EDP Sciences logo
Subscriber Authentication Point
Search
Menu
Home All issues Volume 644 (December 2020) A&A, 644 (2020) A53 Full HTML
Open Access
Issue
A&A
Volume 644, December 2020
Article Number A53
Number of page(s) 23
Section Planets and planetary systems
DOI https://doi.org/10.1051/0004-6361/202038649
Published online 02 December 2020

© F. Colas et al. 2020

Licence Creative CommonsOpen Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1 Introduction

The study of the physical and dynamical properties of interplanetary matter, such as interplanetary dust particles (IDPs), meteoroids, asteroids, comets, is crucial to our understanding of the formation and evolution of the solar system. This matter exists in many sizes, from micron-sized dust grains to several hundred kilometer-sized bodies. Whereas the largest bodies are routinely studied via Earth-based telescopic observations as well as less frequent interplanetary missions, the smallest bodies (diameter ≤10 m) are for the most part only observed and characterized when they enter the Earth’s atmosphere as their entry generates enough light to be recorded by even the simplest types of cameras; the smaller particles are called meteors and the larger bodies are fireballs.

We know that ~100 tons of extraterrestrial material collide with the Earth daily, mostly as small particles less than 0.2 mm in size (Zolensky et al. 2006, Rojas et al. 2019). At present, these small particles, called IDPs, are actively being collected in the stratosphere, from polar ices (Duprat et al. 2007), and within impact features on spacecraft (Moorhead et al. 2020). For such particles, the stratospheric collections provide the least contaminated and heated samples. At the other end of the size distribution of extraterrestrial material colliding with the Earth, meteorites are fragments that have survived the passage through the atmosphere without internal chemical alteration, which have been recovered at the surface of the Earth. To date, all known meteorites are pieces of either asteroids, the Moon, or Mars, with asteroidal fragments dominating the flux of material, whereas IDPs originate mostly from comets and possibly from asteroids (Bradley et al. 1996; Vernazza et al. 2015). The most detailed information on the processes, conditions, timescales, and chronology of the early history of the solar system (e.g., Neveu & Vernazza 2019; Kruijer & Kleine 2019 and references therein), including the nature and evolution of the particles in the pre-planetary solar nebula, has so far come from the study of all these extraterrestrial materials. Recovering intact samples of such materials is therefore a critical goal of planetary studies.

However, we are not very efficient at recovering the meteorites that hit the Earth. Estimates based on previous surveys (Bland et al. 1996) and on collected falls [Meteoritical Bulletin database1 ] indicate that, for meteorites with masses greater than 100 g, probably less than 1 in 500 that fall on Earth are currently recovered. In addition, taking France as an example, recovery rates were significantly higher in the nineteenth century than they are now: 45 meteorites were observed to fall and found on the ground in the nineteenth century, whereas they were 5 times fewer in the twentieth century (Fig. 1), showing that there is at present a large potential for improvement. Hot and cold deserts are privileged dense collection areas, but most meteorites are found hundreds and up to millions of years after their fall (Hutzler et al. 2016; Drouard et al. 2019). They have thus been exposed to terrestrial alteration, which has partly obliterated the scientific information they contain. Also, the critical information regarding their pre-atmospheric orbit is no longer available.

The most efficient approach for recovering freshly fallen meteorites is to witness their bright atmospheric entry via dense (60–120 kmspacing) camera and radio networks. These networks make it possible to accurately calculate their trajectory from which both their pre-atmospheric orbit and their fall location (with an accuracy on the order of a few hundred meters) can be constrained.

Records of incoming meteorites started with the appearance of photographic plates at the end of the twentieth century. A first attempt to observe incoming bolides was made in the United States and consisted of a small camera network that was operated between 1936 and 1951 (Whipple 1938), but it was only in the middle of the twentieth century that the first fireball observation networks were developed with the aim of recovering meteorites. Two such networks were established in the 1960s. The first was the Prairie Network (McCrosky & Boeschenstein 1965) in the center of the United States, which remained operational from 1964 to 1975. This network comprised 16 stations located 250 km apart. Only one meteorite was recovered thanks to this network (Lost City, 1970; McCrosky et al. 1971). The low efficiency of the Prairie Network, despite the large area it covered (750 000 km2) mainly resulted from the low efficiency of the photographic plates, the large distance between the stations, and the slow pace of the data reduction process.

The European Fireball Network (EFN) was also developed in the 1960s, under the guidance of the Ondrejov Observatory, following the recovery of the Příbram meteorite in 1959 (Ceplecha 1960). It is still active, currently covers 1 × 106 km2 with about 40 cameras, (Oberst et al. 1998) and benefits from modern equipment. So far, this network has enabled the recovery of nine meteorites (Table 1).

In 1971, the Meteorite Observation and Recovery Project (MORP) project was established over part of Canada and led to the recovery of the Innisfree meteorite (Halliday et al. 1978). The modern digital camera extension of this network, called the Southern Ontario Meteor Network, led to the recovery of the Grimsby meteorite (Brown et al. 2011). The MORP project comprises 16 cameras and covers a surface area of 700 000 km2. Other networks using photographic techniques have also been developed, such as the Tajikistan Fireball Network (Kokhirova et al. 2015), which consists of 5 cameras and covers 11 000 km2. However, none of these other networks have made it possible to recover meteorites so far. We note the existence of other networks such as the SPMN network, which facilitated the recovery of the Villalbeto de la Peña (Trigo-Rodríguez et al. 2006) and Puerto Lápice (Llorca et al. 2009) meteorites, as well as the Finnish Fireball Network, which facilitated recovering the Annama meteorite (Gritsevich et al. 2014; Trigo-Rodríguez et al. 2015). Last, the Desert Fireball Network (Bland et al. 2012) was implemented in Australia in 2007. This network is based on high-resolution digital cameras and has made it possible to recover four meteorites: Bunburra Rockhole in 2007 (Spurný et al. 2012), Mason Gully in 2010 (Dyl et al. 2016), Murrili in 2015 (Bland et al. 2016), and Dingle Dell in 2016 (Devillepoix et al. 2018). The success of this network results from the efficiency of the cameras and the size of the network as well as an efficient data reduction and analysis process (Sansom et al. 2019a). A method to construct a successful fireball network is discussed in Howie et al. (2017).

As of today, there are 38 meteorites with reliable reconstructed orbits, 22 of which were detected by camera networks (see Table 1). Among the remaining 16 meteorites, 14 are the result of random visual observations such as the Chelyabinsk event (data from security cameras were used for orbit computation; Borovička et al. 2013a) and two meteorites were detected as asteroids before their fall (Almahata Sitta and 2018LA). During the same time interval (1959-2020), 397 meteorites were recovered after their falls were witnessed by eye (Meteoritical Bulletin Database).

The main limitation of current networks is their size. Most of these networks consist of a fairly small number of cameras spread over a comparatively small territory. Altogether, they cover only 2% of the total surface of the Earth (Devillepoix et al. 2020). This implies that the number of bright events per year witnessed by these networks is small and that decades would be necessary to yield a significant number (≥100) of samples.

The Fireball Recovery and InterPlanetary Observation Network (FRIPON) scientific project was designed to contribute to this global effort to recover fresh meteorites. It comprises a network deployed over a large fraction of western Europe and a small fraction of Canada (seeFig. 2). As of today, this network consists of 150 cameras and 25 receivers for radio detection and covers an area of 1.5 × 106 km2 (Sect. 2). The FRIPON network is coupled in France with the Vigie-Ciel citizen science program, the aim of which is to involve the general public in the search for meteorites in order to improve their recovery rate. In the present paper, we first describe the technology of the FRIPON network and its architecture, and finally we give the first results obtained after four years of observations and report on the first meteorite recovery in Italy2 (Gardiol et al. 2020).

thumbnail Fig. 1

Distribution of observed meteorite falls in France. In nineteenth century France, 45 meteorites were recovered after their fall was observed, a number that fell by a factor of 5 in the twentieth century. Even in the nineteenth century, witnessed falls were not randomly distributed. They were mostly located in the great river plains (Seine and Loire in the northwest, Garonne in the southwest, and Rhône valley in the southeast). In these regions, the population was denser, the view is free of obstacles (such as mountains), and the skies are often clear. The striking difference between the two centuries illustrates the need for distributed observers for meteorite recovery. Rural populations have declined because of urbanization in the twentieth century. A camera network such as FRIPON can monitor atmospheric entries and take over that role that was previously played by human observers. However, trained human eyes are still required to recover the meteorites; this is the aim of the Vigie-Ciel citizen science program (Colas et al. 2015).
Table 1

Thirty-eight known meteorites with reliable orbit reference discovered by networks (“N”), visual observations (“V”) or telescopic observations (“T”).

2 FRIPON science project

2.1 General description of the network

The FRIPON science project was originally designed by a core team of six French scientists from the Paris Observatory (IMCCE), the French National Museum of Natural History (MNHN-IMPMC), Université Paris-Saclay (GEOPS), and Aix-Marseille University (LAM / CEREGE / OSU Pythéas) to: (i) monitor the atmospheric entry of fireballs, that is, interplanetary matter with typical sizes greater than ~1 cm; (ii) characterize their orbital properties to constrain both their origin and fall location; and (iii) recover freshly fallen meteorites. This project benefited from a grant from the French National research agency (Agence Nationale de la Recherche: ANR) in 2013 to install a network of charged coupled device (CCD) cameras and radio receivers to cover the entire French territory. Specifically, the grant was used to design the hardware (Sect. 2.2), building on experience gained from previous networks; develop an efficient and automatic detection and data reduction pipeline (Sect. 2.3); and build centralized network and data storage architectures (Sect. 2.2.3). The FRIPON project is designed as a real-time network with the aim of triggering a field search within the 24 h that follow the fall in order to recover fresh meteorites. As of today, FRIPON-France consists of 105 optical all-sky cameras and 25 receivers for radio detection. These assets are homogeneously distributed over the territory, although the radio network is slightly denser in the south of France (Fig. 2).

