Space Debris Observations with the Slovak AGO70 Telescope: Astrometry and Light Curves

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Advances in Space Research 65 (2020) 2018–2035 www.elsevier.com/locate/asr

Space debris observations with the Slovak AGO70 telescope: Astrometry and light curves

Jirˇ´ı Sˇilha a,⇑, Stanislav Krajcˇovicˇ a, Matej Zigo a, Juraj To´th a, Danica Zˇ ilkova´ a, Pavel Zigo a, Leonard Kornosˇ a, Jaroslav Sˇimon a, Thomas Schildknecht b, Emiliano Cordelli b, Alessandro Vananti b, Harleen Kaur Mann b, Abdul Rachman b, Christophe Paccolat b, Tim Flohrer c

a Comenius University, Faculty of Mathematics, Physics and Informatics, 84248 Bratislava, Slovakia b Astronomical Institute, University of Bern, CH-3012 Bern, Switzerland c ESA/ESOC, Space Debris Oﬃce, Robert-Bosch-Strasse 5, DE-64293 Darmstadt, Germany

Received 5 July 2019; received in revised form 10 November 2019; accepted 25 January 2020 Available online 5 February 2020

Abstract

The Faculty of Mathematics, Physics and Informatics of Comenius University in Bratislava, Slovakia (FMPI) operates its own 0.7-m Newtonian telescope (AGO70) dedicated to the space surveillance tracking and research, with an emphasis on space debris. The observation planning focuses on objects on geosynchronous (GEO), eccentric (GTO and Molniya) and global navigation satellite system (GNSS) orbits. To verify the system’s capabilities, we conducted an observation campaign in 2017, 2018 and 2019 focused on astrometric and photometric measurements. In last two years we have built up a light curve catalogue of space debris which is now freely available for the scientiﬁc community. We report periodic signals extracted from more than 285 light curves of 226 individual objects. We constructed phase diagrams for 153 light curves for which we obtained apparent amplitudes. Ó 2020 COSPAR. Published by Elsevier Ltd. All rights reserved.

Keywords: Space debris; Optical measurements; Space surveillance; Light curve; Catalogue; GEO; GTO; HEO

