summary
Agglomeration of ice and dust particles in the mesosphere are studied, using classical electrostatic approaches which are extended to capture the induced polarisation of surface charge. The instances of strong attraction between particles of the same sign of charge are predicted, which take place at small separation distances and also lead to the formation of stable aggregates.
Viswanathan Lakshmi Narayanan, Satonori Nozawa, Shin-Ichiro Oyama, Ingrid Mann, Kazuo Shiokawa, Yuichi Otsuka, Norihito Saito, Satoshi Wada, Takuya D. Kawahara, and Toru Takahashi
Atmos. Chem. Phys., 21, 2343–2361, https://doi.org/10.5194/acp-21-2343-2021, https://doi.org/10.5194/acp-21-2343-2021, 2021
Short summary
Short summary
In the past, additional sodium peaks occurring above the main sodium layer of the upper mesosphere were discussed. Here, formation of an additional sodium peak below the main sodium layer peak is discussed in detail. The event coincided with passage of multiple mesospheric bores, which are step-like disturbances occurring in the upper mesosphere. Hence, this work highlights the importance of such mesospheric bores in causing significant changes to the minor species concentration in a short time.
Carsten Baumann, Margaretha Myrvang, and Ingrid Mann
Ann. Geophys., 38, 919–930, https://doi.org/10.5194/angeo-38-919-2020, https://doi.org/10.5194/angeo-38-919-2020, 2020
Short summary
Short summary
Dust grains exist throughout our solar system. This dust is subject to destruction processes like sublimation and sputtering. Sputtering is the erosion of dust through the impact solar wind and can be very effective near the Sun. We performed calculations to find out how important the sputtering process is compared to the sublimation of dust. Recently launched spacecraft will probe the proximity of the Sun and measure the dust population. Our work will help to understand these measurements.
Henriette Trollvik, Ingrid Mann, Sveinung Olsen, and Yngve Eilertsen
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2020-278, https://doi.org/10.5194/amt-2020-278, 2020
Preprint withdrawn
Short summary
Short summary
We discuss the design of a rocket instrument to collect mesospheric dust consisting of ice with embedded non-volatile meteoric smoke particles. The instrument consists of a collection device and an attached conic funnel. We consider the dust trajectories in the airflow and fragmentation at the funnel. For summer atmospheric conditions at 85 km and assuming that the ice components vaporize we estimate that up to 1014 to 1015 amu of non-volatile dust material can be collected.
Derek McKay and Andreas Kvammen
Geosci. Instrum. Method. Data Syst., 9, 267–273, https://doi.org/10.5194/gi-9-267-2020, https://doi.org/10.5194/gi-9-267-2020, 2020
Short summary
Short summary
Researchers are making increasing use of machine learning to improve accuracy, efficiency and consistency. During such a study of the aurora, it was noted that biases or distortions had crept into the data because of the conditions (or ergonomics) of the human trainers. As using machine-learning techniques in auroral research is relatively new, it is critical that such biases are brought to the attention of the academic and citizen science communities.
Ingrid Mann, Libor Nouzák, Jakub Vaverka, Tarjei Antonsen, Åshild Fredriksen, Karine Issautier, David Malaspina, Nicole Meyer-Vernet, Jiří Pavlů, Zoltan Sternovsky, Joan Stude, Shengyi Ye, and Arnaud Zaslavsky
Ann. Geophys., 37, 1121–1140, https://doi.org/10.5194/angeo-37-1121-2019, https://doi.org/10.5194/angeo-37-1121-2019, 2019
Short summary
Short summary
This work presents a review of dust measurements from spacecraft Cassini, STEREO, MMS, Cluster, Maven and WIND. We also consider the details of dust impacts and charge generation, and how different antenna signals can be generated. We compare observational data to laboratory experiments and simulations and discuss the consequences for dust observation with the new NASA Parker Solar Probe and ESA Solar Orbiter spacecraft.
Matti M. Ala-Lahti, Emilia K. J. Kilpua, Andrew P. Dimmock, Adnane Osmane, Tuija Pulkkinen, and Jan Souček
Ann. Geophys., 36, 793–808, https://doi.org/10.5194/angeo-36-793-2018, https://doi.org/10.5194/angeo-36-793-2018, 2018
Short summary
Short summary
We present a comprehensive statistical analysis of mirror mode waves and the properties of their plasma surroundings in sheath regions driven by interplanetary coronal mass ejection (ICME) to deepen our understanding of these geo-effective plasma environments. The results imply that mirror modes are common structures in ICME sheaths and occur almost exclusively as dip-like structures and in mirror stable stable plasma.