Starting from 2016, scientists from neighboring countries were interested in joining the scientific project through the use of the FRIPON-France3 hardware, software, and infrastructure. This was the case for Italy (PRISMA network; Gardiol et al. 2016; Barghini et al. 2019), Germany (FRIPON-Germany), Romania (FRIPON-MOROI network; Anghel et al. 2019a; Nedelcu et al. 2018), the United Kingdom (FRIPON-SCAMP), Canada (FRIPON-DOME), the Netherlands (FRIPON- Netherlands), Spain (FRIPON-Spain), Belgium (FRIPON-Belgium), and Switzerland (FRIPON-Switzerland). Single FRIPON cameras were also made available to the following countries to initiate new collaborations: Austria, Brazil, Chile, Denmark, Mexico, Morocco, Peru, and Tunisia. As of today, 150 cameras, using FRIPON technology, and 25 radio receivers are operational around the world (see Fig. 2).

The FRIPON science project regroups all the above-mentioned national networks, with all the cameras monitored and remotely controlled by the Service Informatique Pythéas (SIP; Aix-Marseille University, France), which maintains the whole network with the support of the scientific team. All the data from the FRIPON network are stored and processed in Marseille. The data processing consists of monthly astrometric and photometric reduction of the calibration images and daily processing of multi-detections. Two databases host the data. One stores the raw data and the other stores higher-level, processed data, such as orbits and trajectories. These data are available to all coinvestigators of the network4. On request, national data can be sent to a different reduction pipeline for alternate processing and storage5.

thumbnail Fig. 2

FRIPON network map as of end 2019. The color code is the following: 1. Blue: FRIPON-France, optical stations. 2. Red: coupled optical camera and radio receiver stations. 3. Black: stations under development. 4. Green: PRISMA (Italy). 5. Light Orange: MOROI (Romania). 6. Yellow: FRIPON-Belgium/Neterlands/Germany/Denmark. 7. Gray: SCAMP (United Kingdom). 8. Dark blue: DOME (Canada). 9. Dark Orange: SPMN (Spain). 10. Pink: GRAVES radar.

2.2 Hardware and observing strategy

2.2.1 Optical cameras

Since the early 2000s, digital cameras have been used by all networks that are deployed to monitor fireballs. Two alternate technical solutions are adopted. The first is based on a low-resolution detector (e.g., Southern Ontario Meteor Network; Brown et al. 2011), while the second relies on a high-resolution detector (e.g., Desert Fireball Network; Bland et al. 2012). The measurements acquired by low-resolution cameras can be accurate enough to compute orbits and strewn fields as long as the network is dense, with numerous cameras. For example, the Southern Ontario Meteor Network, which has been operating in Canada since 2004, led to the recovery of the Grimsby meteorite (Brown et al. 2011). In the case of the FRIPON network, we followed the philosophy of the Canadian Fireball Network (Brown et al. 2011) as detailed hereafter.

We used a CCD Sony ICX445 chip with 1296 × 964 pixels and a pixel size of 3.75 × 3.75 μm. For the optical design, we used a 1.25 mm focal length F/2 fish-eye camera lens, which leads to a pixel scale of 10 arcmin. Given that fireballs are typically observed at an altitude between 100 and 40 km, we designed a network with a median distance of 80 km between cameras to perform an optimal triangulation. Jeanne et al. (2019) showed thatthe astrometric accuracy is on the order of 1 arcmin, equivalent to 30 m at a distance of 100 km. In Sect. 3, we show that the final accuracy on the trajectory is on the order of 20 m for the position and of 100 m s−1 for the velocity; this value is required for the identification of meteorite source regions in the solar system as shown by Granvik & Brown (2018).

The optical device and the CCD were embedded into a special case (Fig. 3) sealed with a transparent dome, thereby allowing us to record full-sky images. Moreover, these cases are equipped with a passive radiator, which serves to release the heat produced by the electronics during the warm periods of the year to minimize CCD dark current.

Each camera is controlled by an Intel NUCi3 computer on which the data are temporarily stored. A single power over ethernet (PoE) cable is used for data transfer and for powering and remotely managing the camera through a TPLINK (TL-SG22110P or 1500G-10PS) switch. Such a solution makes it easy to install the optical station and operate it remotely and to use cables up to 100 meters long between the camera and the computer. Figure 3 shows the design6 of the camera as well as its installation at the Pic du Midi Observatory.

thumbnail Fig. 3

Mosaic of technology developed for the FRIPON network: (a) Final design of optical detectors2. (b) Core device comprising a GigaBit Ethernet camera and fish-eye optics. (c) FRIPON optical camera installed on the platform of Pic du Midi Observatory (2876 m altitude), in use during harsh weather conditions.

2.2.2 Radio receivers

In addition to optical observations, we used the powerful signal of the GRAVES radar of the French Air Force. This radar is particularly well adapted for the detection, identification, and tracking of space targets including incoming meteoroids (Michal et al. 2005). Located near Dijon (Burgundy, central eastern France), its four main beams transmit nominally on a half-volume located south of a line between Austria and western France. However, the secondary radiation lobes of the radar make it possible to also detect meteors that disintegrate in the northern part of France. For such observations we do not need as tight a mesh as we do for the optical network. We have 25 stations with an average distance of 200 km, mainly in France, but also in Belgium, United Kingdom, Italy, Switzerland, Spain, and Austria. The GRAVES radar system transmits on 143.050 MHz in a continuous wave (CW) mode 24 hours a day. A meteoroid entering the E and D layers of theEarth ionosphere produces ions and free electrons generated by the ionization of air and of meteoroid molecules. The free electrons have the property of scattering radio waves according to “back or forward meteor scatter” modes when they are illuminated by a radio transmitter. The FRIPON radio setup is presented in the appendix.

2.2.3 Data storage and access

The FRIPON stations are composed of a Linux minicomputer, a wide-angle camera, and a manageable switch guaranteeing the isolation of the network of the host institute. The installation is done with an automated deployment system based on a USB key.

When connecting to the host, the station establishes a secure VPN tunnel to the central server of the FRIPON project hosted by the information technology department of the OSU Institut Pythéas (SIP) for all cameras and partner networks worldwide. The minicomputer is used for the acquisition and temporary storage of long exposure captures, and detections through the FreeTure open source software (Audureau et al. 2014) and a set of scripts. The data, which include astrometric long exposures images, single detection (stacked images), and multiple detections (both optical and radio raw data) are subsequently transferred to the central server.

The data collected on the server are then indexed in a database. During this operation, visuals are generated. When an optical event groups at least two stations, the FRIPON pipeline is executed to generate the dynamical and physical properties of the incoming meteoroid such as its orbit, its mass and its impact zone.

All the data are made available through a web interface that is accessible to the worldwide community in real time7. This interface makes it possible to display and download data in the form of an archive that complies with the data policy of the project by means of access right management.

2.2.4 Detection strategy

The acquisition and detection software FreeTure was specifically developed by the FRIPON team and runs permanently on the minicomputers (see Audureau et al. 2014 for a full description). The images corresponding to single detections by FreeTure are stored locally and a warning (time and location) is sent to the central server in Marseille. If at least one other station detects an event within +/− 3 s, it is then treated as a “multiple detection”. We note that we implemented a distance criterion of less than 190 km to avoid false detections. This value was determined empirically by manually checking one year of double detections. This strategy works well during the night, but leads to 30% of false detections mainly during twilight.

Radio data corresponding to the last week of acquisition are only stored locally. Only radio data acquired at the time of an optical multi-detection are uploaded from the radio stations to the Marseille data center for processing.

2.3 Dataprocessing

2.3.1 Optical data

Scientific optical data are CCD observations recorded at a rate of 30 frames per second (fps). This acquisition rate is necessary to avoid excessive elongation of the meteor in the images in the case of high speed fireballs. For example, a typical bolide with an average speed of 40 km s−1 at 100 km altitude at the zenith leads to a 20°/s apparent speed on the sky and to a four pixel elongated trail on the CCD. It is larger than the average width of the point spread function (PSF; typically 1.8 pixels), but still easy to process for centroid determination. No dark and flatfield corrections are made.

However, almost no reference star is measurable on a single frame with such an acquisition speed, as the limiting magnitude is about zero. It is thus necessary to record images with a longer exposure time for calibration. We therefore recorded five second exposure images every ten minutes; the goal is to have a decent signal-to-noise ratio (S/N) up to a magnitude of 4.5 and to only marginally affect detection efficiency. Such a calibration strategy allows the detection of a few thousand calibration stars for a given camera on a clear night. To mitigate the effect of cloudy nights and breakdowns, we computed an astrometric calibration once per month for each station. This works for most cameras as their mounts are rigid. However, we occasionally detected flexible mounts based on the repeated calibrations, which led us to shorten the masts of such stations.

Calibration procedure uses the ICRF28 reference frame. The distortion function of the optical system is computed in the topocentric horizontal reference system. This allows for an astrometric solution for stars above 10 degrees of elevation with an accuracy of 1 arcmin. Our procedure leads to the calculation of the azimuth and the elevation of the bolides in the J2000 reference frame. More details regarding our astrometric calibration procedure can be found in Jeanne et al. (2019).