2010 MSC: 00-01; 99-00

1. Introduction

The demand for Space Situational Awareness (SSA) is constantly growing due to the increase of the space traffic ⇑ Corresponding author. which is now largely joined by private sector which oper- E-mail addresses: [email protected] (J. Sˇilha), stanislav.krajcovic ates its own launchers and satellites (del Portillo et al., @fmph.uniba.sk (S. Krajcˇovicˇ), [email protected] (M. Zigo), 2019; May et al., 2018). In order to have the usage of space [email protected] (J. To´th), [email protected] (P. Zigo), sustainable understand how debris is created, active debris [email protected] (L. Kornosˇ), [email protected]. removal, and the real time and high quality data acquisi- sk (J. Sˇimon), [email protected] (T. Schildknecht), [email protected] (E. Cordelli), alessandro.vananti@aiub. tion is a necessity. Space debris is situated on various types unibe.ch (A. Vananti), [email protected] (H.K. Mann), of geocentric orbits, from low Earth orbits (LEO) of sev- [email protected] (A. Rachman), christophe.paccolat@aiub. eral hundreds kilometres above the Earth’s surface to unibe.ch (C. Paccolat), tim.fl[email protected] (T. Flohrer). https://doi.org/10.1016/j.asr.2020.01.038 0273-1177/Ó 2020 COSPAR. Published by Elsevier Ltd. All rights reserved. J. Sˇ ilha et al. / Advances in Space Research 65 (2020) 2018–2035 2019 geosynchronous Earth orbits (GEO) at the heights of SST is responsible for regular tracking by using optical about 35,800 km above the surface. The regular monitoring (passive and active) and radar systems. This service and cataloguing of debris through the Space Surveillance requires orbit determination function and maintenance of and Tracking (SST) systems helps to identify and prevent a catalogue. SST requires access to a network of sensors possible collisions with operational infrastructure. The implying real-time data acquisition and processing (Silha majority of observation data comes from radar and optical et al., 2019). passive sensors but inclusion of the Satellite Laser Ranging (SLR) systems to the observations of non-active satellites 1.2. Optical networks and single systems and upper stages is also being considered (Shappirio et al., 2016; Konacki et al., 2016). The primary source of orbital elements data for cata- The research of space debris investigates the popula- logued space debris is the Space Surveillance Network tion’s dynamical (e.g. orbital elements) and physical prop- (SSN). The SSN consists of dozens ground-based radar erties (e.g. surface material). It covers wide range of topics and optical sensors and one space-based optical sensor including the survey and cataloguing (Schildknecht et al., (Raley et al., 2016; Abbasi et al., 2019). It covers all orbital 2004; Molotov et al., 2008; Fiedler et al., 2019), attitude regions, from LEO up to High Earth Orbits (HEO), and its determination (e.g. through light curves) (Williams, 1979; catalogue contains the mean osculating elements in a form Santoni et al., 2013) to support the debris mitigation efforts of TLE (Two-Line Elements) publicly available at (Liou et al., 2010; Forshaw et al., 2017; Wang et al., 2018) (Network, 2019). and anomalous behaviour of the object (Slatton and The largest civilian network performing the SST func- Mckissock, 2017), deals with the models of the spatial dis- tion is the International Scientific Optical Network (ISON) tribution for small populations (from lm to cm) (Krisko operated by the Keldych Institute of Applied Mathematics, et al., 2015) and analyzes the surface properties of the Russian Academy of Sciences, Russia. There are more than object (Vananti et al., 2017; Cardona et al., 2016; Lu three dozen of observation facilities worldwide contribut- et al., 2017). ing to the ISON network (Molotov et al., 2008, 2017). Sev- In recent years several European countries increased eral other networks perform the SST functionality their efforts toward partial independence of SST capabili- including: Russian network of Automated Warning System ties (e.g. observations of GEO population) from the inter- on Hazardous Situations in Outer Space (ASPOS OKP) national partners, e.g. USA and Russian Federation. These center (Agapov et al., 2018), SMARTNET (Fiedler et al., efforts can be demonstrated through the establishment of 2019) and OWL (Park et al., 2018). European Space Agency’s (ESA’s) SSA programme with A single sensor is not able to cover any of the popula- a SST segment addressing technology developments for tions from LEO up to HEO completely and is usually used monitoring space debris. A part of the ESA SST is the to acquire statistical information about a specific orbital Coordination Expert Center which will be responsible for region by using sky surveys, or to acquire scientific data the interfaces between heterogeneous sensors and the cata- for a specific object. Well established sensors are, for exam- loguing function of the SST segment (Jilete et al., 2019; ple, ESA OGS (Spain) (Schildknecht et al., 2004) and Silha et al., 2017). Additionally, in 2015, the European NASA MODEST (Chile) (Seitzer et al., 2004) which both Commission established its own EU SST Support Frame- dedicate their observation program to the continuous sur- work governed through the EU SST consortium which veys of the GEO ring, or ZIMLAT (Switzerland) system of now consists of eight EU countries (Morand et al., 2018). the Astronomical Institute of the University of Bern Any effort to perform SST functions require a sufficient (AIUB) used for optical observations of debris and Near network of sensors, both radar and optical, in order to get Earth Asteroids (NEA) (Silha et al., 2018). This system also a wide coverage, suitable frequency of observations per cooperates with the aforementioned networks such as object and continuous data flow into the system. Slovak ISON and SMARTNET to which ZIMLAT has estab- Republic, as a member of EU and prospectively future lished interfaces. member of ESA, expressed its interest to participate in the European SST programs, as well in space debris 1.3. Data products research, by focusing on astrometric and photometric data acquisition with optical passive sensors (Silha et al., 2019). As for the SST applications, the optical measurements provide several different products. The most important 1.1. Optical measurements for the maintenance of a catalogue and monitoring of the system’s performance are the astrometric measurements There are two major observations strategies recognized which contain the relative position of the object compared for optical passive observations of space debris. Optical to the star background. The astrometric measurements are surveys aim to discover new objects for cataloguing or to usually provided in spherical equatorial coordinates with get statistical information like the object’s brightness distri- reference epoch in J2000. The data format is usually Con- bution or orbital plane. Tracking (follow-up) observations sultative Committee for Space Data Systems (CCSDS) are carried out for orbit determination and debris research. Tracking Data Message (TDM) format (The Consultative 2020 J. Sˇ ilha et al. / Advances in Space Research 65 (2020) 2018–2035

