In this paper, we analyse the technical biases of two intensified video cameras, ICC7 and ICC9, of the double-station meteor camera system CILBO (Canary Island Long-Baseline Observatory). This is done to thoroughly understand the effects of the camera systems on the scientific data analysis. We expect a number of errors or biases that come from the system: instrumental errors, algorithmic errors and statistical errors. We analyse different observational properties, in particular the detected meteor magnitudes, apparent velocities, estimated goodness-of-fit of the astrometric measurements with respect to a great circle and the distortion of the camera.
We find that, due to a loss of sensitivity towards the edges, the cameras detect only about 55 % of the meteors it could detect if it had a constant sensitivity. This detection efficiency is a function of the apparent meteor velocity.
We analyse the optical distortion of the system and the “goodness-of-fit” of individual meteor position measurements relative to a fitted great circle. The astrometric error is dominated by uncertainties in the measurement of the meteor attributed to blooming, distortion of the meteor image and the development of a wake for some meteors. The distortion of the video images can be neglected.
We compare the results of the two identical camera systems and find systematic differences. For example, the peak magnitude distribution for ICC9 is shifted by about 0.2–0.4 mag towards fainter magnitudes. This can be explained by the different pointing directions of the cameras. Since both cameras monitor the same volume in the atmosphere roughly between the two islands of Tenerife and La Palma, one camera (ICC7) points towards the west, the other one (ICC9) to the east. In particular, in the morning hours the apex source is close to the field-of-view of ICC9. Thus, these meteors appear slower, increasing the dwell time on a pixel. This is favourable for the detection of a meteor of a given magnitude.
Recently, several multi-station video camera systems to observe meteors have been set up, among others, in Japan (SonotaCo et al., 2010), in Canada (Weryk et al., 2013) and in the USA (Cooke and Moser, 2012; Jenniskens et al., 2011). The Canary Island Long-Baseline Observatory (CILBO) is a double-station meteor camera set-up operated by the Meteor Research Group of the European Space Agency. It is part of the video camera system of the International Meteor Organisation (Molau et al., 2015). CILBO consists of two stations, one on Tenerife and one on La Palma. A small building with an automated roll-off roof houses a set of video cameras with image intensifiers that monitor the same volume in the atmosphere for meteors. The pointing of the cameras is such that their image centres point to a height of 100 km between the two islands. Analysing the same meteor as seen from both camera stations allows the position relative to the Earth to be derived and, with that, to the cameras.
Photograph and sketch of the video cameras, called ICC (Intensified CCD camera).
The main scientific goals of the set-up are as follows:
To study physical and chemical properties of meteoroids and, taking
into account the modifications of the meteoroid properties during their
flight in the solar system, to constrain the physical and chemical properties of
their parent body. To study the variability of the background dust flux in the Earth
environment during a complete year.
To fulfil these goals, the following measurements are needed: (a) flux
densities of the meteors, derived from the meteor numbers per time; (b) the
physical properties of the meteoroids and their distribution, derived from
light curves and velocity analysis; (c) meteoroid orbits, derived from the
double-station observations; (d) chemical properties of the meteoroids,
derived from spectra of the meteors.
A double-station set-up is very well suited to address these points. Since the
distances of the meteor to the cameras can be determined, the absolute
magnitude and the velocity in m s
To properly analyse all of these measurements, many biases have to be considered. Meteors of a given mass will generate more light the higher their velocity when entering the atmosphere. They will only be detected when they are above a certain brightness, which also depends on the distance to the observing camera. Because of the optical effects of the camera, they may be detectable in the centre of the field of view but not at the edges, where the camera sensitivity is lower. The higher the apparent velocity of a meteor, the more pixels are covered per unit time by the meteor, making it more difficult to detect it. The observing geometry will affect the observations – as we will show, a camera pointing to the east will record more meteors than one pointing west. This is because the east-pointing camera sees meteors from the apex direction with lower apparent velocity, increasing the dwell time and thus the meteors signal on a pixel.