For the photometric reduction, we used the same frames as for the astrometric calibration, namely the long exposure frames. We then established a correspondence between the observed stars and those present in the HIPPARCOS catalog (Bessell 2000). The following steps are subsequently applied to calculate the absolute magnitude light curve of a meteor, namely: (i) determination of the flux of an equivalent magnitude 0 star at zenith and the linear extinction function of the air mass for one-month cumulative observation; (ii) measurement of the bolide flux on individual frames and conversion in magnitude; and (iii) conversion of the meteor magnitude Mag into an absolute magnitude AMag, defined as its magnitude at a distance of 100 km, (1)

Figure 4 shows the final absolute magnitude light curve of an event recorded by 15 stations on 27 February 2019. We notice that the closest station saturates faster with a −8 magnitude plateau compared to the other cameras. These light curves are saturated at different times, depending on their distance to the bright flight. For the brightest part of the light curve, a saturation model will be applied in the future. At this point, we point out several limitations of our data reduction procedure as follows:

  • couds may partly cover the night sky, which may bias the measure of instrumental magnitudes;

  • meteors are mainly detected at small elevations (typically below 30°). These records are therefore affected by nonlinearities of the atmospheric extinction;

  • a uniform cloud layer can be the source of an under estimation of bolide magnitude.

The first photometric measurements of the FRIPON network are reflected in the histogram of all detections in Sect. 3.1.2. Routines to merge all light curves into one are now under development. As our data reduction is based on dynamics, the photometric curves are only used at present to detect major events.

To summarize, the astrometric reduction allows us to obtain an accuracy of one-tenth pixel or 1 arcmin for meteor measurements. Photometry is at that time only usable for events with an absolute magnitude lower than − 8 with an accuracy of 0.5 magnitude.

2.3.2 Trajectory determination

Most of our method is described in Jeanne et al. (2019) and in Jeanne (2020) and is only be recalled briefly in this section. Owing to the limited accuracy of the Network Time Protocol (NTP; Barry et al. 2015), which is typically 20 ms, we first use a purely geometrical model (without taking into account time) by assuming that the trajectory follows a straight line, after the approach of Ceplecha (1987). This method allows us to separate the space and time components of our measurements and to overcome the problem of temporal accuracy. We give special attention to global error estimation, which becomes accessible thanks to the large number of cameras involved in most of FRIPON’s detections. By comparison, the detections of other networks usually involve fewer cameras, making external biases nonmeasurable and hard to evaluate.

The density of the FRIPON network makes it possible to observe an event with many cameras (15 in the case of the 27 February 2019 event; see Fig. 4). It is then possible to consider the external astrometric bias of each cameraas a random error and to estimate it by a statistical method. Therefore, we developed a modified least-squares regression to fit the data taking into account the internal and external or systematic error on each camera.

We first estimate the internal error of each camera by fitting a plane passing through the observation station and all the measured points. The average internal error of the cameras amounts to 0.75 arcmin, which corresponds to 0.07 pixel. We also compute a first estimation of the external error by averaging distances between the observed positions of stars and those calculated from the HIPPARCOS catalog (Bessell 2000) in a neighborhood of 100 pixels around the meteor. We then compute a global solution using the modified least-squares estimator of the trajectory given by the minimization of the following sum: (2)

where is the residual between the jth measure taken by the ith camera and the trajectory , σi is the internal error of the ith camera, si is the systematic error of the ith camera, and ni is the number of images taken by the ith camera.

This method allows us to characterize the systematic errors of our cameras (e.g., a misaligned lens), but not errors such as the location of the camera. To tackle these errors, we compute a first estimate of the trajectory and we compare the residuals with the expected random and systematic errors. If they are larger than expected for a specific camera, we iteratively decrease its weight during the calculation of the trajectory. The final systematic error is usually on the order of 0.3 arcmin, which ends the iterative process.

Two geometric configurations lead to important errors or degeneracies in the trajectory determination: stations located too far fromthe fireball and stations aligned with the trajectory of the fireball. However, most of the time, the final bright flight straight line trajectory is known with a precision of a few tens of meters. In a second step, all individual data points with time stamps are projected on the straight line to be used afterward for dynamical purposes.

thumbnail Fig. 4

Top: event on 27 February 2019 seen by the Beaumont-lès-Valence FRIPON camera. Bottom: absolute magnitude light curves of the event as seen by 15 cameras; the red curve is Beaumont-lès-Valence. It is clear that the saturation limit is around magnitude –8 (all the other light curves fall above this limit). Cameras located further away may be able to measure more non-saturated data, but all the cameras become heavily saturated as the bolide reaches its maximum luminosity.

2.3.3 Orbit, drag, and ablation model

To compute the orbit of the bolide parent body, we need to measure its velocity before it has experienced significant interaction with the upper atmosphere. This interaction starts well before the bright flight. Therefore, we need a deceleration model to estimate theinfinite velocity, even if the deceleration is not measurable, which happens to be the case for many events (especially the high speed events). This problem is complex because physical parameters evolve during atmospheric entry and moreover several parameters are unknown such as drag coefficient, object size, shape, density and strength. Like other teams (Lyytinen & Gritsevich 2016, Bouquet et al. 2014, Sansom et al. 2019b, etc...) we use a simple physical model to fit the bright flight data.

We used a dynamic model from Bronshten (1983), Eqs. (3) and (4). This model describes the deceleration and ablation of a meteoroid in an atmosphere based on the following three equations:

where cd is the drag coefficient, ch the heat-transfer coefficient, H is the enthalpy of destruction, ρatm is the gas density, m is the normalized meteoroid mass, Me is the pre-entry mass, s is the normalized cross-section area, Se is the pre-entry cross-section area, μ is the so-called shape change coefficient. The atmospheric gas density ρatm is taken from the empirical model NRLMSISE-00 (Lyytinen & Gritsevich 2016).

These three equations can be rewritten into two independent equations (Turchak & Gritsevich 2014). The equation of motion is written as (6)

and the equation of mass is written as (7)

where A is a deceleration parameter (in square meters per kilogram) and B is an ablation parameter (in square meters per joule) as follows:

We used our model to fit the positions of each observation that is projected on the trajectory line (Jeanne et al. 2019). With this model, the observation of a meteor motion makes it possible to estimate the value of the three parameters Ve, A, and B. Using A and B rather than their ratio A∕B, which is proportional to the enthalpy of destruction H of the meteoroid (Turchak & Gritsevich 2014), allowed us to avoid the numerical singularity when B gets close to zero. Jeanne (2020) demonstrated that the least-squares estimators of these three parameters have always defined variances and meaningful values, even in the case of faint meteors. Finally, we computed confidence intervals in the three-dimensional parameter space (Ve, A, B).

2.3.4 Dark flight

At the end of the bright flight, a meteoroid is subject only to aerodynamic drag (including winds) and gravity. At this stage, the meteoroid speed is too low to cause ablation (hence dark flight).

The equation of motion during dark flight is as follows: (8)

where Af(Vw) is the deceleration parameter of the fragment, which depends on the wind velocity (relative to the fragment) Vw. We used a local atmospheric model of wind retrieved from meteorological offices.

The end of the bright flight simulation gives us the initial conditions of the dark flight motion, namely the initial position, speed, and acceleration of the fragment. The initial condition of acceleration gives us a definition of Af0, the limit of Af when wind velocity is huge in front of sound velocity cs as follows: (9)

The evolution of Af as a function of wind velocity can be retrieved in Ceplecha (1987). Finally, we performed several computations using the Monte Carlo method to take into account the measurement errors of all the initial parameters to obtain a ground map (strewn field) as a function of the final mass of the bolide.

Of course, owing to the various simplifying assumptions made, we can only underestimate the size of the strewn field. However, we can see that varying unknowns, such as the object density or the drag parameter, only cause the strewn field to slide along its center line. In the end, the main unknown is the width of that strewn field in the direction perpendicular to its center line, which can be several hundred meters up to 1 km.

Taking the example of the 1 January 2020 fall in Italy (Gardiol et al. 2020), our determined strewn field with a 99% confidence level consisted of a thin strip 5.6 km long and 100 m wide. The actual meteorite was found only 200 meters from the central line of this strip. This demonstrates the accuracy of our method, the offset being mainly due to our approximating the meteorite shape as a sphere.

thumbnail Fig. 5

Distribution of the duration (top) and length (bottom) of 3200 bright flights. The cutoff for short exposures (less than 0.5 s) is due to the acquisition software FreeTure (Audureau et al. 2014).

3 First results

3.1 Statistics and network efficiency

One of the main objectives of the FRIPON network is to measure the unbiased incoming flux of extraterrestrial matter. In this section, we first present the raw statistics of detected falls. Next, we attempt to constrain the absolute flux of incoming material.

3.1.1 Raw meteoroid detections

Figure 5 shows the histogram of duration and length of detected events. The average length of a meteor amounts to about 35 km and it lasts for about 0.8 s. Figure 6 shows the detection rate of the network between January 2016 and March 2020 as well as the average number of monthly clear night sky hours. Between 2016 and January 2019, we observed an increase in the number of detections that reflects the increasing number of installed cameras. Since January 2017, the annual number of detections appears to be fairly constant at around 1000 detections per year. Notably, the Perseid shower is the only shower standing out with regularity because of its high zenithal hourly rate (ZHR) and long duration. The shorter Geminid shower is less prominent (e.g., 2017 and 2018) because of greater cloud coverage. Weak meteor showers are not unambiguously detected in our data as a consequence of the photometric detection limitation of our cameras. As expected, our study shows a strong correlation between the monthly detection rate and the percentage of clear sky due to the local climate or to seasonal variations (or both, see Figs. 6 and C.1).

Figure 7 shows the radiants of 3200 fireballs detected since 2016 and Table 2 gives the number of detections for each shower per year. This figure presents that the main showers are detected and that the sporadic meteors are uniformly distributed over the celestial sphere except for part of the southern hemisphere, which is not at present within the reach of FRIPON. Overall, sporadic meteors represent 55% of the data.

Table 2

Number of meteors observed for the different meteor showers per year.

3.1.2 Quantifying the absolute meteoroid flux

An important goal of the FRIPON network is to estimate the absolute flux of incoming meteoroids. For this purpose, it is mandatory to measure the efficiency of the network in terms of meteoroid discovery.