Committee for Space Data Systems, 2017) or Minor Planet (Schildknecht et al., 2004). The presented system is a New- Center (MPC) format (MPC, 2019a,b). tonian design telescope with a very thin parabolic mirror Photometry is performed in order to acquire informa- with diameter of 700 mm from Alluna optics with sup- tion about physical characteristics of an object. One can ported by gravity actuator. The focal length of the system study the rotation properties of an object through the light is 2962.0 mm. The CCD sensor is the FLI Proline PL1001 curves, method often used in minor planet domain (Pravec Grade 1 CCD camera with 1024 1024 pixels and 24 lm et al., 2005; MMT, 2019; Pontieu, 1997; Silha et al., 2018). pixel size which results in an effective field-of-view (FoV) Light curve is a consecutive series of brightness measure- of 28:50 28:50 and effective iFoV of 1:6700=pixel. AGO70 ments over time. Additionally, properties such as reflec- is equipped with a filter wheel with Johnson-Cousins filters tance/albedo (Kessler and Jarvis, 2004) and surface BVRI. colors can be extracted by using multi-band filters Currently, AGO70 has a limited tracking capability. There- (Cardona et al., 2016; Lu et al., 2017). The reflectance spec- fore, it is not possible to set specific tracking rates, e.g., arbi- troscopy can be performed to obtain the reflectance spec- trary object tracking. However, user can alternate between trum which helps to study the object’s surface properties sidereal tracking and terrestrial tracking (no tracking). To in detail (Vananti et al., 2017). track the targets on GEO and geosynchronous transfer Earth In Section 2 we present our optical system’s parameters orbits (GTO) terrestrial tracking is sufficient and we adapted and its observation programs. In Section 3 we discuss the the observation strategy accordingly (see Section 2.2). validation of the system by observing and processing measurements of Global Navigation Satellite System’s (GNSS) 2.2. Target list objects. Additionally, orbit determination has been performed for selected AIUB/ESA objects which is also dis- The target lists of AGO70 depends on specific observa- cussed in Section 3. Section 4 introduces the space debris tion program. Astrometric data were acquired for two light curve catalogue of The Faculty of Mathematics, Phy- types of object, the GNSS satellites, which were in our case sics and Informatics of Comenius University in Bratislava, American Global Positioning System (GPS)/ Russian Slovakia (FMPI), its construction and properties. Section 5 GLONASS system, and AIUB/ESA objects (objects dis- concludes the work. covered during ESA OGS surveys (Silha et al., 2017; Schildknecht et al., 2004). While GNSS objects are used 2. System description, observation program for validation purposes to identify the time bias and astrometric accuracy of the AGO70’s measurements, AIUB/ In this work we focus on our optical passive system, ESA objects are observed to perform the orbit determina- hereafter AGO70, which has been installed at the Astro- tion to validate AGO70’s capabilities to support catalogu- nomical and Geophysical Observatory in Modra, Slovakia ing efforts. List of selected AIUB/ESA objects and their (AGO) (Minor Planet Center code 118) in September 2016. orbital parameters are listed in Table 2. AGO70 is operated by FMPI. Its observations are primar- Concerning the photometry for light curve acquisition ily dedicated to the space debris research and SST. We dis- we acquired data for objects catalogued by the SSN situ- tinguish three major observation programs at AGO70 - the ated on GTO, GEO and Molniya orbits. For GTO and astrometry to support SST, instrumental photometry to Molniya orbits with high eccentricities the data acquisition characterize the debris attitude states, and BVRI photom- has been performed when the object passes near its apogee etry to characterize the debris surface properties. point, where the apparent angular velocity is the lowest along the whole orbit. 2.1. Telescope parameters 2.3. Observation planning, program SatEph