In general, we distinguish between two effects – physical biases and biases in the detection system. Physical biases include effects that are independent of the detection system. For example, meteors that due to their orbital elements do no intersect with Earth's orbit need to be estimated for modelling purposes. This paper deals with the latter, the detection system and with geometrical aspects. This affects the detectability of meteors and biases the resulting brightness and velocity distributions depending on the camera system's set-up, settings and its pointing. The following section gives more background on the technical aspects of the system. We first describe the set-up and then summarize all the expected errors.
A detailed overview of the set-up is given in a previous paper (Koschny et
al., 2013). In this paper, we focus on the camera and the detection system,
with an emphasis on their technical performance. Figure 1 shows a photograph
and a block diagram of one of the cameras. It consists of the following main
elements: (a) an objective lens-type Fujinon, 25 mm f/0.8; an image
intensifier-type DEP1700 with a fibre-coupled
In the following, we are analysing data from two cameras, called ICC7 (on Tenerife) and ICC9 (on La Palma). “ICC” stands for Intensified CCD camera. Both cameras are identical. They point to the same volume in the atmosphere, between the two islands. Thus their pointing azimuth is roughly opposite; the pointing elevation is similar but not quite identical.
The video cameras continuously record the night sky. With a field of view of
approximately 600 deg
MetRec generates a background noise image which is subtracted before the detection. The detection algorithm itself is described in Molau (1999, 2014). The software searches for brightness peaks in the background-subtracted images. It checks whether these peaks move on a great circle from one frame to the next.
For each frame of a detection, MetRec records the total digital number of the event on the detector and the position of its photometric centre. For each detected event, it stores a sum image, an animation of the event and a file containing detailed information on the event.
For each night, MetRec saves all files in a daily directory. The data for ICC7 and ICC9 are stored in individual paths. The detailed information of each meteor is saved in an individual ASCII file with the extension *.inf, henceforth called “information file”. Additionally, MetRec saves a log file that contains, e.g. the used detection parameters, the used reference file which contains the astrometric information of the stars and additional information of a recorded meteor.
The complete content of an information file is, for each frame where the meteor was detected, frame number, precise time taken from the computer clock, magnitude of the event, position of the photometric centre in coordinates relative to the detector and in celestial coordinates and fitted coordinates as described in the following paragraph. An example information file can be found in Koschny et al. (2013).
In addition to the information for each individual meteor, we use the log file entries in this paper to characterize the system behaviour. This file provides additional information for each detected meteor.
The automated event detection runs every clear night, controlled by a
scheduling software as described in Koschny et al. (2013). At the end of the
night, the data are uploaded to a central server for further processing. On
the next day, the data for each night is visually inspected and false
detections are deleted. The data are submitted on a monthly basis to the
video archive of the International Meteor Observation, where a peer-review
process ensures good data quality. All data are available and searchable via
the Virtual Meteor Observatory (Barentsen and Koschny, 2008,
MetRec allows us to manually compare a grabbed image with a star chart to produce a so-called “reference star” file. With this file MetRec can convert the relative positions together with the time of the event to right ascension and declination. The “referencing” process also generates a calibration file to convert pixel values to stellar magnitude. This process is typically done only when the camera pointing has changed.
MetRec attempts to correct any measurement errors in the position
determination. It takes the originally measured right ascension and
declination values and fits them to a great circle. The measured points are
projected onto this great circle. In a next step, MetRec shifts the points on
this great circle to be equally spaced. For longer meteors (
If a second meteor appears during the same second as a previous on, an additional log entry with the same time stamp is saved. However, the corresponding information file with the astrometric information is overwritten and lost.
In the later sections of this paper, we will present some findings on different parameters measured by the system. Then we will draw conclusions on how important the different biases are and which ones can be corrected. In summary, we expect the following errors.