To estimate that flux, we need an estimation of the cloud coverage, the percentage of operational stations, and the sensitivity of our network as a function of meteor brightness. Regarding that last point, Fig. 8 shows the absolutemagnitude histogram after three years of observations. Assuming a power-law size distribution for interplanetary matter (Brown et al. 2002), it appears that FRIPON is clearly not fully efficient for events fainter than −5 in magnitude. This detection threshold is similar to that of the Prairie network (Halliday et al. 1996) and implies, as for other networks (Devillepoix et al. 2020), a minimum detection size of ~1 cm for incoming meteoroids. We note that smaller objects can nevertheless be detected if their entry speed is high enough.

To calculate the efficiency of FRIPON, we only used the French stations as these were the first to be installed and France was fully covered in 2017. We considered its area, with a 120 km band added around it (Fig. C.1) for a total of 106 km2, which was the basis for the calculation. For ≥1 cm meteoroids (i.e., for magnitude <− 5 fireballs), we obtained an average rate of 250 events/yr/106 km2. Last, to estimate the incoming meteoroid flux for ≥1 cm bodies, we needed to correct for dead time (day time: 0.5 and average cloud cover: 0.4). The dead time-corrected meteoroid flux for ≥1 cm meteoroids is 1250/yr/106 km2, which is comparable to the 1500/yr/106 km2 value given by Halliday et al. (1996). Our determination is raw and requires that we carry out a more detailed analysis in the future with more data. Our analysis shows that the network has reached a complete efficiency for the French territory for meteoroids larger than 1 cm.

thumbnail Fig. 6

Detection statistics for the last 3 yr of operation. Of a total of 3700 trajectories computed: double 58%, triple 20%, quadruple 8%, and more than 4 simultaneous detections 14%. The number of detections (black bars) gradually increases as the installation of the stations progresses. The blue bars (orange for January) indicate the number of clear night sky hours each month, making it possible to also visualize the effect of cloud cover. The main meteor showers are listed at the top.
thumbnail Fig. 7

Fireball radiant in Sanson-Flamsteed projection of equatorial coordinates from January 2016 to December 2019. The color scale corresponds to the initial velocity of the objects: (1) low velocities (in blue) for asteroidal like objects, (2)high velocities (in yellow) for cometary-like objects. The main showers are detected. Of the objects, 55% are sporadic: their radiants cover the sky uniformly except for its southern part, which is invisible from European latitudes. The north toroidal sporadic source is visible in the top left corner and low speed objects are shown along the ecliptic plane coming from the anti-helion source.
thumbnail Fig. 8

Histogram of the absolute magnitude of all the events detected by the network showing that the exhaustive detection regime is only reached around mag − 5. The slope is compatible with that obtained by previous studies such as Brown et al. (2002), as shown on Fig. 11, which describes the distribution of interplanetary matter from 1 cm to 1 km. The global shape of the histogram is similar to that in Ott et al. (2014), which is shifted as CILBO cameras are more sensitive than FRIPON cameras.

3.1.3 Orbit precision

A precise determination of the orbit requires the extraction of a realistic initial velocity for the object. This can only be achieved by taking into account its deceleration in the upper atmosphere before the bright flight. Therefore our model of drag and ablation depends on three parameters (see Sect. 2.3.3): the initial velocity V, a drag coefficient A, and an ablation coefficient B. Depending on the quality of the data – for example, the number of cameras, weather conditions, and distance of the camera to the bolide – these three parameters do not have the same influence on the trajectory calculation and cannot be determined with the same accuracy. We classified the meteors in three categories:

  • 1.

    Those whose deceleration is hardly noticeable (A∕σA < 2), which represent 65% of all meteors.

  • 2.

    Those for which only the deceleration is noticeable (A∕σA > 2 and B∕σB < 2), which represent 21% of all meteors. In those cases, the ablation is not observed.

  • 3.

    Those for which both the deceleration and the ablation are noticeable (A∕σA > 2 and B∕σB > 2), which represent 14% of all meteors.

For dynamical studies, only the detections that fall in one of the last two categories (35% of all detections) can be used. The typical velocity accuracy is then 100 m s−1, which is required both for the identification of meteorite source regions in the solar system (Granvik & Brown 2018) and for the search for interstellar meteoroids (Hajduková et al. 2019).

thumbnail Fig. 9

Histogram of sporadic fireball entry velocities. Two populations can be observed: (1) low speed objects corresponding mostly to asteroidal orbits and (2) fast objects corresponding to TNOs or comet-like objects. This dichotomy has also been observed by Drolshagen et al. (2014) with the CILBO network for smaller objects.

3.2 Dynamical properties of the observed meteoroids

In the following, we restrict our analysis to sporadic meteors. The histogram of initial velocities is shown in Fig. 9. It reveals two populations of meteoroids whose entry velocities differ by about 50 km s−1, suggesting an asteroidal (55%) and a cometary (45%) population. This result can also be inferred from the histogram of meteoroid detections as a function of the inverse of the semimajor axis of their orbit (Fig. 10). This figure clearly shows a main belt population with semimajor axes between that of Mars and that of Jupiter, as well as a cometary population, possibly including Oort cloud material, with semimajor axes greater than that of Jupiter. Last, we note the presence of a few meteoroids with negative semimajor axes. However, rather than concluding that interstellar matter was detected, we attribute these events to large errors associated with the calculation of their initial velocity. As a matter of fact, these events have semimajor axes that differ significantly from that of the interstellar object 1I/Oumuamua.

It is clear that in more than three years of observation, FRIPON has not detected any interstellar object so far. This compares toresults obtained by other networks such as CMOR (Weryk & Brown 2004), who found that only 0.0008 % of the objects detected might be of interstellar origin; while a more recent work (Moorhead 2018) did not find interstellar candidate in CMOR data. In the case of the FRIPON network, only an upper limit of 0.1% can be given, but we expect the real value to be much lower. Hajduková et al. (2019) showed that no network so far has ever experienced a conclusive detection of an interstellar meteoroid. Most false detections are likely to stem from abad error estimation, especially that of the initial speed, which requires an estimation of the drag coefficient.

3.3 Meteorite falls and first field search

Based on Halliday et al. (1989), about ten meteorites weighing more than 100 g must fall each year over the area covered by the FRIPON network. Table 3 lists the events that produced a computed significant initial and/or final mass. The fall rate that we observe for final masses equal or greater than 100 g is 2.7 per year. This value is compatible with that of Halliday et al. (1996), once corrected to take into account the 20% overall efficiency of the FRIPON network (see above), as this yields a corrected rate of 14 falls per year. Among these events, only 1 led to the recovery of meteorite fragments. This event occurred near Cavezzo in Italy (Gardiol et al. 2020) and was detected by PRISMA cameras. Further details regarding the meteorite and its recovery will be presented in a forthcoming paper. This recovery is particularly important in showing that it is possible to find a 3 g stone thanks to the mobilization of the public with the help of various media (e.g., internet and newspapers). This strategy has worked well and can be reproduced for all comparatively small falls (typically a few dozen grams). In such cases, it is clear that the chances of finding the stone are low and do not warrant the organization of large searches, while an appeal to the general public may be fruitful. In the Cavezzo case, the meteorite was found on a path by a walker and his dog.

It is also possible to calculate the meteorite flux for objects with final masses greater than 10 g and compare this value with previous estimates found by Halliday et al. (1989) (81/yr/106 km2), Bland et al. (1996) (225/yr/106 km2), Drouard et al. (2019) (222/yr/106 km2), and Evatt et al. (2020) (149/yr/106 km2).

We chose to compute the flux of objects with final masses greater than 100 g for which the accuracy is moderate to high (m∕σm > 2). This flux is 14 meteorites/yr/106 km2 (see above). We extrapolated it down to a mass of 10 g, assuming a power-law distribution of the final masses of the meteorites (Huss 1990), and obtained a value of 94 meteorites/year/106 km2, close to the value from Halliday et al. (1989); this is also based on fireball data. This value is, however, lower than the other estimates (Bland et al. 1996 and Drouard et al. 2019), which are based on field searches. The Evatt et al. (2020) estimate based on the study of meteorites found in Antarctic blue ice gives a mid-range value that is consistent with all previous estimates.

thumbnail Fig. 10

Histogram of sporadic fireballs detected as a function of 1/a. This value is proportional to the orbital energy, making it possible to highlight two populations of objects: (1) the slow objects (of asteroidal origin) with a maximum related to the 3:1 and ν6 resonances (green line), which are the main sources of NEOs; and (2) the fast objects around Neptune (purple line). These two populations are separated by Jupiter (orange line). The figure also shows the orbits of the Earth (blue), Mars (red), and the interstellar object 1I/Oumuamua (black). The FRIPON orbits with negative (1/a) values suffer from large errors and certainly do not correspond to orbits of interstellar objects.
Table 3

2016-2020 events with significant computed initial or final masses with a m∕σm > 2.

4 Perspectives

4.1 Extension of the network

Significantly increasing the area covered by the network (by at least an order of magnitude) will be fundamental in increasing the recovery rate of meteorites, as this will lead to the detection, over a reasonable period, of a statistically significant number of very bright meteors that might be recovered on the ground as meteorites. Hence, there is a major interest in extending the FRIPON network over all of Europe and to other parts of the world. Such an extension has already begun (see Fig. 2) and will be pursued over the coming years. The development plan includes, as a priority, the densification of the European coverage as well as its extension to southern countries such as Morocco, Algeria, and Tunisia. For Spain, FRIPON is complemented by the SPMN network (Trigo-Rodríguez et al. 2004), with which we already collaborate for trans-national events and with whom we organized a search for a possible meteorite fall in January 2019. Such a southern extension would be sufficient to generate a network area about ten times larger than that of metropolitan France. In addition, the network is currently also being developed in Canada in North America and in Chile in South America. Figure 11 shows that 30 objects larger than one meter fall on Earth (510 × 106 km2) every year. Taking into account the current surface area of the FRIPON network, the average expected detection rate of such objects is limited to an average of one in ten years. Extending the area of the network is thus a necessity to reach an acceptable detection rate for 1 m objects. An extension to Europe and North Africa would make it reach a surface area of 6 × 106 km2, which is comparable to that of the Australian DFN network (Devillepoix et al. 2016), leading to a probability of a one-meter event approximately every years.

thumbnail Fig. 11

Flux of small near-Earth objects colliding with the Earth (Brown et al. 2002). Data are shown over a range of 14 magnitudes in energy. The statistical model is based on near-Earth population for big sizes and, for the smaller objects, it is derived from a decade-long survey of ground-based observations of meteor and fireballs. The FRIPON network lies exactly between minor planets (detected by telescopes and planetary impacts) and interplanetary dust (detected by meteor networks). The solid arrow corresponds to FRIPON nominal mode; the dashed line is for rare events, observable by FRIPONbut with a very low probability.

