| BZs >gnuplot.ps Acta Polytechnica Vol. 51 No. 2/2011 Searching for Space Debris Elements with the “Pi of the Sky” System M. Soko�lowski, M. Należyty, A. Majczyna, R. Wawrzaszek, P. Wajer Abstract The main purpose of the “Pi of the Sky” system is to investigate short timescale astrophysical phenomena (particularly gamma-ray bursts, optical transients and variable stars). Wide field, short exposures and full automation of the system, together with effective algorithms, give good prospects for effective identification of space debris elements. These objects can be a great danger for current and future space missions, and should be continuously monitored and cataloged. Algorithms for identifying optical transients (OT), designed for the “Pi of the Sky” experiment enable moving objects like planes, satellites and space debris elements to be identified. The algorithm verifies each OT candidate against a database of known satellites and is also able to automatically self-identify moving objects not present in this database. Thedata collected by theprototype in theLasCampanasObservatory enabledus to obtain a large sample of observations of moving objects. Some of these objects were identified as high-orbit geostationary (GEO) satellites, which shows that it is possible to observe even distant satellites with small aperture photo lenses. The analysis of the sample is still going on. The preliminary results and algorithms for automatic identification of moving objects will be described here. Keywords: space debris, robotic telescopes, satellite observations, satellite tracking, optical transients. 1 Introduction Space debris consists of objects originally launched by humans, which now orbit the Earth but are no longer in use. They can be upper stages of rock- ets, dead satellites, engine modules of geostationary satellites, remnants from satellite collisions, etc. Col- lisions of these elements moving at a speed of sev- eral km/s can destroy active satellites and can in the worst case be dangerous for astronauts on missions (spacecraft or space stations). This problem was spectacularly demonstrated in reality on 12 Febru- ary 2009, when the Irridium 33 and Cosmos 2 251 satellites collided over northern Siberia at relative speeds of about 11 km/s. The crash produced thou- sands of new pieces of space junk, which can be dangerous for other satellites. Such elements should be cataloged and continuously monitored to enable satellites and spacecraft to avoid them. Most space debris elements populate Low Earth Orbits (LEO), but the number of objects near to geo- stationary orbit (GEO) is constantly growing. There are about 13 000 artificial objects in the Earth’s or- bit, fewer than 800 of which are active satellites, while more than 12,000 are space debris elements larger than 10 cm. In principle, the only way to avoid collisions with potentially dengerous space de- bris elements (larger than 1 cm in size) is by ma- noeuvering spacecraft. However, in order to do this, space junk elements must be continuously monitored and cataloged. Efforts are being undertaken by na- tional and international space agencies to discover and monitor space debris elements. The largest ex- isting catalog of objects in the Earth orbit is the Space Object Catalog, provided by NORAD (North American Aerospace Defense Command), which is based on a network of optical and radio telescopes of various apertures. Systems like NORAD typically work in two modes. In wide-field mode they can dis- cover new space debris elements, which are later pre- cisely tracked by narrow field instruments. Long term narrow mode observations enables precise determi- nation of orbital parameters. The orbital elements are stored in the Two Line Elements (TLE) format which, besides identifiers, consists of ten parameters fully describing the object’s orbit [1]. There are also initiatives in Europe to create a system similar to NORAD. One of the ideas is the Space Situational Awareness program (SSA). The first stage of such a system is the European Space Surveillance System (ESSS) [2], which was proposed for automatic detec- tion and identification of space debris pieces, and for determining orbital elements. It will track objects from LEO orbits and predict their movements. Nat- ural candidates to join such a system are robotic tele- scopes. These instruments work automatically with almost no human attention. They also perform auto- matic data analysis, which is expected in the ESSS. In many