The mechanical/thermal instability of the mounting: due to thermal
effects, the precise pointing position of the camera may change. This is a
systematic error affecting the position measurement of the meteor. The lens and possibly also the image intensifier generate a drop-off
caused by both vignetting and the tangent effect at larger distances to the
centre of the field of view. This is a systematic error affecting the
detectability of a meteor. Due to the projection of the celestial sphere on the flat sensor
surface, the system generates distortion which needs to be corrected when
computing positions of the meteors. This is corrected by the 3rd-order
polynomial “plate fit” performed during the measurement; however see
Sect. 2.3.3 point c. The sensor is read out with 25 frames per second and the readout generates
noise. In addition, random noise is generated by the image intensifier. The
noise statistics are estimated from a sequence of dark frames (no light
entering the sensor system). It is random noise affecting all measurements. The pixel resolution of the sensor does not precisely match the pixel
format of the used PAL format (768 pixel The sensor is an interline-transfer sensor, i.e. every second physical
line on the sensor is masked and used for readout. This and the previous
point will reduce the quality of the position determination of the meteor. (Absolute) timing errors (offset of the computer clock): this is a
systematic error that only affects the position, not the velocity. A timing
error of 1 s would correspond to a position error in right ascension of
Distortion of the image of a meteor close to the edge of the field of
view. This effect is particularly pronounced for bright meteors and it will
result in errors in the astrometric position of the meteor.
Wake: during the movement of the meteor it may develop a train, which
shifts the photometric centre to the opposite direction of the meteor's
movement. This effect will result in an apparent change in the velocity of
the meteor. Typically, trains develop towards the end of the meteor, so this
effect will reduce the perceived speed of the meteor towards the end. Blooming: for bright meteors, so-called blooming may occur; i.e. electrons
spill over from one pixel to other adjacent pixels. The shift of the
photometric centre can then go in any direction. The image distortion is corrected using a 3rd-order polynomial fit. In
particular, towards the edges of the field of view, a 3rd order may not be
good enough to properly describe the distortion. This will introduce a
systematic deviation of the measured positions with respect to the real position. When determining the position of a meteor, our detection software
attempts to fit the positions using a linear or quadratic equation resulting
in a constant and linear equation for the velocity, respectively. Due to
geometric effects this may not be sufficient to describe the position and
causes a deviation between the fit and the actual measured meteor position.
The effect is meteor dependent, as it is affected by the length of the meteor
in number of frames. Any velocity determination error may be estimated by
calculating how the velocity will really change when crossing the field of
view and how good the quadratic fit is. Meteor beginning and end: since the meteor will start or end at a random
time during the exposure of the first or last frame, taking the photometric
centre as the position of the meteor for this frame does not give the correct
results. This is a systematic error that only affects the velocity. Quantization error of position in the information files: the position of
a meteor is stored as a relative position in the frame (from 0 to 1) with an
accuracy of three decimal places only. This corresponds roughly to 0.3 pixel.
If meteor positions are recomputed later in the analysis process this
information is used, resulting in a quantization of the position. This is a
random error which affects both position and velocity. It is meteor
dependent, because meteors with more frames will be less affected.
Statistical random error: both the position and the brightness
measurements of a meteor in an individual frame are affected. This is an
error due to the probabilistic nature of the event and is independent from
the used instrument or its settings. It affects both position and velocity
and it can be derived from the accuracy of the meteor fit that is currently
investigated. It is meteor dependent, influenced by the number of frames,
meteor brightness and possibly velocity.
In the following sections, we characterize the camera systems in detail. We give results on technical aspects related to camera and software (flat field effects, distortion, etc.). We then present statistics on overall distributions of different meteor characteristics (meteor length, brightness, etc.). We combine these results and provide, as a result, the means to properly de-bias the data from the cameras for scientific analysis.
Albin et al. (2015a, b) have made a first attempt to analyse a selected number of bias effects for meteors detected simultaneously to ICC7 and ICC9. Here we expand on this work and also treat some of the data from the cameras separately. We use data from the information and the log files.