H. Gunell, L. Andersson, J. De Keyser, and I. Mann
Ann. Geophys., 33, 1331–1342, https://doi.org/10.5194/angeo-33-1331-2015, https://doi.org/10.5194/angeo-33-1331-2015, 2015
Short summary
Short summary
In a simulation study of the downward current region of the aurora, i.e. where electrons are accelerated upward, double layers are seen to form at low altitude and move upward until they are disrupted at altitudes of ten thousand kilometres or thereabouts. When one double layer is disrupted a new one forms below, and the process repeats itself. The repeated demise and reformation allows ions to flow upward without passing through the double layers that otherwise would have kept them down.
H. Gunell, L. Andersson, J. De Keyser, and I. Mann
Ann. Geophys., 33, 279–293, https://doi.org/10.5194/angeo-33-279-2015, https://doi.org/10.5194/angeo-33-279-2015, 2015
Short summary
Short summary
In this paper, we simulate the plasma on a magnetic field line above the aurora. Initially, about half of the acceleration voltage is concentrated in a thin double layer at a few thousand km altitude. When the voltage is lowered, electrons trapped between the double layer and the magnetic mirror are released. In the process we see formation of electron beams and phase space holes. A temporary reversal of the polarity of the double layer is also seen as well as hysteresis effects in its position.
A. P. Walsh, S. Haaland, C. Forsyth, A. M. Keesee, J. Kissinger, K. Li, A. Runov, J. Soucek, B. M. Walsh, S. Wing, and M. G. G. T. Taylor
Ann. Geophys., 32, 705–737, https://doi.org/10.5194/angeo-32-705-2014, https://doi.org/10.5194/angeo-32-705-2014, 2014
H. Gunell, J. De Keyser, E. Gamby, and I. Mann
Ann. Geophys., 31, 1227–1240, https://doi.org/10.5194/angeo-31-1227-2013, https://doi.org/10.5194/angeo-31-1227-2013, 2013
C. P. Escoubet, J. Berchem, K. J. Trattner, F. Pitout, R. Richard, M. G. G. T. Taylor, J. Soucek, B. Grison, H. Laakso, A. Masson, M. Dunlop, I. Dandouras, H. Reme, A. Fazakerley, and P. Daly
Ann. Geophys., 31, 713–723, https://doi.org/10.5194/angeo-31-713-2013, https://doi.org/10.5194/angeo-31-713-2013, 2013
I. Mann and M. Hamrin
Ann. Geophys., 31, 39–44, https://doi.org/10.5194/angeo-31-39-2013, https://doi.org/10.5194/angeo-31-39-2013, 2013
Related subject area
Subject: Small bodies (dwarf planets, asteroids, comets) to dust | Keywords: Interplanetary dust
Impact ionization double peaks analyzed in high temporal resolution on Solar Orbiter
Dust sputtering within the inner heliosphere: a modelling study
Dust observations with antenna measurements and its prospects for observations with Parker Solar Probe and Solar Orbiter
Samuel Kočiščák, Andreas Kvammen, Ingrid Mann, Nicole Meyer-Vernet, David Píša, Jan Souček, Audun Theodorsen, Jakub Vaverka, and Arnaud Zaslavsky
Ann. Geophys., 42, 191–212, https://doi.org/10.5194/angeo-42-191-2024, https://doi.org/10.5194/angeo-42-191-2024, 2024
Short summary
Short summary
In situ observations are crucial for understanding interplanetary dust, yet not every spacecraft has a dedicated dust detector. Dust encounters happen at great speeds, leading to high energy density at impact, which leads to ionization and charge release, which is detected with electrical antennas. Our work looks at how the transient charge plume interacts with Solar Orbiter spacecraft. Our findings are relevant for the design of future experiments and the understanding of present data.