4.2 Software

The reduction pipeline is operational and only requires minor improvements. The acquisition software FreeTure still shows a surprisingly high false detection rate, which requires that daylight observations are turned off at the moment. A new version using deep learning techniques is being developed so that daytime observations will become possible. The development of a tool to compute the light curve of heavily saturated events (Anghel et al. 2019b) is also planned.

4.3 Hardware

The hardware currently in use in the network corresponds to pre-2014 technology. A complete hardware update after five years of utilization is thus desirable to improve the temporal resolution of the light curves and the performance and flexibility of the acquisition computers. A non-exhaustive list of improvements includes upgrading from CCD to CMOS detectors and switching the current PCs to Raspberry Pi4 single board computers (SBCs).

In addition, a prototype of an all-sky radiometer is presently under development (Rault & Colas 2019), to resolve the saturation issue and improve on the bandwidth of the cameras. This radiometer covers the visible and near-infrared wavelengths. It is based on a 16 PIN photodiode matrix, followed by a trans-impedance amplification chain and a 14 bit industrial USB data acquisition module, which samples at a rate of 20 kHz. As an example, we superimposed on Fig. 12 the FRIPON camera lightcurve for an event of magnitude -9.5, which occurred on 14 August 2019 at 03:07:02 UTC and the corresponding high data rate radiometer light curve.

thumbnail Fig. 12

Raw light flux from a bolide observed on 14 August 2019 at 03h07m02s UTC. Red triangles: Dijon FRIPON camera data(30 Hz, 12bits). Blue squares: radiometer prototype data (20 kHz, 14 bits). The faster acquisition rate and the higher amplitude dynamic range of the radiometer allows more detailed observations of the meteor fragmentation and of high speed luminosity variations.

4.4 Radio

The aim of FRIPON radio receivers is an accurate measurement of meteor velocities through the Doppler effect, allowing a much better determination of the orbital data (especially semimajor axes). In Table 4, we present the value of the initial velocity and effective surface-to-mass ratio derived for a meteor observed on 15 October 2018 at 1:15 UTC by five cameras. The accuracy achieved with the radio data leads to errors one order of magnitude lower compared to that achieved with only the visible images. However, it seems at present that only about 30 % of the optical detections lead to a detectable radio signal and that several bright radio events do not have any visible counterpart. For this reason, radio data have not been widely used yet, and further work is needed to improve our understanding of the complex phenomena associated with the generation of radio echoes by the plasma surrounding the meteors. Over time, we came to the conclusion that detailed information on the fragmentation and final destruction of bolides might also be obtained thanks to the head echoes produced by the GRAVES HPLA radar. Last, we sometimes detected unexpected oscillations on the usually smooth Doppler shift curves (Rault et al. 2018), which indicates cyclic fluctuations on the radial positions of the radar cross section (RCS) of the plasma envelope surrounding the meteor bodies (see Fig. 13).

4.5 Cross-reference data with infrasound network

In recent years, infrasound has become an efficient technique, allowing for global detection of explosive sources in the atmosphere, and by extension of meteoroid atmospheric entries. There is an ongoing effort to improve the identification of valid signals and optimize the detection threshold for the International Monitoring System (IMS) developed to enforce the Comprehensive Nuclear-Test-Ban Treaty (CTBT; Marty 2019). Studies have determined that the IMS system, completed by experimental infrasound networks, is able to identify approximately 25% of fireballs with E > 100 t (TNT equivalent) energy and can provide key ground-based confirmation of the impact (timing and geo-location). This is particularly significant, as most impacts occur over the ocean, where no other instruments are likely to record the bolides (Silber & Brown 2018).

It is expected that infrasonic observations of NEOs that regularly impact the Earth atmosphere will increase as the number of stations are deployed worldwide. Combining infrasound with optical observations, such as those collected by the dense network of cameras operated by FRIPON, would contribute to fill gaps in existing observation systems and help constrainingsource parameters, such as trajectory and energy deposition. The results become even more interesting in Europe, where the integration of national networks allows for a better characterization of smaller-energy events (Ott et al. 2019). The Atmospheric dynamics Research InfraStructure in Europe (ARISE) supports such multidisciplinary approaches by providing an extensive infrasound database for the estimation of NEOs potential risk and societal impact.

Table 4

Improvement of orbit precision by addition of radio data: example of the fireball of October 15, 2018.

thumbnail Fig. 13

Cyclic Doppler fluctuations on radio echo of the bolide observed on 8 August 2018 at 02h25m UTC, as seen by the Sutrieu radio receiver. Initial speed was 25.8 km s−1.

4.6 Other records

An extensive database of images covering a large area may be used for additional purposes. The study of transient luminous events (TLEs), such as sprites or spatial debris re-entries, may be cited as examples (Cecconi et al. 2018). Since the Summer of 2017, the software FreeTure contains an experimental real-time algorithm for the detection of TLEs. This algorithm runs along with the meteor detection part on selected stations with a view to help localize TLEs observed by the future CNES space mission TARANIS (Blanc et al. 2017). The FRIPON network infrastructure can also be used to conduct large-scale light pollution monitoring campaigns using the all-sky calibration images collected over time (Jechow et al. 2018).

4.7 Observation from space

The network can also be extended vertically by combining space measurements with ground measurements. Space-borne observations have several advantages, such as providing a wide geographical coverage with one camera, longer recording times, and no weather constraints. The small satellite sector is evolving very quickly, opening up new opportunities for scientific missions (Millan et al. 2019). In particular, relatively inexpensive missions make it possible to design swarms of satellites or even constellations dedicated to monitoring the Earth and therefore meteors. In this framework, a Universitary Cubesat demonstrator called Meteorix is under study (Rambaux et al. 2019). The Meteorix mission is dedicated to the observation and characterization of meteors and space debris entering the Earth’s atmosphere. The orbit chosen for Meteorix is a low Earth sun-synchronous orbit at an altitude of 500 km. Such configuration will make it possible to detect on average a sporadic meteoroid entry per day and about 20 meteors during a major meteor shower. The nominal mission lifetime is one year. Three-dimensional astrometry and photometry would become possible in case of a detection over the FRIPON network.

5 Conclusion

The FRIPON scientific network, originally developed to cover the French territory, is now a fully automated network monitoring fireballs above part of western Europe and a small fraction of Canada. As of today, it consists of 150 cameras and 25 radio receivers covering an area of about 1.5 × 106 km2. The level of automation of the network is such that a recovery campaign can be triggered only a few hours after a meteorite reached the surface of the Earth. The FRIPON scientific project has been monitoring meteoroid entries in western Europe since 2016, thereby allowing the characterization of the dynamical and physical properties of nearly 4000 meteoroids. It has thus allowed us to significantly enhance the statistics of orbital parameters of meteoroids, while also searching for possible interstellar meteoroids. The FRIPON observations show that the distribution of the orbits of incoming bolides appears bimodal, comprising a cometary population and a main belt population. Sporadic meteors amount to about 55% of all meteoroids. In addition, we found no evidence for the presence of interstellar meteoroids in our sample. Overall, it appears that the range of sensitivity of the FRIPON network encompasses particles originating both from cometsand asteroids. A first estimate of the absolute flux of meteoroids bigger than 1 cm amounts to 1250 /yr/106 km2, which is a value compatible with previous reports. We also estimate the flux of meteorites heavier than 100 g to 14/yr/106 km2, which is a value compatible with data from other fireball networks but lower than those obtained from collecting meteorites. Finally, the first meteorite has been recovered in Italy following observations by the PRISMA network, a component of the FRIPON network.

Further extension of the FRIPON network is under way. In the coming years, it will be extended to North and West Africa as well as Canada and to the southern hemisphere in South America and South Africa. The goal is to reach a size large enough to allow the recovery of at least one fresh meteorite per year. In addition to the geographical extension of the network, technical developments will be conducted to improve the photometry of saturated images. Moreover, we plan to implement new algorithms in the detection software, so that daytime observations become possible and useful. Finally, we plan to fully exploit the radio network, both to improve current orbits and to reach a better understanding of the physical mechanism of meteoroid entries.