The data flow followed the description in Sect. 1. We have used a total of 51 062 and 56 951 information files and 925 and 913 log files for ICC7 and ICC9, respectively. The analysed time range was from 13 September 2011 to 31 August 2015.
In the following subsections, we describe different parameters of the measurements. These will be interpreted in the discussion section.
8 Bit median flat of the ICC7 camera. The
We start by analysing the detection efficiency of both cameras vs. the
apparent meteor velocity in pixels per second. The detection efficiency is
defined as the ratio of the theoretically expected number of meteor
detections on the CCD vs. the number of actual meteor measurements on the CCD
(Albin et al., 2015a). Due to vignetting and projection effects the cameras
have a sensitivity drop to the edges and corners of the CCD. Thus, the number
of detections decreases to the edges due to the lower signal-to-noise ratio
(
Figure 2 shows the flat field of the ICC7 system. The flat field of ICC9 looks similar. The image is an 8 bit median stack of about 10 individual images, recorded when thin fog provided a rather homogeneous sky background. The grey bar indicates the corresponding normalized brightness. It can be seen that the intensity drops to the edges and corners of the CCD. An optical system with no vignetting or projection effects would lead to a uniformly shaped distribution and a detection efficiency of 1. To compute the theoretically expected number of measurements we take the part on the CCD with the highest detection density and extrapolate this value for the complete CCD. A detailed description can be found in Albin et al. (2015a), who also computed the detection efficiency for the CILBO system depending on the meteor brightness. They found that the detection efficiency is at around 0.55 for meteors with a brightness down to 4.5 mag and drops down to 0.45 and lower for fainter meteors. This means that the meteor cameras detect only half of the meteors which it would be able to detect for an evenly illuminated sensor.
Detection efficiency vs. the downsampled velocity of a meteor in pixels per second. A detailed description of the detection efficiency can be found in Albin et al. (2015a).
Figure 3 shows the detection efficiency vs. the meteor velocity in pixels per
second. For the analysis, we use the filtered velocity data set from the
information files. The data set has been divided into bins of
25 pixel s
The pixel dwell time of a meteor is inversely proportional to the apparent
meteor velocity on the CCD. Consequently, a higher meteor velocity decreases
the
The shown effects and the detection efficiency function as shown in Albin et al. (2015a) are necessary to de-bias the mass distribution of the meteors that is correlated to the brightness measurements. Additionally, the determined flux needs to be corrected by at least a factor of 2.
Box plot of the ICC7 distortion. The difference between actual position and CCD position is shown in arcminutes vs. the radial distance from the centre of the CCD. Each box plot contains the data of the a 10-pixel-wide bin.
Albin et al. (2015b) described the velocity profiles of several
simultaneously detected meteors with the CILBO camera set-up. For the
analysis they used the geocentric velocity in km s
Normalized distribution of determined goodness-of-fit in arcminutes.
The orange and blue bars show the distribution for ICC7 and ICC9,
respectively. The bars are slightly off-centre and have an actual width of
0.1
We generated optical distortion maps to determine the astrometric deviations
of the real star positions relative to their expected positions according to
the 3rd-order polynomial plate fit performed by MetRec. Figure 4 shows the
computed distortion distribution for the ICC7 camera. The distortion is shown
by plotting the deviation of the real measured star position against its
expected position determined by the plate fit. It is given in arcminutes and
is plotted against the radial distance from the CCD centre in downsampled
pixels. The data are summarized in bins of 10 pixels and visualized as a box
plot A box plot is a way to visualize non-Gaussian distributions. It
uses the so-called median and the interquartile range (IQR). The median is
the point where a distribution is divided into two equal-sized sets. The 25-
and 75-percentile are the lower and upper limit of the IQR; the IQR contains
50 % of the data around the median. In a box plot, the median is shown as
a horizontal solid line in a box; the box itself corresponds to the IQR. The
dashed line has a length of 1.5
Since the ICC9 distribution looks similar, only the ICC7 data are shown. We will see that other astrometric errors are larger and conclude that at least for the inner 90 % of the field of view, errors due to insufficient distortion correction can be neglected.