Parameters of the AGO70 are listed in Table 1. We also For observation planning we used our own program provide parameters of ESA’s ESASDT for comparison SatEph which was developed by the FMPI as a tool for

Table 1 Configurations of AGO70 and ESASDT telescopes. Operator FMPI ESA Telescope AGO70 ESASDT Telescope design Newtonian Ritchey-Chretien Mount Equatorial (Open fork) Equatorial (English/Yoke) Camera CCD CCD Array dimension 1024 1024 4096 4096 Primary mirror [m] 0.70 1.00 Focal length [mm] 2962.0 4500.0 Focal ratio f/4.2 f/4.5 Effective FoV [arc-min] 28.5 28.5 42.0 42.0 Effective iFoV [arc-sec/pix] 1.67 0.62 J. Sˇ ilha et al. / Advances in Space Research 65 (2020) 2018–2035 2021

Table 2 List of selected AIUB/ESA objects and their orbital parameters for the reference epoch 2018-03-17. Name Semi-major axis [km] Eccentricity [–] Inclination [deg] RAAN [deg] E07052A 42597.9 0.0007 13.5 327.1 E08211A 44113.4 0.0683 13.2 343.4 E09109C 42377.4 0.0332 18.4 13.2 E09206A 42762.8 0.0014 8.2 32.7 E09231A 41896.7 0.0073 14.5 353.7 E09233A 42451.8 0.0010 15.4 7.5 E10012B 42490.0 0.0015 13.3 27.7 E10039C 42700.2 0.0023 14.4 0.4 E10277A 43445.9 0.0242 4.7 74.9 E10305A 39453.0 0.0640 11.1 350.4 E11299A 42512.6 0.0009 13.6 26.6 E14328A 24829.4 0.7007 63.5 39.1 E14328F 24829.4 0.7007 63.5 39.1 space debris observations and quick debris identiﬁcation. It The used trackings are demonstrated in Fig. 1 where we is programmed in Java language (JDK version 1.7) and show composites of Flexible Image Transport System executable under Java Run Time Environment (JRE) (FITS) image series acquired by AGO70. Figure 1a depicts higher than JRE 1.5. The program consists from several composite of 8 frames of a GPS satellite NAVSTAR 76 freely available packages and also from own algorithms. (USA 266) (2016-007A) acquired for astrometry and sys- The basic components of SatEph are packages containing tem calibration. Satellite is the object moving from the Simpliﬁed General Perturbations SGP (Hoots and south-east (lower left corner) in the north-west direction Roehrich, 1980; Vallado et al., 2006) and TLE which can (higher right corner). Furthermore, Figure 1b shows a be loaded in it. SatEph is controlled via graphic user inter- composite of 130 frames for a defunct GEO satellite face (GUI) which makes the work with program very sim- acquired for photometry and light curve extraction. One ple. We used SatEph during observations to perform target brightness spike was captured in this series. Plotted object selection and calculate the ephemeris. was drifting towards bottom of the image (south).The apparently dashed horizontal lines are stars appearing to 2.4. Observation strategy drift left to right at the sidereal rate.