Acknowledgements

FRIPON was initiated by funding from ANR (grant N.13-BS05-0009-03), carried by the Paris Observatory, Muséum National d’Histoire Naturelle, Paris-Saclay University and Institut Pythéas (LAM-CEREGE). Vigie-Ciel was part of the 65 Millions d’Observateurs project, carried by the Muséum National d’Histoire Naturelle and funded by the French Investissements d’Avenir program. FRIPON data are hosted and processed at Institut Pythéas SIP (Service Informatique Pythéas), and a mirror is hosted at IMCCE (Institut de Mécanique Céleste et de Calcul des Éphémérides / Paris Observatory) with the help of IDOC (https://idoc.ias.u-psud.fr) (Integrated Data and Operation Center), supported by CNRS and CNES. PRISMA is the Italian Network for Systematic surveillance of Meteors and Atmosphere. It is a collaboration initiated and coordinated by the Italian National Institute for Astrophysics (INAF) that counts members among research institutes, associations and schools (http://www.prisma.inaf.it). PRISMA was partially funded by 2016 and 2020 Research and Education grants from Fondazione Cassa di Risparmio di Torino and by a 2016 grant from Fondazione Agostino De Mari (Savona). FRIPON-Bilbao is supported by a grant from Diputacion Foral Bizkaia (DFB/BFA). FRIPON-MOROI was supported by a grant of the Romanian Ministery of Research and Innovation, CCCDI - UEFISCDI, project number PN-III-P1-1.2-PCCDI-2017-0226/16PCCDI/2018, within PNCDI III. Rio de Janeiro camera is hosted and partially maintained by MAST (Museum of Astronomy and Related Sciences)/MCTIC. The Meteorix project acknowledges supports from labex ESEP (Exploration Spatiale des Environnements Planétaires), DIM-ACAV+ Région Île-de-France, Janus CNES, IDEX Sorbonne Universités and Sorbonne Université. We thank Maria Gritsevich for comments which have been very helpful in improving the manuscript.

Appendix A Countries and observation stations involved in FRIPON

Table A.1

Algeria.

Table A.2

Australia.

Table A.3

Austria.

Table A.4

Belgium.

Table A.5

Brazil.

Table A.6

DOME - Canada.

Table A.7

Chile.

Table A.8

Denmark.

Table A.9

Vigie-Ciel - France.

Table A.10

Germany.

Table A.11

PRISMA - Italia.

Table A.12

Mexico.

Table A.13

Morocco.

Table A.14

Netherlands.

Table A.15

MOROI - Romania.

Table A.16

Peru.

Table A.17

Senegal.

Table A.18

South Africa.

Table A.19

SPMN - Spain.

Table A.20

Switzerland.

Table A.21

Tunisia.

Table A.22

SCAMP - UK.

Appendix B FRIPON radio hardware description

FRIPON radio setup (Rault et al. 2014) is a multi-static radar consisting of 25 distant receivers and a high power large aperture (HPLA) radar. Thanks to its omni-directional reception antenna, each single radio station is able to receive scattered GRAVES echoes from a meteor, from its ionized trail and/or from the plasma surrounding the meteor body.

A typical FRIPON radio setup consists of

  • a 2.5 m long vertical ground-plane antenna ref. COMET GP-5N connected to the radio receiver via a 50Ω coaxial cable model KX4;

  • a general purpose Software Defined Radio (SDR) ref. FUNcube Dongle Pro + (Abbey 2013).

The ground-plane antenna radiation pattern is omni-directional in the horizontal plane, allowing both back and forward meteor scatter modes. The gain of this vertically polarized antenna is around 6 dBi. The FUNcube SDR is connected to one of the USB ports of the station and the I∕Q data produced bythe radio are recorded 24 h a day on the local computer hard disk. The SDR is a general coverage receiver (Fig. B.1), whose main characteristics are as follows:

  • frequency range 150 kHz to 240 MHz and 420 MHz to 1.9 GHz;

  • sensitivity: typically 12 dB SINAD NBFM for 0.15 μV at 145 MHz;

  • reference oscillator stability: 1.5 ppm;

  • sampling rate: 192 kHz;

  • bit depth: 16 bits (32 bits used internally).

A low noise amplifier (LNA) and a surface acoustic wave (SAW) filter fitted in the front end of each receiver offer an adequate sensitivity and selectivity for the meteor echoes.

thumbnail Fig. B.1

Diagram of the FUNcube (Abbey 2013) Software Defined Radio.

Appendix C Map of FRIPON meteor trajectories

thumbnail Fig. C.1

Map of the 3700 trajectories measured with FRIPON data from 2016 to early 2020. The concentration of detections is in part explained by the background sunshine weather map (sunshine duration in hours per year). The Rhône valley and the south of France have twice as many clear nights as the north. Another factor is that the installation of the cameras, done mostly throughout 2016, started in southern France and around Paris.