Goodness-of-fit vs. frame length. The box plots show the median,
interquartile range (IQR) and 1.5
For each meteor, MetRec stores a value called “accuracy” in the log file, which describes the goodness of the fit of the individual meteor positions relative to a great circle in the sky. We will henceforth refer to this as “goodness-of-fit”. The value is given in arcminutes and is the root mean square of the deviations of individual meteor position measurements to the projections on a least-square great circle line. The smaller the value, the better the fit. This section analyses the recorded accuracies.
Figure 5 shows the normalized goodness-of-fit distribution based on all
meteor observations for ICC7 (orange or bright bars) and ICC9 (blue or dark
bars). “Normalized” means that the sum of all histogram bars is 1. The
distribution plot is shown from 0.0 to 4.0
It can be seen that both cameras detect a significant number of meteors with
a goodness-of-fit of 0.0
The median and IQR of the ICC7 and ICC9 accuracies are
ICC7
Normalized distribution of the peak brightness in magnitudes. The orange and blue curves correspond to the ICC7 and ICC9 camera, respectively.
MetRec uses half-resolution images for the detection, i.e.
384 pixel
When using these data to compute orbits, one can use the goodness-of-fit values to estimate, via Monte-Carlo runs, the errors of the orbital elements. A Monte-Carlo-based method to compute the astro-dynamic properties of the detected meteors is described in detail in Albin et al. (2016). To simplify this procedure, it is proposed to use an average error value as derived in the following.
Normalized distribution of the recorded frames for ICC7 (solid curve) and ICC9 (dashed curve). Since MetRec's detection threshold is set to 3 frames, no meteors are recorded on fewer frames.
Figure 6 shows a box plot of the complete accuracy data of ICC7 and ICC9 in
arcminutes versus the length of a meteor measured in number of frames. All
goodness-of-fit values from the log files have been used with the exception
of the 0.0
In conclusion, we suggest assuming a typical deviation of about
1.0–1.2
Distribution of the meteor velocities in pixel per second. The orange (bright) curves correspond to ICC7 and the blue (dark) curves show the ICC9 data. The solid distributions show the complete data set, containing all determined velocities. The dashed curves show the filtered velocity data set as explained in the text.
ICC7 and ICC9 have the same technical set-up and are operated in a similar way. Items like the detection threshold and the minimum number of frames per meteor are identical. Here, we compare the measured brightness distribution of both CILBO cameras to check whether deviations in the data can be identified. For our analysis we assume that meteors appear randomly on the sky. Since some meteors either begin or end outside CILBO's field of view (FOV) or both, we consider only meteors which were completely within the FOV. Otherwise a bias or offset in the meteors' brightness profile would affect the statistics. For the analysis we only take meteors into account that are not closer to the CCD edges than 5 % of the length and width of the CCD. Thus, the data set reduces to 49 494 meteors for ICC7 and 54 402 meteors for ICC9 which corresponds to 97 and 96 % of each individual data set, respectively.
Figure 7 shows the normalized distribution of the ICC7 and ICC9 brightness
data vs. the peak brightness values in magnitudes. The orange (brighter)
curve corresponds to the ICC7 data and the blue (darker) curve corresponds to
the ICC9 data. The median and corresponding IQR for both cameras are
ICC7
Maximum brightness in magnitude vs. the length of the meteor in
frame numbers for ICC7. The box plot shows the median, IQR and
1.5
Maximum brightness in magnitude vs. length of the meteor in frame
numbers for ICC9. The box plot shows the median, IQR and 1.5
Apparent meteor velocity in pixels per second vs. the video frame
length for ICC7. The box plot shows the median, IQR and 1.5
Apparent meteor velocity in pixels per second vs. the video frame
length for ICC9. The box plot shows the median, IQR and 1.5
MetRec's detection threshold is currently set to 3 frames. With 25 frames per second this corresponds to a meteor duration of larger than 40 ms (starting at the very end of the exposure of the first frame, ending at the very beginning of the last one) to 120 ms. In some rare cases a meteor with 3 frames can also have an appearance time, e.g. of 160 ms, due to frame drops in the detection pipeline.