Carsten Baumann, Margaretha Myrvang, and Ingrid Mann
Ann. Geophys., 38, 919–930, https://doi.org/10.5194/angeo-38-919-2020, https://doi.org/10.5194/angeo-38-919-2020, 2020
Short summary
Short summary
Dust grains exist throughout our solar system. This dust is subject to destruction processes like sublimation and sputtering. Sputtering is the erosion of dust through the impact solar wind and can be very effective near the Sun. We performed calculations to find out how important the sputtering process is compared to the sublimation of dust. Recently launched spacecraft will probe the proximity of the Sun and measure the dust population. Our work will help to understand these measurements.
Ingrid Mann, Libor Nouzák, Jakub Vaverka, Tarjei Antonsen, Åshild Fredriksen, Karine Issautier, David Malaspina, Nicole Meyer-Vernet, Jiří Pavlů, Zoltan Sternovsky, Joan Stude, Shengyi Ye, and Arnaud Zaslavsky
Ann. Geophys., 37, 1121–1140, https://doi.org/10.5194/angeo-37-1121-2019, https://doi.org/10.5194/angeo-37-1121-2019, 2019
Short summary
Short summary
This work presents a review of dust measurements from spacecraft Cassini, STEREO, MMS, Cluster, Maven and WIND. We also consider the details of dust impacts and charge generation, and how different antenna signals can be generated. We compare observational data to laboratory experiments and simulations and discuss the consequences for dust observation with the new NASA Parker Solar Probe and ESA Solar Orbiter spacecraft.
Cited articles
Alain, G. and Bengio, Y.: Understanding intermediate layers using linear
classifier probes, ArXiv, https://doi.org/10.48550/arXiv.1610.01644, 2016. a
Aubier, M., Meyer-Vernet, N., and Pedersen, B.: Shot noise from grain and
particle impacts in Saturn's ring plane, Geophys. Res. Lett., 10,
5–8, 1983. a
Babic, K. R., Zaslavsky, A., Issautier, K., Meyer-Vernet, N., and Onic, D.: An
analytical model for dust impact voltage signals and its application to
STEREO/WAVES data, Astron. Astrophys., 659, A15, https://doi.org/10.1051/0004-6361/202142508, 2022. a
Boser, B. E., Guyon, I. M., and Vapnik, V. N.: A training algorithm for optimal
margin classifiers, in: Proceedings of the fifth annual workshop on
Computational learning theory, Association for Computing Machinery, 144–152, https://doi.org/10.1145/130385.130401, 1992. a
Bougeret, J.-L., Kaiser, M. L., Kellogg, P. J., Manning, R., Goetz, K., Monson,
S., Monge, N., Friel, L., Meetre, C., Perche, C., Sitruk, L., and Hoang, S.: Waves: The radio
and plasma wave investigation on the Wind spacecraft, Space Sci. Rev.,
71, 231–263, 1995. a
Collette, A., Grün, E., Malaspina, D., and Sternovsky, Z.: Micrometeoroid
impact charge yield for common spacecraft materials, J. Geophys.
Res.-Space, 119, 6019–6026, 2014. a
Cortes, C. and Vapnik, V.: Support-vector networks, Mach. Learn., 20,
273–297, 1995. a
Fawaz, H. I., Lucas, B., Forestier, G., Pelletier, C., Schmidt, D. F., Weber,
J., Webb, G. I., Idoumghar, L., Muller, P.-A., and Petitjean, F.:
InceptionTime: Finding AlexNet for time series classification, Data
Min. Knowl. Disc., 34, 1936–1962,
https://doi.org/10.1007/s10618-020-00710-y, 2020. a