cases they run algorithms very similar to those required for discovering space debris. Examples of European telescopes already tracking elements in the Earth orbit are TAROT (FOV of 2◦ × 2◦) and Starbrook (FOV of 10◦ × 6◦) [2]. Another component of this system will be wide- field optical systems like “Pi of the Sky”. The pur- pose of this study is to verify the ability of the “Pi 103 Acta Polytechnica Vol. 51 No. 2/2011 Parameter Prototype Final focal length 85 mm 85 mm focal ratio 1.2 1.2 CCD Sensor brand Fairchild STA CCD size 2 048 × 2 048 px 2 048 × 2 048 px FOV 20◦ × 20◦ 1.5–2 steradians CCD pixel size 15 × 15 μm 15 × 15 μm time of exposure 10 s 10 s relative accuracy of 20 ms 20 ms shutter synchronization limiting magnitude 12m 12m for stationary objects average accuracy of astrometry 10arcsec 10arcsec # of CCD cameras 2 2 × 12 (goal 2 × 16) # of mounts 1 2 × 3 (goal 2 × 4) Fig. 1: Basic parameters of “Pi of the Sky” telescopes. The image on the left shows the prototype system installed in the Las Campanas Observatory in Chile between 2004 and 2010 of the Sky” system to observe and automatically dis- cover objects in the Earth’s orbit (satellites or space debris). 2 “Pi of the Sky” system The “Pi of the Sky” system [3, 4] was origi- nally designed for observing short timescale optical events, particularly optical counterparts of gamma- ray bursts (GRBs), optical transients, and to monitor variable objects (stars, blazars etc.). The system is remotely controlled, fully automatic, to a large degree autonomous, and performs automatic data analysis. Special algorithms were designed and developed in order to automatically discover optical transients and discover new objects in the sky [5, 6, 4]. The final version of the system is currently under construction. The prototype system was installed in the Las Cam- panas Observatory in Chile (Figure 1) between 2004 and 2010. The prototype consists of two cameras, which observe the same field in the sky and collect images in synchronized mode. Besides its primary scientific goals, the data from the prototype has al- lowed to study the potential of the system for discov- ering and identifying space debris. The final system will have a substantially larger FOV (the final goal is 2 steradians), which will allow the system to look for new pieces of space debris more effectively (more information can be found in [3]). The data from the prototype verified the system capabilities, and fur- ther developed the algorithms and software tools for space debris oriented analysis. Figure 1 gives a sum- mary of the parameters of the prototype and the full version of the system. 3 Satellites in “Pi of the Sky” data The algorithms designed for identifying the short op- tical transients in the “Pi of the Sky” data are de- scribed in detail in [6]. The on-line algorithm analy- ses images while they are being collected by the cam- era, and looks for new objects appearing in the sky. In the first step, the algorithm finds objects which ap- pear in the new image, but were not present in previ- ous images. After this step, a list of flash candidates is created, then further cuts are applied in order to reject the background (mostly cosmic rays hits, hot pixels, sky background, star fluctuations etc.). One of the most important cuts at this stage is the coinci- dence of two cameras, requiring the optical transient to be visible in two cameras. This cut eliminates cosmic rays striking one of the CCD chips. After the coincidence cut, a list of real flashes from the sky is obtained. Most of them are due to moving objects, e.g. planes, satellites, and perhaps space de- bris elements. The primary goal of the algorithm is to identify OTs from natural astrophysical sources, so such objects must be identified, flagged, excluded from the sample of OT candidates, and saved to log files. Then another algorithm can simply be used to analyze the objects in detail and look for space debris elements. In order to reject most of these events, databases of orbital elements in Two Lines Element (TLE) for- mat are retrieved from the Internet (mainly from the NORAD website) every evening. They are combined into a single large database containing ≈ 13 000 or- 104 Acta Polytechnica Vol. 51 No. 2/2011 Distance to closest satellite Entries 9660 Mean 8346 RMS 4750 Distance from event to closest satellite [arcsec] 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 N u m b e r o f e v e n ts 210 Distance to closest satellite Entries 