Figure 8 shows the normalized distribution of the length of the meteors in number of frames. The solid histogram represents the ICC7 data and the dashed histogram shows the ICC9 data. CILBO detects meteors with a length of up to 70–80 frames. For a better data readability, here we show the distributions up to a length of 15 frames, corresponding to a meteor appearance time of 0.6 s. It can be seen that the number of meteor recordings decreases for longer events. Both distributions peak at meteors with a length of 3 frames. For increasing lengths, the number of meteors decreases faster for ICC9 than for ICC7. ICC7 detects more meteors on 3 to 7 frames than ICC9. Afterwards, the ICC7 distribution is slightly above the one of ICC9.
The apparent velocity of a meteor is computed from its position in each
frame, assuming that the frame rate is 40 ms. The position of a meteor is
available in two coordinate systems: firstly, in a CCD-fixed system given as
The second coordinate system in which MetRec provides the astrometry in is the
equatorial coordinate system, where the meteor position is given in right
ascension and declination. Due to optical distortions, the angular velocity
distribution in degrees differs from the distribution given in CCD
coordinates depending on the position in the field of view. Since this paper
focuses on the technical aspects of the CILBO cameras, in the
following only we consider the apparent velocity in the CCD-fixed coordinate system. For
those who prefer to think in degrees per second, note that 100 pixel s
Figure 9 shows the density distribution of ICC7 and ICC9 versus the velocity
in pixels per second. The solid curves are the distributions of all mean
meteor velocities, where the orange (lighter) curve corresponds to ICC7 and
the blue (darker) curve corresponds to ICC9 data. The velocity axis ranges
from 0 to 300 pixel s
Goodness-of-fit vs. peak brightness in magnitude for ICC7. The
box plot shows the median, IQR and 1.5
Goodness-of-fit vs. peak brightness in magnitude for ICC9. The box
plot shows the median, IQR and 1.5
Meteors appear and disappear at some arbitrary time during the exposure time
of the first and last frame of a detection (see Sect. 3.4). Thus, normally
the determined photometric centres of the first and last frame are shifted
towards the photometric centres determined from the second and second-to-last
video frame, respectively. To compute the velocity, the time interval between
two frames is used, namely 40 ms. This means that the first and last
velocity determination are typically underestimated. We leave those
values and call this the filtered velocity data. The dashed curves in Fig. 9
show the filtered mean velocity data sets of ICC7 and ICC9. Both dashed
curves appear similar to the solid ones. The median and IQR values for both
filtered data sets are ICC7
In the following sections, we only use the filtered velocity data set if not otherwise mentioned. We suggest that velocities computed from the first and last recorded frame should not be used.
Angular distance and normalized distribution of detected meteors vs. the time of the day in UTC (ICC7). The red (upper) and blue (lower) dashed curves show the angular distances between the ICC7 boresight and the apex and antihelion directions, respectively. The coloured areas around the dashed lines show the yearly variations. The solid vertical lines indicate the rising time of the antihelion (blue, left) and the apex (red, right) radiants. The hatched area shows the yearly variations. The black curve corresponds to the right axis and gives the normalized number of all detected meteors.
Angular distance and normalized distribution of detected meteors vs. the time of the day in UTC (ICC9). The red (upper) and blue (lower) dashed curves show the angular distance between the ICC9 boresight and the apex and antihelion directions, respectively. The coloured area around the dashed lines show the yearly variations. The solid vertical lines indicate the rising time of the antihelion (blue, left) and the apex (red, right) radiants. The hatched area shows the yearly variations. The black curve corresponds to the right axis and gives the normalized number of all detected meteors.