Glorot, X., Bordes, A., and Bengio, Y.: Deep Sparse Rectifier Neural Networks,
in: Proceedings of the Fourteenth International Conference on Artificial
Intelligence and Statistics, edited by: Gordon, G., Dunson, D., and Dudík,
M., Proc. Mach. Learn. Res., 15,
315–323, 2011. a
Goodfellow, I., Bengio, Y., and Courville, A.: Deep learning, MIT press, ISBN: 9780262035613, 2016. a
Grün, E., Zook, H. A., Fechtig, H., and Giese, R.: Collisional balance of
the meteoritic complex, Icarus, 62, 244–272, 1985. a
Gurnett, D. A., Grün, E., Gallagher, D., Kurth, W., and Scarf, F.:
Micron-sized particles detected near Saturn by the Voyager plasma wave
instrument, Icarus, 53, 236–254, 1983. a
He, K., Zhang, X., Ren, S., and Sun, J.: Deep Residual Learning for Image
Recognition, in: IEEE Conference on Computer Vision and Pattern Recognition,
IEEE Comput. Soc., 770–778, https://doi.org/10.1109/CVPR.2016.90, 2016. a
Holzinger, A., Langs, G., Denk, H., Zatloukal, K., and Müller, H.:
Causability and explainability of artificial intelligence in medicine, Wiley
Interdisciplinary Reviews, Data Min. Knowl. Disc., 9, e1312, https://doi.org/10.1002/widm.1312,
2019. a
Hornung, K., Malama, Y. G., and Kestenboim, K. S.: Impact vaporization and
ionization of cosmic dust particles, Astrophys. Space Sci., 274,
355–363, 2000. a
Ioffe, S. and Szegedy, C.: Batch Normalization: Accelerating Deep Network
Training by Reducing Internal Covariate Shift, in: nternational Conference on
Machine Learning, edited by: Bach, F. and Blei, D., Vol. 37,
Proc. Mach. Learn. Res., 37, 448–456, 2015. a
Ishimoto, H.: Modeling the number density distribution of interplanetary dust
on the ecliptic plane within 5 AU of the Sun, Astron. Astrophys., 362,
1158–1173, 2000. a
Karim, F., Majumdar, S., Darabi, H., and Harford, S.: Multivariate
LSTM-FCNs for time series classification, Neural Networks, 116, 237–245,
https://doi.org/10.1016/j.neunet.2019.04.014, 2019. a
Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., and Fei-Fei,
L.: Large-Scale Video Classification with Convolutional Neural Networks, in:
2014 IEEE Conference on Computer Vision and Pattern Recognition, 24–27 June 2014, Columbus, Ohio, USA,
1725–1732, https://doi.org/10.1109/CVPR.2014.223, 2014. a
Kingma, D. and Ba, J.: Adam: A Method for Stochastic Optimization, in:
International Conference on Learning Representations, 7–9 May 2015, San Diego, California, USA, arXiv, https://doi.org/10.48550/arxiv.1412.6980,
2014. a
Kočiščák, S., Kvammen, A., Mann, I., Sørbye, S. H.,
Theodorsen, A., and Zaslavsky, A.: Modelling Solar Orbiter Dust Detection
Rates in Inner Heliosphere as a Poisson Process, arXiv preprint
arXiv:2210.03562, https://doi.org/10.48550/arxiv.2210.03562, 2022. a, b, c
Kvammen, A.:
AndreasKvammen/ML_dust_detection: v1.0.0 (v1.0.0), Zenodo [code and data set],
https://doi.org/10.5281/zenodo.7404457, 2022. a
Kvammen, A., Wickstrøm, K., McKay, D., and Partamies, N.: Auroral image
classification with deep neural networks, J. Geophys. Res.-Space, 125, e2020JA027808, https://doi.org/10.1029/2020JA027808, 2020. a
Maksimovic, M., Bale, S., Chust, T.,
et al.: The solar orbiter radio and plasma waves (rpw) instrument, Astron.