During our observations, we used either sidereal or ter- 2.5. Data processing, astrometry restrial tracking - depending on the objective. Sidereal tracking has been used for objects on GNSS and GEO The image processing pipeline for AGO70 is currently orbits once we were focused on the acquisition of astromet- under development (Silha et al., 2019). For that reason ric data. Objects as well the stars appeared as points in the we used for astrometric reduction the Astrometrica tool resulting images. Because the relative angular velocity of (Raab, 2016) and the star catalogues, either UCAC 4, 00= 00= GNSS and GEO objects is around 25 35 s and 15 s PPMXL or Gaia (Collaboration et al., 2018). To improve respectively, the selected exposure times were very short the processing efficiency with Astrometrica we used script to achieve a stellar appearance of the object for accurate suite Astrometry.net (Lang et al., 2010) to identify the cen- astrometric measurement. Therefore, we had to adapt ter of the field of view. During processing, the user manu- exposure times to the angular velocities of the objects ally selects the target within Astrometrica and is also and size of iFoV (see Table 1). For the GNSS objects we responsible for extracting the outputs, namely the spherical selected exposure time equals to 0.1 s and for GEO equals coordinates of the object and its brightness, from the to 0.2 s. Astrometrica tool. User is also responsible for constructing Terrestrial tracking was used in the majority of cases the tracklet, which is a series of consecutive measurements while acquiring light curves, with exposure times varying of the same object. Because this tool does not assume that in relation to the brightness of the object, from 1.0 s to the observed objects are on geocentric orbits, we had to few seconds to achieve measurements with high signal-to- correct the extracted astrometric positions for the annual noise ratio (SNR), optimally above 30. For each object aberration by using the following equations (Green, 1985) we acquired at least two series with different sampling to 0 avoid the aliasing problem (Silha et al., 2018). Usually, sin- a ¼ a da ð1Þ gle series did not exceed 10 min of length. Majority of the d ¼ d0 dd ð2Þ light curve series was acquired with R filter and during nights with mediocre observation conditions. Table 3 sum- da ’ c1secd0 Y_ cosa0 X_ sina0 ð3Þ marizes the parameters used during observations per- 1 _ 0 _ 0 0 _ 0 0 formed by AGO70. dd ’ c Zcosd X cosa sind Y sina sind ; ð4Þ 2022 J. Sˇ ilha et al. / Advances in Space Research 65 (2020) 2018–2035

Table 3 Summary of parameters used during AGO70 observation programs. Observation program Tracking Exposure time [s] Target Astrometry, calibration Sidereal 0.1 GNSS Astrometry, orbit determination Sidereal 0.2 GEO, Molniya Instrumental photometry Terrestrial 1.0–5.0 GEO, GTO, Molniya

Fig. 1. Composite of FITS frames acquired for operational GPS satellite NAVSTAR 76 (USA 266) (2016-007A) (a) (composite of 8 frames) and the rotating defunct GEO satellite Echostar 3 (1997-059A) (b) (composite of 130 frames) with the AGO70. Acquired with sidereal tracking with exposure of 0.1s/ R filter and GEO tracking with exposure 1.3s/ R filter, respectively. Once corrected, the obtained measurements were then using the SATORB program, orbit determination tool delivered to AIUB for further processing, system calibra- which is part of the CelMech program suite (Beutler, tion and orbit determination, which has been developed 2005). The force model of SATORB includes all relevant by AIUB to support optical sensors calibration. It helps forces and perturbations such as Earth’s geopotential with to identify and remove the constant time stamp bias in spherical harmonics resolution of degree 12 and order 12, the measurements (epoch bias) and to qualify the astromet- gravitational perturbations from the Sun, Moon, Earth ric accuracy of the AGO70 system. This analysis requires tides, corrections due to general relativity, direct radiation ground-truth data to be compared to the acquired data, pressure (Sun only), eclipses (Earth, Moon). This tool is O-C analysis (Observed - Calculated). To achieve this we used regularly for more than a decade for orbit determina- had to use very accurate predictions of GNSS satellites in tion and catalogue maintenance at AIUB (Schildknecht PRE formats generated by the Center for Orbit Determina- et al., 2004). Once the orbit is determined the residuals tion in Europe (CODE) (Dach et al., 2019). These satellites’ are calculated by performing the O-C analysis where the positions can be interpolated for specific time and can have observed are the measurements used for the orbit determi- accuracy of few millimeters/centimeters. Once the positions nation and calculated are the positions calculated from the are available they can be transformed to the local equato- new improved orbit. These residuals are calculated with rial coordinate system in J2000 (ac; dc)(Flohrer, 2008) Eqs. (5) and (6) are then used to qualify the obtained which is then compared to the measured positions of the solution. satellite (ao; do):