In Sect. 3.2 to 3.8 we showed distributions of different measured values like the accuracy or brightness of a meteor as determined by MetRec. Both ICC cameras are identical, but show deviations in the measured parameters. This section investigates possible correlations between certain measurements and parameters.
First, we describe the dependencies between the measurements and the recorded frame length. Afterwards we investigate possible detection time correlations. The last two subsections show some correlations with the measured brightness and determined velocities.
Figures 10 to 13 show box plots of the maximum brightness of a meteor in magnitudes and filtered mean apparent velocity in pixels per second for ICC7 and ICC9, respectively. The data are plotted vs. the length of a meteor in frames. Only meteors which were detected completely within the FOV of the cameras are considered.
The median and corresponding IQR of the brightness data for ICC7 and ICC9
show that the maximum brightness increases for longer meteors. Meteors with a
length of 3 frames have a median and IQR of
Ratio plot of the velocity in pixels per second of ICC9 divided by ICC7 vs. the detection time. The ratio is colour coded and given in the right colour bar.
Ratio plot of the faintest brightness measurements of ICC9 divided by ICC7 vs. the detection time. The ratio is colour coded and given in the right colour bar.
The box plots of the velocity distributions for ICC7 and ICC9 (Figs. 12, 13)
show a slight difference. Median and IQR for ICC9 are basically constant for
all shown meteor lengths. The IQR ranges between 50 and 150 pixel s
Goodness-of-fit vs. detection time for ICC7. The box plot shows the
median, IQR and 1.5
Goodness-of-fit vs. detection time for ICC9. The box plot shows the
median, IQR and 1.5
Figures 14 and 15 show the measured goodness-of-fit versus the average peak
brightness in mag for ICC7 and ICC9, respectively. We use all goodness-of-fit
values larger than 0.0
As mentioned in Sect. 2.3.3 point b, bright meteors overexpose the CCD pixels. This leads to blooming which results in an additional broadening of the meteor on a single video frame. Another effect may be that bright meteors are more likely to display a wake (Sect. 2.3.3 point a). Due to these effects the photometric centre cannot be determined correctly, which leads to a larger position determination error for brighter meteors.
Ratio plot of the distribution of the normalized length of a meteor in frames of ICC9 divided by ICC7. Each frame distribution is shown vs. the detection time. The ratio is colour-coded, with the values given in the bottom colour bar.
Even though both cameras are identical from a technical point of view, ICC9 detects fainter meteors. We argue in the following that this is a geometrical effect and can be explained by the camera pointing direction.
Both camera boresights intersect between Tenerife (ICC7) and La Palma (ICC9)
at an altitude of 100 km. Thus, ICC7 is pointing roughly to the west and
ICC9 to the east. The elevations of the boresights with respect to the
horizon are approximately 53
In Figs. 16 and 17 we plot the angular distance between the camera boresights and the apex and antihelion (AH) directions for the time frame 18:00 to 06:00 UTC. The red dashed line is the angular distance to the apex, the blue dashed line to the antihelion direction. The shaded areas around the lines indicate the annual variation. The black vertical lines indicate the rise times of antihelion (blue, left line) and apex (red, right line). Again, the shaded area indicates the annual variation. The thick black line is the normalized distribution of the observed meteors as a function of time during the night.
The antihelion point rises shortly after sunset, the apex direction after midnight. Since ICC7 is pointing towards the west, its angular distance to the apex point is always much larger than for ICC9.
Figure 18 shows the ratio between the number of meteors for a given apparent
velocity of ICC9 to ICC7 using a kernel density estimator (Pedregosa et al.,
2011). This plot shows an interesting behaviour. Starting after midnight, ICC9
sees more meteors than ICC7 in the velocity range of 50 to 200 pixel s
The larger number of slow meteors in ICC9 also explains Fig. 19. Since the meteors are slower, they spend more time on a pixel and fainter meteors can be detected. This is an important finding to derive scientific conclusions, e.g. determining flux densities. The limiting magnitude determined for stars will be identical for identical systems, no matter where the camera is pointing. However, the detection threshold for meteors will be different.