Astrophys., 642, A12, https://doi.org/10.1051/0004-6361/201936214, 2020. a
Malaspina, D. M., Newman, D. L., Willson III, L. B., Goetz, K., Kellogg, P. J.,
and Kerstin, K.: Electrostatic solitary waves in the solar wind: Evidence for
instability at solar wind current sheets, J. Geophys. Res.-Space, 118, 591–599, 2013. a
Malaspina, D. M., Szalay, J. R., Pokornỳ, P., Page, B., Bale, S. D.,
Bonnell, J. W., de Wit, T. D., Goetz, K., Goodrich, K., Harvey, P. R.,
MacDowall, R. J., and Pulupa, M.: In situ observations of interplanetary dust variability in the inner
heliosphere, Astrophys. J., 892, 115, https://doi.org/10.3847/1538-4357/ab799b, 2020. a
Mann, I. and Czechowski, A.: Dust destruction and ion formation in the inner
solar system, Astrophys. J., 621, L73, https://doi.org/10.1086/429129, 2005. a
Mann, I. and Czechowski, A.: Dust observations from Parker Solar Probe: dust
ejection from the inner Solar System, Astron. Astrophys., 650, A29, https://doi.org/10.1051/0004-6361/202039362,
2021. a, b
Mann, I., Nouzák, L., Vaverka, J., Antonsen, T., Fredriksen, Å., Issautier, K., Malaspina, D., Meyer-Vernet, N., Pavlů, J., Sternovsky, Z., Stude, J., Ye, S., and Zaslavsky, A.: Dust observations with antenna measurements and its prospects for observations with Parker Solar Probe and Solar Orbiter, Ann. Geophys., 37, 1121–1140, https://doi.org/10.5194/angeo-37-1121-2019, 2019. a
Montavon, G., Orr, G. B., and Müller, K.-R. (Eds.): Neural Networks: Tricks
of the Trade, Springer Berlin Heidelberg, https://doi.org/10.1007/978-3-642-35289-8,
2012. a
Müller, D., Cyr, O. S., Zouganelis, I., Gilbert, H. R., Marsden, R.,
Nieves-Chinchilla, T., Antonucci, E., Auchère, F., Berghmans, D.,
Horbury, T. S., Howard, R. A., Krucker, S., Maksimovic, M., Owen, C. J., Rochus, P., Rodriguez-Pacheco, J., Romoli, M., Solanki, S. K., Bruno, R., Carlsson, M., Fludra, A., Harra, L., Hassler, D. M., Livi, S., Louarn, P., Peter, H., Schühle, U., Teriaca, L., del Toro Iniesta, J. C., Wimmer-Schweingruber, R. F., Marsch, E., Velli, M., De Groof, A., Walsh, A., and Williams, D.: The solar orbiter mission-science overview, Astron.
Astrophys., 642, A1, https://doi.org/10.1051/0004-6361/202038467, 2020. a
Samek, W., Montavon, G., Lapuschkin, S., Anders, C. J., and Müller, K.-R.: Explaining Deep Neural Networks and Beyond: A Review of
Methods and Applications, Proceedings of the IEEE, 247–278, https://doi.org/10.1109/JPROC.2021.3060483, 2021. a
Shwartz-Ziv, R. and Tishby, N.: Opening the Black Box of Deep Neural Networks
via Information, ArXiv, abs/1703.00810, https://doi.org/10.48550/arxiv.1703.00810, 2017. a
Soucek, J., Píša, D., Kolmasova, I., Uhlir, L., Lan, R.,
Santolík, O., Krupar, V., Kruparova, O., Baše, J., Maksimovic, M., Bale, S. D., Chust, T., Khotyaintsev, Yu. V., Krasnoselskikh, V., Kretzschmar, M., Lorfèvre, E., Plettemeier, D., Steller, M., Štverák, Š., Vaivads, A., Vecchio, A., Bérard, D., and Bonnin, X.: Solar Orbiter Radio and Plasma Waves–Time Domain Sampler: In-flight
performance and first results, Astron. Astrophys., 656, A26, https://doi.org/10.1051/0004-6361/202140948, 2021. a, b, c, d, e, f
Szalay, J., Pokornỳ, P., Bale, S., Christian, E., Goetz, K., Goodrich, K.,
Hill, M., Kuchner, M., Larsen, R., Malaspina, D., McComas, D. J., Mitchell, D., Page, B., and Schwadron, N.: The near-sun dust
environment: initial observations from parker solar probe, Astrophys.
J. Suppl. Ser., 246, 27, https://doi.org/10.3847/1538-4365/ab50c1, 2020. a, b, c
Theodoridis, S. and Koutroumbas, K.: Chap. 3 – Linear Classifiers, in:
Pattern Recognition (Fourth Edition), edited by: Theodoridis, S. and
Koutroumbas, K., 91–150, Academic Press, Boston, 4th Edn.,
https://doi.org/10.1016/B978-1-59749-272-0.50004-9, 2009. a
Trosten, D. J., Strauman, A. S., Kampffmeyer, M., and Jenssen, R.: Recurrent
Deep Divergence-based Clustering for Simultaneous Feature Learning and
Clustering of Variable Length Time Series, in: ICASSP 2019–2019 IEEE
International Conference on Acoustics, Speech and Signal Processing (ICASSP),
3257–3261, https://doi.org/10.1109/ICASSP.2019.8682365, 2019. a
Van der Maaten, L. and Hinton, G.: Visualizing data using t-SNE, J.