1 Di ¼ cos ðÞsin dio sin dic þ cos dio cos dic cosðÞaio aic ; ð5Þ 2.6. Data processing, photometry and Photometry processing contained six steps, with first qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi four steps with similar logic to steps published in Silha D ¼ D2 þ D2 þ ...þ D2 = ; ð Þ rms 1 2 n n 6 et al. (2018). where Di is the angular distance between the i-th measurement acquired at time ti (aic; dic) and the ephemeris (aio; dio) 2.6.1. Image reduction calculated for the time ti. Drms is the O-C RMS of residuals First, we performed image reduction to reduce the which provide information about astrometric accuracy and LIGHT frames by subtracting master DARK frame and can identify the epoch biases in measurements. dividing master FLAT FIELD. This is well established Once the astrometric data is corrected the orbit determi- procedure in astronomy and therefore we will not discuss nation can be performed. In our case this is done by AIUB it further. This step was performed by the tool AstroI- J. Sˇ ilha et al. / Advances in Space Research 65 (2020) 2018–2035 2023 mageJ, public domain, Java-based, software for astronomy image processing (Collins et al., 2017).

2.6.2. Light curve extraction Second, we constructed the light curve, brightness vari- ation over time, from the calibrated frames. This was also performed by AstroImageJ by using aperture photometry. Along other information we extracted total intensity of the object I [ADU] and estimated its error rðÞI as follows (Collins et al., 2017; Merline and Howell, 1995): 2 npix rðÞI ’ I þ npix 1 þ N total; ð7Þ nB where npix is the number of pixels used in integration of source, nB is the number of pixels used to estimate the background noise, and N total is the total estimated noise per pixel [ADU/pix]. If the image reduction has been performed when the BIAS and DARK counts have been removed, which is in our case true for majority of cases, N total should contain only the sky background noise, read-out noise and stray (parasitic) light signal. To get the values in magnitude logarithmic scale and to obtain the instrumental magnitude values and corresponding errors we used following formulas: mi ¼2:5logðÞI ð8Þ 2:5 rðÞI Dm ¼ ; ð9Þ i lnðÞ10 I where Eq. (9) was obtained as the derivative of Eq. (8).