References

  1. Abbey, T. 2013, J. Br. Astron. Assoc., 123, 169 [Google Scholar]
  2. Anghel, S., Birlan, M., Nedelcu, D. A., & Boaca, I. 2019a, Rom. Astron. J., 29, 189 [Google Scholar]
  3. Anghel, S., Birlan, M., Nedelcu, D.-A., & Boaca, I. 2019b, EPSC-DPS Joint Meeting 2019, Vol. 2019, EPSC–DPS2019–1758 [Google Scholar]
  4. Audureau, Y., Marmo, C., Bouley, S., et al. 2014, Proceedings of the International Meteor Conference, Giron, France, 18-21 September 2014, ed. J.-L. Rault & P. Roggemans, 39 [Google Scholar]
  5. Barghini, D., Gardiol, D., Carbognani, A., & Mancuso, S. 2019, A&A, 626, A105 [EDP Sciences] [Google Scholar]
  6. Barry, M. A. T., Gault, D., Bolt, G., et al. 2015, PASA, 32, e014 [Google Scholar]
  7. Bessell, M. S. 2000, PASP, 112, 961 [Google Scholar]
  8. Bischoff, A., Barrat, J.-A., Bauer, K., et al. 2017, Meteorit. Planet. Sci., 52, 1683 [Google Scholar]
  9. Bischoff, A., Barrat, J.-A., Berndt, J., et al. 2019, Geochemistry, 79, 125525 [Google Scholar]
  10. Blanc, E., Farges, T., Jehl, A., et al. 2017, SPIE Conf. Ser., 10564, 1056404 [Google Scholar]
  11. Bland, P. A., Smith, T. B., Jull, A. J. T., et al. 1996, MNRAS, 283, 551 [Google Scholar]
  12. Bland, P. A., Spurný, P., Bevan, A. W. R., et al. 2012, Aust. J. Earth Sci., 59, 177 [Google Scholar]
  13. Bland, P. A., Towner, M. C., Sansom, E. K., et al. 2016, Ann. Meeting Meteorit. Soc., 79, 6265 [Google Scholar]
  14. Borovicka, J., Spurný, P., Kalenda, P., & Tagliaferri, E. 2003, Meteorit. Planet. Sci., 38, 975 [Google Scholar]
  15. Borovička, J., Spurný, P., Brown, P., et al. 2013a, Nature, 503, 235 [NASA ADS] [Google Scholar]
  16. Borovička, J., Tóth, J., Igaz, A., et al. 2013b, Meteorit. Planet. Sci., 48, 1757 [Google Scholar]
  17. Borovička, J., Spurný, P., Šegon, D., et al. 2015, Meteorit. Planet. Sci., 50, 1244 [Google Scholar]
  18. Bouquet, A., Baratoux, D., Vaubaillon, J., et al. 2014, Planet. Space Sci., 103, 238 [Google Scholar]
  19. Bradley, J. P., Keller, L. P., Brownlee, D. E., & Thomas, K. L. 1996, Meteorit. Planet. Sci., 31, 394 [Google Scholar]
  20. Bronshten, V. A. 1983, Physics of Meteoric Phenomena (Dordrecht: D.Reidel Publishing Company) [Google Scholar]
  21. Brown, P., Ceplecha, Z., Hawkes, R. L., et al. 1994, Nature, 367, 624 [Google Scholar]
  22. Brown, P., Hildebrand, A. R., Green, D. W. E., et al. 1996, Meteorit. Planet. Sci., 31, 502 [Google Scholar]
  23. Brown, P. G., Hildebrand, A. R., Zolensky, M. E., et al. 2000, Science, 290, 320 [Google Scholar]
  24. Brown, P., Spalding, R. E., ReVelle, D. O., Tagliaferri, E., & Worden, S. P. 2002, Nature, 420, 294 [Google Scholar]
  25. Brown, P., McCausland, P. J. A., Fries, M., et al. 2011, Meteorit. Planet. Sci., 46, 339 [Google Scholar]
  26. Brown, P. G., Vida, D., Moser, D. E., et al. 2019, Meteorit. Planet. Sci., 54, 2027 [Google Scholar]
  27. Carbognani, A., Barghini, D., Gardiol, D., et al. 2020, Eur. Phys. J. Plus, 135, 255 [Google Scholar]
  28. Cecconi, B., Le Sidaner, P., Tomasik, L., et al. 2018, ArXiv e-prints [arXiv:1811.12680] [Google Scholar]
  29. Ceplecha, Z. 1960, Bull. Astron. Inst. Czechosl., 11, 164 [Google Scholar]
  30. Ceplecha, Z. 1987, Bull. Astron. Inst. Czechosl., 38, 222 [NASA ADS] [Google Scholar]
  31. Chodas, P., Chesley, S., & Yeomans, D. 2010, AAS/Division of Dynamical Astronomy Meeting, 41, 6.03 [Google Scholar]
  32. Colas, F., Zanda, B., Bouley, S., et al. 2015, European Planetary Science Congress 2015, held 27 September–2 October, 2015 in Nantes, France, id.EPSC2015-604, 10, EPSC2015 [Google Scholar]
  33. de la Fuente Marcos, C., & de la Fuente Marcos, R. 2018, Res. AAS, 2, 57 [Google Scholar]
  34. Devillepoix, H. A. R., Bland, P. A., Towner, M. C., et al. 2016, in International Meteor Conference Egmond, the Netherlands, 2-5 June 2016, ed. A. Roggemans & P. Roggemans, 60 [Google Scholar]
  35. Devillepoix, H. A. R., Sansom, E. K., Bland, P. A., et al. 2018, Meteorit. Planet. Sci., 53, 2212 [Google Scholar]
  36. Devillepoix, H. A. R., Cupák, M., Bland, P. A., et al. 2020, Planet. Space Sci., 191, 105036 [Google Scholar]
  37. Drolshagen, E., Ott, T., Koschny, D., Drolshagen, G., & Poppe, B. 2014, Proceedingsof the International Meteor Conference, Giron, France, 18-21 September 2014, ed. J. L. Rault & P. Roggemans, 16 [Google Scholar]
  38. Drouard, A., Gattacceca, J., Hutzler, A., et al. 2019, Geology, 47, 673 [Google Scholar]
  39. Duprat, J., Engrand, C., Maurette, M., et al. 2007, Adv. Space Res., 39, 605 [Google Scholar]
  40. Dyl, K. A., Benedix, G. K., Bland, P. A., et al. 2016, Meteorit. Planet. Sci., 51, 596 [Google Scholar]
  41. Evatt,G., Smedley, A., Joy, K., et al. 2020, Geology https://pubs.geoscienceworld.org/geology/article-pdf/doi/10.1130/G46733.1/4988172/g46733.pdf [Google Scholar]
  42. Fry, C., Melanson, D., Samson, C., et al. 2013, Meteorit. Planet. Sci., 48, 1060 [Google Scholar]
  43. Gardiol,D., Cellino, A., & Di Martino M. 2016, in International Meteor Conference Egmond, the Netherlands, 2-5 June 2016, ed. A. Roggemans & P. Roggemans, 76 [Google Scholar]
  44. Gardiol, D., Barghini, D., Buzzoni, A., et al. 2020, MNRAS, in press, https://doi.org/10.1093/mnras/staa3646 [Google Scholar]
  45. Granvik, M., & Brown, P. 2018, Icarus, 311, 271 [Google Scholar]
  46. Gritsevich, M., Lyytinen, E., Moilanen, J., et al. 2014, in Proceedings of the International Meteor Conference, Giron, France, 18-21 September 2014, eds. J. L. Rault, & P. Roggemans, 162–169 [Google Scholar]
  47. Gritsevich, M., Lyytinen, E., Hankey, M., et al. 2017, in European Planetary Science Congress, EPSC2017–995 [Google Scholar]
  48. Haack, H., Michelsen, R., Stober, G., Keuer, D., & Singer, W. 2010, Meteorit. Planet. Sci. Suppl., 73, 5085 [NASA ADS] [Google Scholar]
  49. Hajduková, M. J., Sterken, V., & Wiegert, P. 2019, Meteoroids: Sources of Meteors on Earth and Beyond, eds. G. O. Ryabova, D. J. Asher, & M. J. Campbell-Brown (Cambridge: Cambridge University Press), 235 [Google Scholar]
  50. Halliday, I., Blackwell, A. T., & Griffin, A. A. 1978, JRASC, 72, 15 [Google Scholar]
  51. Halliday, I., Griffin, A. A., & Blackwell, A. T. 1981, Meteoritics, 16, 153 [Google Scholar]
  52. Halliday, I., Blackwell, A. T., & Griffin, A. A. 1989, Meteoritics, 24, 173 [Google Scholar]
  53. Halliday, I., Griffin, A. A., & Blackwell, A. T. 1996, Meteorit. Planet. Sci., 31, 185 [Google Scholar]
  54. Howie, R. M., Paxman, J., Bland, P. A., et al. 2017, Exp. Astron., 43, 237 [Google Scholar]
  55. Huss, G. R. 1990, Meteoritics, 25, 41 [Google Scholar]
  56. Hutzler, A., Gattacceca, J., Rochette, P., et al. 2016, Meteorit. Planet. Sci., 51, 468 [Google Scholar]
  57. Jeanne, S. 2020, PhD thesis, Ecole doctorale d’Ile de France, France [Google Scholar]
  58. Jeanne, S., Colas, F., Zanda, B., et al. 2019, A&A, 627, A78 [EDP Sciences] [Google Scholar]
  59. Jechow, A., Ribas, S. J., Domingo, R. C., et al. 2018, J Quant. Spectr. Rad. Transf., 209, 212 [Google Scholar]
  60. Jenniskens, P., Fries, M. D., Yin, Q.-Z., et al. 2012, Science, 338, 1583 [Google Scholar]
  61. Jenniskens, P., Rubin, A. E., Yin, Q.-Z., et al. 2014, Meteorit. Planet. Sci., 49, 1388 [Google Scholar]
  62. Jenniskens, P., Utas, J., Yin, Q.-Z., et al. 2019, Meteorit. Planet. Sci., 54, 699 [Google Scholar]
  63. Jenniskens, P., Moskovitz, N., Garvie, L. A. J., et al. 2020, Meteorit. Planet. Sci., 55, 535 [Google Scholar]
  64. Knapic, C., De Marco, M., Smareglia, R., & Molinaro, M. 2014, ASP Conf. Ser., 485, 131 [Google Scholar]
  65. Kokhirova, G. I., Babadzhanov, P. B., & Khamroev, U. K. 2015, Sol. Syst. Res., 49, 275 [Google Scholar]
  66. Kruijer, T. S., & Kleine, T. 2019, Geochim. Cosmochim. Acta, 262, 92 [Google Scholar]
  67. Llorca, J., Casanova, I., Trigo-Rodríguez, J. M., et al. 2009, Meteorit. Planet. Sci., 44, 159 [Google Scholar]
  68. Lyytinen, E., & Gritsevich, M. 2016, Planet. Space Sci., 120, 35 [Google Scholar]
  69. Maksimova, A. A., Petrova, E. V., Chukin, A. V., et al. 2020, Meteorit. Planet. Sci., 55, 231 [Google Scholar]
  70. Marty, J. 2019, The IMS Infrasound Network: Current Status and Technological Developments, eds. A. Le Pichon, E. Blanc, & A. Hauchecorne (Cham: Springer International Publishing), 3 [Google Scholar]
  71. McCrosky, R. E., & Boeschenstein, Jr. H. 1965, SAO Special Report, 173 [Google Scholar]
  72. McCrosky, R. E., Posen, A., Schwartz, G., & Shao, C.-Y. 1971, J. Geophys. Res., 76, 4090 [Google Scholar]
  73. Michal, T., Eglizeaud, J. P., & Bouchard, J. 2005, ESA SP, 587, 61 [Google Scholar]
  74. Millan, R. M., von Steiger, R., Ariel, M., et al. 2019, Adv. Space Res., 64, 1466 [Google Scholar]
  75. Moorhead, A. V. 2018, Meteorit. Planet. Sci., 53, 1292 [Google Scholar]
  76. Moorhead, A. V., Kingery, A., & Ehlert, S. 2020, J. Spacecraft Rockets, 57, 160 [Google Scholar]
  77. Nedelcu, D. A., Birlan, M., Turcu, V., et al. 2018, Romanian Astron. J., 28, 57 [NASA ADS] [Google Scholar]
  78. Neveu, M., & Vernazza, P. 2019, ApJ, 875, 30 [Google Scholar]
  79. Oberst, J., Molau, S., Heinlein, D., et al. 1998, Meteorit. Planet. Sci., 33, 49 [Google Scholar]
  80. Ott, T., Drolshagen, E., Koschny, D., Drolshagen, G., & Poppe, B. 2014, in Proceedings of the International Meteor Conference, Giron, France, 18-21 September 2014, eds. J. L. Rault & P. Roggemans, 23 [Google Scholar]
  81. Ott, T., Drolshagen, E., Koschny, D., et al. 2019, Planet. Space Sci., 179, 104715 [Google Scholar]
  82. Rambaux, N., Vaubaillon, J., Lacassagne, L., et al. 2019, in ESA NEO and Debris Detection Conference- Exploiting Synergies -ESA/ESOC, Darmstadt, Germany [Google Scholar]
  83. Rault, J.-L., & Colas, F. 2019, ArXiv e-prints, [arXiv:1911.04290] [Google Scholar]
  84. Rault, J.-L., Colas, F., & Vaubaillon, J. 2014, in Proceedings of the International Meteor Conference, Giron, France, 18-21 September 2014, eds. J.-L. Rault & P. Roggemans, 185 [Google Scholar]
  85. Rault, J.-L., Birlan, M., Blanpain, C., et al. 2018, Proc. Int. Meteor Conf., Petnica, Serbia, 21-24 September, 2017 (eds.: Gyssens, M., & Rault, J.-L.) International Meteor Organization, 103 [Google Scholar]
  86. Rojas, J., Duprat, J., Engrand, C., et al. 2019, Lunar Planet. Sci. Conf., 1968 [Google Scholar]
  87. Sansom, E. K., Gritsevich, M., Devillepoix, H. A. R., et al. 2019a, ApJ, 885, 115 [Google Scholar]
  88. Sansom, E. K., Jansen-Sturgeon, T., Rutten, M. G., et al. 2019b, Icarus, 321, 388 [Google Scholar]
  89. Sansom, E. K., Bland, P. A., Towner, M. C., et al. 2020, Meteorit. Planet. Sci., 55, 2157 [Google Scholar]
  90. Silber, E., & Brown, P. 2018, Infrasound Monitoring as a Tool to Characterize Impacting Near-Earth Objects (NEOs), 939 [Google Scholar]
  91. Simon, S. B., Grossman, L., Clayton, R. N., et al. 2004, Meteorit. Planet. Sci., 39, 625 [Google Scholar]
  92. Spurný, P., Oberst, J., & Heinlein, D. 2003, Nature, 423, 151 [Google Scholar]
  93. Spurný, P., Borovička, J., Kac, J., et al. 2010, Meteorit. Planet. Sci., 45, 1392 [Google Scholar]
  94. Spurný, P., Bland, P. A., Shrbený, L. ¡., et al. 2012, Meteorit. Planet. Sci., 47, 163 [Google Scholar]
  95. Spurný, P., Haloda, J., Borovička, J., Shrbený, L., & Halodová, P. 2014, A&A, 570, A39 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  96. Spurný, P., Borovička, J., Baumgarten, G., et al. 2017, Planet. Space Sci., 143, 192 [Google Scholar]
  97. Spurný, P., Borovička, J., & Shrbený, L. ¡. 2020, Meteorit. Planet. Sci., 55, 376 [Google Scholar]
  98. Trigo-Rodríguez, J. M., Castro-Tirado, A. J., Llorca, J., et al. 2004, Earth Moon Planets, 95, 553 [Google Scholar]
  99. Trigo-Rodríguez, J. M., Borovička, J., Spurný, P., et al. 2006, Meteorit. Planet. Sci., 41, 505 [Google Scholar]
  100. Trigo-Rodríguez, J. M., Llorca, J., Rubin, A. E., et al. 2009, Meteorit. Planet. Sci., 44, 211 [Google Scholar]
  101. Trigo-Rodríguez, J. M., Lyytinen, E., Gritsevich, M., et al. 2015, MNRAS, 449, 2119 [Google Scholar]
  102. Turchak, L. I., & Gritsevich, M. I. 2014, J. Theor. Appl. Mech., 44, 15 [Google Scholar]
  103. Vernazza, P., Marsset, M., Beck, P., et al. 2015, ApJ, 806, 204 [Google Scholar]
  104. Weryk, R. J., & Brown, P. 2004, Earth Moon Planets, 95, 221 [Google Scholar]
  105. Whipple, F. L. 1938, Proc. Am. Phil. Soc., 79, 499 [Google Scholar]
  106. Zolensky, M., Bland, P., Brown, P., & Halliday, I. 2006, Meteorites and the Early Solar System II, eds. D. S. Lauretta & H. Y. McSween (Tucson, AZ: University of Arizona Press), 869 [Google Scholar]
2