Mach. Learn. Res., 9, 2579–2605, http://jmlr.org/papers/v9/vandermaaten08a.html (last access: 12 January 2023), 2008. a
Vaverka, J., Pellinen-Wannberg, A., Kero, J., Mann, I., De Spiegeleer, A.,
Hamrin, M., Norberg, C., and Pitkänen, T.: Potential of earth orbiting
spacecraft influenced by meteoroid hypervelocity impacts, IEEE Trans.
Plasma Sci., 45, 2048–2055, 2017. a
Vech, D. and Malaspina, D. M.: A novel machine learning technique to identify
and categorize plasma waves in spacecraft measurements, J.
Geophys. Res.-Space, 126, e2021JA029567, https://doi.org/10.1029/2021JA029567, 2021. a
Villar, J. R., Vergara, P., Menéndez, M., de la Cal, E., González,
V. M., and Sedano, J.: Generalized Models for the Classification of Abnormal
Movements in Daily Life and its Applicability to Epilepsy Convulsion
Recognition, International J. Neural Syst., 26, 1650037,
https://doi.org/10.1142/s0129065716500374, 2016.
a
Wang, Z., Yan, W., and Oates, T.: Time series classification from scratch with
deep neural networks: A strong baseline, in: 2017 International joint
conference on neural networks (IJCNN), 1578–1585, IEEE, https://doi.org/10.1109/IJCNN.2017.7966039, 2017. a, b, c
Wickstrøm, K., Mikalsen, K. Ø., Kampffmeyer, M., Revhaug, A., and
Jenssen, R.: Uncertainty-Aware Deep Ensembles for Reliable and Explainable
Predictions of Clinical Time Series, IEEE J. Biomed. Health, 25, 2435–2444, https://doi.org/10.1109/jbhi.2020.3042637, 2021. a, b
Wickstrøm, K., Kampffmeyer, M., Mikalsen, K. Ø., and Jenssen, R.: Mixing
up contrastive learning: Self-supervised representation learning for time
series, Pattern Recogn. Lett., 155, 54–61,
https://doi.org/10.1016/j.patrec.2022.02.007, 2022. a, b
Zaslavsky, A., Meyer-Vernet, N., Mann, I., Czechowski, A., Issautier, K.,
Le Chat, G., Pantellini, F., Goetz, K., Maksimovic, M., Bale, S. D., and Kasper, J. C.:
Interplanetary dust detection by radio antennas: Mass calibration and fluxes
measured by STEREO/WAVES, J. Geophys. Res.-Space,
117, A5, https://doi.org/10.1029/2011JA017480, 2012. a, b
Zaslavsky, A., Mann, I., Soucek, J., Czechowski, A., Píša, D.,
Vaverka, J., Meyer-Vernet, N., Maksimovic, M., Lorfèvre, E., Issautier,
K., Rackovic Babic, K., Bale, S. D., Morooka, M., Vecchio, A., Chust, T., Khotyaintsev, Y., Krasnoselskikh, V., Kretzschmar, M., Plettemeier, D., Steller, M., Štverák, Š., Trávníček, P., and Vaivads, A.: First dust measurements with the Solar Orbiter Radio and Plasma
Wave instrument, Astron. Astrophys., 656, A30, https://doi.org/10.1051/0004-6361/202140969, 2021. a, b, c, d, e, f, g
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., and Torralba, A.: Learning deep
features for discriminative localization, in: Proceedings of the IEEE
conference on computer vision and pattern recognition, 2921–2929, arXiv, https://doi.org/10.48550/arxiv.1512.04150, 2016. a
Short summary
Collisional fragmentation of asteroids, comets and meteoroids is the main source of dust in the solar system. The dust distribution is however uncharted and the role of dust in the solar system is largely unknown. At present, the interplanetary medium is explored by the Solar Orbiter spacecraft. We present a novel method, based on artificial intelligence, that can be used for detecting dust impacts in Solar Orbiter observations with high accuracy, advancing the study of dust in the solar system.
Collisional fragmentation of asteroids, comets and meteoroids is the main source of dust in the...