2.6.3. Data screening and detrending Third step was to screen the light curve for outliers. We identify as outliers every measurement points which were either very bright or faint comparing to the whole data series. This could be bright pixels caused by the cosmic ray Fig. 2. Light curve of object SL-12 R/B(2) (2006-022D) acquired during particles interacting with the pixels or object passing in night 2017-07-25. Light curve and its polynomial fit of 2nd order (a), front of the star. Screening has been done manually by resulting detrended light curve (b). the team responsible for the data processing, so it is a sub- jective process at the moment. In case the object’s overall (FFT), Lomb-Scargle (Scargle, 1982) and Phase Dispersion signal decreased, e.g., due to the change of atmospheric Minimization (PDM) published in (Stellingwerf, 1978; extinction (to first order a function of elevation above the Schwarzenberg-Czerny, 1997) and adapted to MATLAB horizon) or phase angle, angle between the sun, object code by (Ofek, 2014). The initial analysis is perfomed by and observer, the caused trend needed to be removed by using FFT or Lomb-Scargle to identify the candidate fre- using a mathematical fit to the data. This is demonstrated quencies present in the time series. Then, the PDM is used on example in Fig. 2a where is plotted raw light curve (blue to reconstruct the phase diagram for the instrumental mag- dots) and its polynomial fit (red line) and in Fig. 2b where nitude, m as a function of phase /, and to obtain the mea- is plotted detrended light curve (blue dots). The experience i sured period P. shows that using polynomial fit (usually order of 1 or 2) is efficient enough to remove such trends without modifying the data set, e.g., obtained measured period or amplitude 2.6.5. Data series fitting of the light curve. Fifth step was performed to evaluate the quality of the reconstructed phase by investigating the residuals of the 2.6.4. Phase diagram construction, period estimation measurements and to estimate the errors in the obtained Fourth step was to extract the period present in the sig- parameters. This step also helps to extract the amplitude nal. For this step we used our own tool for frequency anal- of the phase diagram A. It is common to fit a sophisticated ysis based on algorithms such as Fast Fourier Transform periodic signal with combination of sine and cosine func- 2024 J. Sˇ ilha et al. / Advances in Space Research 65 (2020) 2018–2035 tions in the form of Fourier series (Pravec et al., 2005). In the values mi;max and mi;min with values mc;max and mc;min, our case we used following function to fit obtained phase respectively. diagrams: We used the determined Fourier function to perform O- Xn C analysis (see Section 2.5), where the measured data were / / ¼ a0 þ ðÞancosðÞþjwmi bnsinðÞjwmi ; ð10Þ the acquired relative magnitudes ms as function of phase j¼1 (marked as blue circles in Fig. 3) and calculated were data generated by Fourier harmonics as a function of / (marked where n is the number of harmonics and a ; a ; b ; w are 0 j j as continuous red line in Fig. 3). By comparing these two coefficients to be found. We set n to 8 as a trade off between data sets we calculated root mean square value D ; by the complex shape of the phase diagram to be fitted for rms mag using modified Eqs. (5) and (6). which higher harmonics are needed and excluding the fitting of very sharp maximums caused by the specular reflec- tions. An example of fitted series is plotted in Fig. 3, where 2.6.6. Error propagation the circles are the measured points and the red continuous Last step was to estimate the measurement error (one- rðÞ½ line is the determined Fourier series fit. Data was generated sigma error) for the obtained amplitude A mag , con- r/ðÞ rðÞ½ by processing light curve of object SL-12 R/B(2) (2006- structed phase , measured period P s and fre- rðÞ½ ½ 022D) (see also Fig. 2) for which we found P ¼ 13:15s. quency f Hz , where fHz is the frequency calculated ½ ¼ = The amplitude A of phase diagram is defined as the dif- as inverted value of measured period Ps (f 1 P). This ference between the brightest and faintest measurement has been done by following the work of (Montgomery point (Warner, 2006). One option to get these parameters and Odonoghue, 1999): rffiffiffiffi would be to identify the brightest m ; and faintest points i max 2 m ; in the data series. However, once the data is dis- rðÞ¼A rðÞm ; ð12Þ i min N persed, which is often the case for fainter objects, the rffiffiffiffi resulting values will not accurately represent the reality. 2 rðÞm r/ðÞ¼ ; ð13Þ For that reason we identify the points mi;max and mi;min N A but we use them to calculate the average value of maximum rffiffiffiffi 6 1 rðÞm mi;max and average value of minimum mi;min, respectively, by rðÞ¼f ; ð14Þ taking all the points close to these extremes, usually in N pT A % rffiffiffiffi point within 0.25 of the light curve of the specific 6 1 rðÞm phase. The measured amplitude is obtained as rðÞ¼P P 2; ð15Þ N pT A A ¼jm ; m ; j: ð11Þ m i max i min where N is the total number of measurements, T is the total Other option is to calculate the maximum mc;max and duration of the series [s] and rðÞm is a root-mean-square minimum mc;min of the obtained Fourier function. To get deviation in the observed magnitudes [mag], which is in calculated magnitude Ac we need to replace in Eq. (11) our case Drms;mag calculated in Section 2.6.5. Eqs. (12)–

Fig. 3. Reconstructed phase and its ﬁt obtained by applying the Fourier harmonic analysis (order of 8) (Eq. (10)). Processed were the data plotted in Fig. 2b. J. Sˇ ilha et al. / Advances in Space Research 65 (2020) 2018–2035 2025