9660 Mean 8346 RMS 4750 = 1800’’satR Night 20070526 MC Distance to closest satellite : night 20070526 and MC Fig. 2: The image on the left shows the distribution of the distance from a flash event candidate to the closest satellite from the catalog, for events found by the coincidence algorithm during night of 2007.05.26/27. For comparison, distances from randomly generated flashes to the nearest satellite are shown as red dots — the combinatorial background is nicely reproduced. For the purposes of this analysis, the signal threshold was decreased to have more accidental coincidences. The image on the right shows the distribution of the distances from real data zoomed in the range [0,3000] arc sec only Fig. 3: Five subsequent 10s exposures showing fast, low orbit satellite SL-12 observed by the “Pi of the Sky” prototype Fig. 4: Five successive 10s exposures showing geostationary satellite XM-4 BLUES observed by the “Pi of the Sky” prototype. The satellite is a large object, with solar panels exceeding 10 m bital elements. For each image, the positions of all satellites in the database are calculated (using the predict package [7]), each flash candidate is verified and, if it is closer than Rsat = 1 000 arc sec from any of the satellites, it is flagged and excluded from further OT analysis. This rejection radius was de- termined according to the distribution of angular distances from flashes to the closest satellite from the database, which is clearly peaked around zero (Figure 2). The red dots on the plot represent the distance distri- bution for randomly generated flashes to the closest satellite from the catalog, and illustrate the size of the combinatorial background. The satellites identified according to the TLE database are mostly LEO and GEO satellites. Im- ages of low orbit satellite SL-12 are shown in Fi- gure 3. There are also many geostationary satellites in the sample (mostly large, with solar panels ex- ceeding 10 m). However, these observations show that it is possible to observe even geostationary ob- jects with the small aperture (70 mm) instrument. Images of geostationary satellite XM-4 BLUES are shown in Figure 4. The orbital element databases are not complete, and many satellites are not included in them. In or- der to identify them, event candidates are examined against track criteria. There are currently two pro- cedures for track identification: • Normal track — tries to form a track out of events from different images, it finds slowly mov- ing satellites (like the geostationary satellite in 105 Acta Polytechnica Vol. 51 No. 2/2011 CCDX 0 200 400 600 800 10001200 140016001800 2000 C C D Y 0 200 400 600 800 1000 1200 1400 1600 1800 2000 normal_tracks.lst CCDX 0 200 400 600 800 10001200 140016001800 2000 C C D Y 0 200 400 600 800 1000 1200 1400 1600 1800 2000 single_camera_tracks.lst Fig. 5: Tracks identified by the two algorithms described in the text during the same night. The image on the left shows 160 normal tracks and the image on the right presents 40 single camera tracks CCDX 0 200 400 600 800 100012001400160018002000 C C D Y 0 200 400 600 800 1000 1200 1400 1600 1800 2000 track00010.lst Track Info −−−−−−−−−−−−−−−−−−− Frame = 847 Point# = 435 Line type = P Params : a=−0.00003399 b=−0.00004576 c=701.24591028 Chi2 = 0.15 Oper = U CCDX 0 200 400 600 800 100012001400160018002000 C C D Y 0 200 400 600 800 1000 1200 1400 1600 1800 2000 track00455.lst Track Info −−−−−−−−−−−−−−−−−−− Frame = 309 Point# = 88 Line type = L Params : a=49.10953497 b=−1.00000000 c=−67695.77024120 Chi2 = 8.18 Oper = U Fig. 6: An example of a normal track with a fitted parabola (left image) and a single camera track with a fitted straight line (right image) Figure 4). If it is possible to fit a track to a set of events from different or same images and the velocity of the object is approximately con- stant, all events matching the track are flagged as moving objects. An example of 160 normal tracks fitted during a single night is shown in Figure 5. • Single camera tracks — identify tracks us- ing events identified by a single camera. The tracking starts with events from a single im- age, and requires at least 5 events in a track. If a new single camera track is identified, it is matched to earlier tracks of the same type. If they match each other, the earlier track is ex- tended by the new one and a single larger track is formed. This procedure rejects fast satellites (or planes) which produce “long line” signatures in a single image (Figure 3). An example of 40 nor- mal tracks fitted during a single night is shown in Figure 5. In the initial version only straight lines were fit- ted. For many moving objects, however, FOV of 20◦ × 20◦ is large enough to observe significant cur- vature of the track. Thus, in order to make the fit procedure more efficient, a parabolic curve is now also fitted. If the track consists of at least 20 events 106 Acta Polytechnica Vol. 51 No. 2/2011 Fig. 7: Example of a text file containing satellite observational data and χ2par/χ 2 line < 2, parabolic fit parameters are cho- sen. Examples of a straight line and parabolic tracks are shown in Figure 6. The efficiency of identifying moving objects by the normal track procedure was studied by checking how many of the satellites iden- tified in the TLE database were later identified in the track procedure. The procedure is already very efficient and 99 % of objects that are moving objects according to the TLE database were also identified by the normal track procedure. Software tools were created in order to obtain the positions and times of all observations of the given TLE-satellite. The observational data (right ascen- sion, declination, time and some additional informa- tion) is saved to text files (Figure 7). Optionally, the data may be saved to the database in order to allow for fast searches of multi-night observations of satellites. Php scripts were developed to access the data via the web browser. For example, it extracts all observations of the given satellite (from multiple nights). The data was later used to fit the orbital parame- ters. Two programs were tested: FindOrb [8] and the custom developed orbfinder software. Preliminary tests show that it is possible to fit orbital parameters using “Pi of the Sky” data if at least 0.7–1.0 % of the whole orbit is observed. 4 Conclusions The data from the prototype “Pi of the Sky” system was used to study the capability of the system to dis- cover space debris elements. Preliminary estimates show that the system is able to observe at least half of the objects from the NORAD database. It is even possible to observe large and bright satellites on the geostationary orbit. Algorithms for automatic iden- tification of moving objects were developed, tested and seem to be efficient. The analysis is still going on, but it seems that a wide field system like “Pi of the Sky” could be used successfully for survey tasks for discovering and calculating the orbit of large and easy targets. Acknowledgement We are very grateful to G. Pojmanski for giving ac- cess to the ASAS dome and for sharing his experience with us. We would like to thank the staff of the Las Campanas Observatory for their help during instal- lation of the apparatus. This work was financed by the Polish Ministry of Science from 2005 until 2010 as a research project. References [1] Website The Source for Space Surveillance Data, http://www.spacetrack.org/tle format.html [2] Klinkrad, H., et al.: Europe’s Eyes on the Skies — The Proposal for a European Space Surveillance System, ESA Bulletin 133 (2008). [3] Żarnecki, A. F., et al.: Improving photometry of the Pi of the Sky, this journal. [4] Ma�lek, K., et al.: Pi of the Sky Detector, Ad- vances in Astronomy, Volume 2010 (2010), Arti- cle ID 194946, 9 pages, http://www.hindawi.com/journals/aa/2010/ 194946.html [5] Sokolowski, M.: Investigation of astrophysical phenomena in short time scales with Pi of the Sky apparatus, PHD thesis, Institute for Nuclear Studies, Jan 2008, http://arxiv.org/abs/0810.1179 [6] Soko�lowski, M., et al.: Automated Detection of Short Optical Transients of Astrophysical Origin in Real Time, Advances in Astronomy, Vol. 2010 (2010), Article ID 463496, 11 pages, http://www.hindawi.com/journals/aa/2010/ 463496.html [7] Web page of Predict package, http://www.qsl.net/kd2bd/predict.html [8] Web page of FindOrb package, http://www.projectpluto.com/find orb.htm 107 Acta Polytechnica Vol. 51 No. 2/2011 M. Soko�lowski E-mail: msok@fuw.edu.pl The Andrzej Soltan Institute for Nuclear Studies Hoża 69, 00-681 Warsaw, Poland M. Należyty University of Warsaw Astronomical Observatory Al. Ujazdowskie 4, 00-478 Warsaw, Poland A. Majczyna The Andrzej Soltan Institute for Nuclear Studies Hoża 69, 00-681 Warsaw, Poland R. Wawrzaszek Space Research Center of the Polish Academy of Sciences Bartycka 18A, 00-716 Warsaw, Poland P. Wajer Space Research Center of the Polish Academy of Sciences Bartycka 18A, 00-716 Warsaw, Poland 108