In Figs. 14 and 15 we showed that the goodness-of-fit is a function of the
magnitude. Since the magnitude distribution changes over the night, the
goodness-of-fit also will change over night. This is illustrated in Figs. 20 and
21. The goodness-of-fit is best during the evening hours and gets worse
towards the morning. The solid line indicates the median value, the dashed
lines the IQRs. The values start at around 0.7
Figure 22 shows three plots of the normalized length of a meteor in frames versus time for both ICC7 and ICC9, plus the ratio between two distributions. For each frame length bin, the integral of the distribution is 1. The colour map limits are the same for both cameras so that the differences between the camera systems can be visualized. It can be seen that both distributions show similar evolutions over time. Longer meteors are dominantly present during the evening and midnight hours and short meteors appear mostly during the morning hours. However, the distributions of ICC7 are spread wider than the distributions of ICC9. The ratio indicates a higher contribution of short meteors for ICC9 by a factor of up to 2. We explain this again by the apex meteors. ICC9 points closer to the apex than ICC7, in particular during the morning hours. Thus apex meteors appear shorter in ICC9.
In Sect. 2.3 we have listed the expected errors and biases from the instrument itself, the measurement pipeline and statistical sources. Here we map the findings of the previous section to these errors.
Mechanical/thermal stability: any mechanical/thermal instability would result in a shift of the field of view relative to an Earth-fixed direction. This would shift the measured position of a meteor. When visually inspecting the data, MetRec allows us to overlay the expected star positions with the real image. This was done regularly, and such a shift was observed in very rare cases towards the morning hours. It was typically less than 2 pixels. Since it only occurred in a few nights, it was not considered in this analysis and would deserve further study.
Brightness drop-off: the drop-off of brightness towards the edges of the optical system results in a loss of about 55 %. This will be an important effect when computing flux densities using the limiting magnitude of the system – the detected meteor numbers really are a function of the position in the field of view. The drop-off is larger than what would be expected from pure geometrical effects. It is assumed that this is an effect of the image intensifier. For non-intensified systems, we would expect this effect to be less severe.
Astrometric accuracy: the measurement accuracy of meteor positions
(astrometry) is influenced by a number of the listed errors. Figure 4 shows
the deviation between measured star positions and the expected position as
determined by the 3rd-order polynomial plate fit performed by the detection
software. It is below 0.2
We conclude that for our camera systems a typical error of 1 to 1.5
The position measurement inaccuracies will also affect the velocity determination. In addition, the first and last frame of the meteor should not be used for velocity determination, for the obvious reason that it is not known at what time during the 40 ms exposure the meteor appears or disappears.
In a future work we will determine possible effects of daily, weekly or seasonal temperature fluctuations. Scientific projects that will derive, e.g. flux densities from the CILBO camera system, will need to consider bias effects that have been shown in this work to un-bias and derive proper scientific conclusions form the observations.
We did not do a detailed analysis of random noise affecting the measurements. We assume that since the noise is random it does not produce any bias or shift in any of the measurements, but will only increase the scatter of the data.
We find that a major contribution to the detected brightness distribution comes from the pointing direction of the cameras. The pointing direction has to be taken into account when interpreting the detected number of meteors.
Currently, data until 2015 are available on
The authors declare that they have no conflict of interest.
We acknowledge the tireless efforts of Hans Smit and Cornelis van der Luijt (ESA/Space Science Office) for keeping the cameras operational. CILBO hardware and maintenance are funded thanks to the research faculty of ESA/Space Science Office. We also acknowledge the Instituto de Astrofisica de Canarias (J. Licandro) which hosts the CILBO system and provides local support. Edited by: L. Vazquez Reviewed by: P. Gural and one anonymous referee