Discovered from observations by the PRISMA network, a component of the FRIPON network.

3

FRIPON-France is also known as FRIPON-Vigie-Ciel, in order to bring to the fore its citizen science component in France.

5

For example, PRISMA data are also stored at the INAF IA2 (Italian Center for Astronomical Archives) facilities in Trieste (Knapic et al. 2014) and processed by an independent pipeline (Barghini et al. 2019, Carbognani et al. 2020).

6

Shelyak Instruments, www.shelyak.com

All Tables

Table 1

Thirty-eight known meteorites with reliable orbit reference discovered by networks (“N”), visual observations (“V”) or telescopic observations (“T”).

In the text
Table 2

Number of meteors observed for the different meteor showers per year.

In the text
Table 3

2016-2020 events with significant computed initial or final masses with a m∕σm > 2.

In the text
Table 4

Improvement of orbit precision by addition of radio data: example of the fireball of October 15, 2018.

In the text
Table A.1

Algeria.

In the text
Table A.2

Australia.

In the text
Table A.3

Austria.

In the text
Table A.4

Belgium.

In the text
Table A.5

Brazil.

In the text
Table A.6

DOME - Canada.

In the text
Table A.7

Chile.

In the text
Table A.8

Denmark.

In the text
Table A.9

Vigie-Ciel - France.

In the text
Table A.10

Germany.

In the text
Table A.11

PRISMA - Italia.

In the text
Table A.12

Mexico.

In the text
Table A.13

Morocco.

In the text
Table A.14

Netherlands.

In the text
Table A.15

MOROI - Romania.

In the text
Table A.16

Peru.

In the text
Table A.17

Senegal.

In the text
Table A.18

South Africa.

In the text
Table A.19

SPMN - Spain.

In the text
Table A.20

Switzerland.

In the text
Table A.21

Tunisia.

In the text
Table A.22

SCAMP - UK.

In the text

All Figures

thumbnail Fig. 1

Distribution of observed meteorite falls in France. In nineteenth century France, 45 meteorites were recovered after their fall was observed, a number that fell by a factor of 5 in the twentieth century. Even in the nineteenth century, witnessed falls were not randomly distributed. They were mostly located in the great river plains (Seine and Loire in the northwest, Garonne in the southwest, and Rhône valley in the southeast). In these regions, the population was denser, the view is free of obstacles (such as mountains), and the skies are often clear. The striking difference between the two centuries illustrates the need for distributed observers for meteorite recovery. Rural populations have declined because of urbanization in the twentieth century. A camera network such as FRIPON can monitor atmospheric entries and take over that role that was previously played by human observers. However, trained human eyes are still required to recover the meteorites; this is the aim of the Vigie-Ciel citizen science program (Colas et al. 2015).
In the text
thumbnail Fig. 2

FRIPON network map as of end 2019. The color code is the following: 1. Blue: FRIPON-France, optical stations. 2. Red: coupled optical camera and radio receiver stations. 3. Black: stations under development. 4. Green: PRISMA (Italy). 5. Light Orange: MOROI (Romania). 6. Yellow: FRIPON-Belgium/Neterlands/Germany/Denmark. 7. Gray: SCAMP (United Kingdom). 8. Dark blue: DOME (Canada). 9. Dark Orange: SPMN (Spain). 10. Pink: GRAVES radar.
In the text
thumbnail Fig. 3

Mosaic of technology developed for the FRIPON network: (a) Final design of optical detectors2. (b) Core device comprising a GigaBit Ethernet camera and fish-eye optics. (c) FRIPON optical camera installed on the platform of Pic du Midi Observatory (2876 m altitude), in use during harsh weather conditions.
In the text
thumbnail Fig. 4

Top: event on 27 February 2019 seen by the Beaumont-lès-Valence FRIPON camera. Bottom: absolute magnitude light curves of the event as seen by 15 cameras; the red curve is Beaumont-lès-Valence. It is clear that the saturation limit is around magnitude –8 (all the other light curves fall above this limit). Cameras located further away may be able to measure more non-saturated data, but all the cameras become heavily saturated as the bolide reaches its maximum luminosity.
In the text
thumbnail Fig. 5

Distribution of the duration (top) and length (bottom) of 3200 bright flights. The cutoff for short exposures (less than 0.5 s) is due to the acquisition software FreeTure (Audureau et al. 2014).
In the text
thumbnail Fig. 6

Detection statistics for the last 3 yr of operation. Of a total of 3700 trajectories computed: double 58%, triple 20%, quadruple 8%, and more than 4 simultaneous detections 14%. The number of detections (black bars) gradually increases as the installation of the stations progresses. The blue bars (orange for January) indicate the number of clear night sky hours each month, making it possible to also visualize the effect of cloud cover. The main meteor showers are listed at the top.
In the text
thumbnail Fig. 7

Fireball radiant in Sanson-Flamsteed projection of equatorial coordinates from January 2016 to December 2019. The color scale corresponds to the initial velocity of the objects: (1) low velocities (in blue) for asteroidal like objects, (2)high velocities (in yellow) for cometary-like objects. The main showers are detected. Of the objects, 55% are sporadic: their radiants cover the sky uniformly except for its southern part, which is invisible from European latitudes. The north toroidal sporadic source is visible in the top left corner and low speed objects are shown along the ecliptic plane coming from the anti-helion source.
In the text
thumbnail Fig. 8

Histogram of the absolute magnitude of all the events detected by the network showing that the exhaustive detection regime is only reached around mag − 5. The slope is compatible with that obtained by previous studies such as Brown et al. (2002), as shown on Fig. 11, which describes the distribution of interplanetary matter from 1 cm to 1 km. The global shape of the histogram is similar to that in Ott et al. (2014), which is shifted as CILBO cameras are more sensitive than FRIPON cameras.
In the text
thumbnail Fig. 9

Histogram of sporadic fireball entry velocities. Two populations can be observed: (1) low speed objects corresponding mostly to asteroidal orbits and (2) fast objects corresponding to TNOs or comet-like objects. This dichotomy has also been observed by Drolshagen et al. (2014) with the CILBO network for smaller objects.
In the text
thumbnail Fig. 10

Histogram of sporadic fireballs detected as a function of 1/a. This value is proportional to the orbital energy, making it possible to highlight two populations of objects: (1) the slow objects (of asteroidal origin) with a maximum related to the 3:1 and ν6 resonances (green line), which are the main sources of NEOs; and (2) the fast objects around Neptune (purple line). These two populations are separated by Jupiter (orange line). The figure also shows the orbits of the Earth (blue), Mars (red), and the interstellar object 1I/Oumuamua (black). The FRIPON orbits with negative (1/a) values suffer from large errors and certainly do not correspond to orbits of interstellar objects.
In the text
thumbnail Fig. 11

Flux of small near-Earth objects colliding with the Earth (Brown et al. 2002). Data are shown over a range of 14 magnitudes in energy. The statistical model is based on near-Earth population for big sizes and, for the smaller objects, it is derived from a decade-long survey of ground-based observations of meteor and fireballs. The FRIPON network lies exactly between minor planets (detected by telescopes and planetary impacts) and interplanetary dust (detected by meteor networks). The solid arrow corresponds to FRIPON nominal mode; the dashed line is for rare events, observable by FRIPONbut with a very low probability.
In the text
thumbnail Fig. 12

Raw light flux from a bolide observed on 14 August 2019 at 03h07m02s UTC. Red triangles: Dijon FRIPON camera data(30 Hz, 12bits). Blue squares: radiometer prototype data (20 kHz, 14 bits). The faster acquisition rate and the higher amplitude dynamic range of the radiometer allows more detailed observations of the meteor fragmentation and of high speed luminosity variations.
In the text
thumbnail Fig. 13

Cyclic Doppler fluctuations on radio echo of the bolide observed on 8 August 2018 at 02h25m UTC, as seen by the Sutrieu radio receiver. Initial speed was 25.8 km s−1.
In the text
thumbnail Fig. B.1

Diagram of the FUNcube (Abbey 2013) Software Defined Radio.
In the text
thumbnail Fig. C.1

Map of the 3700 trajectories measured with FRIPON data from 2016 to early 2020. The concentration of detections is in part explained by the background sunshine weather map (sunshine duration in hours per year). The Rhône valley and the south of France have twice as many clear nights as the north. Another factor is that the installation of the cameras, done mostly throughout 2016, started in southern France and around Paris.
In the text

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.