(15) are valid once the epochs of observations are error free For better understanding of this effect, we will demon- (Montgomery and Odonoghue, 1999), which we always strate it on a simplified example. Object radiates light assume for AGO70, even that is not entirely correct as toward the observer and the incoming flux follows simple shown in Section 3.1. sine function with amplitude equals to 6 in magnitude scale and we assume the data was acquired continuously without 2.6.7. Measured amplitude problem interruption. This function is plotted in Fig. 4a (red contin- A unique case is when the object’s measured period P is uous line). We assume we have different exposure times texp comparable to the signal integration time, hence exposure in following relation with P : P=texp ¼ k ¼ 40; k ¼ 20; time texp. Once the ratio k ¼ P=texp is a small number, e.g. k ¼ 10, and k ¼ 6. The resulting magnitude points for each smaller than 40, the real variability amplitude Ar, the vari- case are shown in Fig. 4a (blue dots). It is clear from the ability of the incoming flux, and measured amplitude Am figure that once we use the before-defined sine function strongly depends on coefficient k. The reason is that Am and we integrate the incoming flux by following four becomes directly affected by the time resolution dt of a sin- defined k values, we get smaller Am with decreasing k. gle magnitude point (Buzzoni et al., 2019). This fact has a The time precision decreases with function dtkðÞ¼1=k, direct effect on the estimated parameter Am and its further while the data (mi) precision is also a function of the signal scientific applications, e.g., rotation axis determination or variability, e.g., to be extracted from the phase diagram UBVRI photometry when different exposure times are used (for example the sine function plotted in Fig. 4a). Fig. 4b for each filter. shows dependency of ratio Am=Ar on k for sine function plotted in Fig. 4a. For comparison we plot also function A 1 m ¼ 1 ; ð16Þ Ar k which very well follows the plotted functionality between Am; Ar an k for sine function. Fig. 4b clearly shows that the reduction Am compared to Ar can be up to 5% for k ¼ 50 and 10% for k ¼ 40. For that reason it is important to use data acquired under such conditions with great caution.

3. Astrometry

3.1. System validation and calibration

There were 14 nights in years 2017 and 2018 during which the GNSS objects were observed with AGO70 for calibration purposes. For each object we acquired more than one tracklet per given night. We distinguished between two types of data - measurements acquired before and after April 2018. The diﬀerence between these two time spans was in the way how the data was taken. Before April 2018 we used the commercial software MaximDL, while after April 2018 we used our own control software to acquire images with the camera. The acquired data were processed by AIUB (see Sec- tion 2.5) and the obtained results can be seen in Fig. 5 where the plotted number of measurements used for the analysis (top), estimated astrometric accuracy (middle) and epoch bias (bottom). For the nights before April 2018 we got accuracy of 0:900 0:200 in average and epoch bias of 29.5 ms 6.9 ms. For nights after April 2018 we got accuracy of 0:800 0:200 in average and epoch bias of +67.7 ms 6.8 ms. While the astrometric accuracy remained consistent during investigated time intervals, the epoch bias changed noticeably - for almost 100 ms. Fig. 4. The eﬀect of real variability magnitude A reduction to measured r Fortunately, the epoch bias seems to behave consistently magnitude Am demonstrated on a signal following sine function with amplitude of 6 magnitudes. The dependency of Am on factor k ¼ P=texp (a). in both cases for given time interval and therefore it could Dependency of ratio Am=Ar as a function of k (b). be removed from the measurements. 2026 J. Sˇ ilha et al. / Advances in Space Research 65 (2020) 2018–2035

Fig. 5. Number of measurements used for the validation analysis (top), estimated astrometric accuracy (middle) and epoch bias (bottom) of AGO70 system. Data obtained from GNSS measurements processed by AIUB.

3.2. Orbit determination objects had the observation arc below one day which was considered for our analysis as not sufficient. This is an We acquired 56 tracklets for 29 individual AIUB/ESA arbitrary value we selected to filter the objects not because objects in total, where one tracklet usually contained from it would represent any physical constrain for the orbit 8 to 12 measurements points. Out of these 29 objects, 7 determination. Therefore we selected only 14 objects with objects had more than two tracklets and 7 objects had five or more days long observation arc for further process- two tracklets with at least the minimum duration between ing (see Table 2) which has been done by AIUB with first and last tracklet more than 5 days. The remaining 15 SATORB (see Section 2.5). For each of 14 selected object

Fig. 6. Position residuals (Drms of O-C) as calculated during AIUB’s orbit determination routine performed with CelMech tool. Solutions when AGO70 system data was not included to the processing (filled, blue) and where it was included to the processing (pattern, green). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) J. Sˇ ilha et al. / Advances in Space Research 65 (2020) 2018–2035 2027 we generated two solutions for the orbit determination. miss-correlaton during the observations. For the remain- One solution was obtained by using only the AIUB’s mea- ing seven objects, the RMS of the obtained solutions were surements, the second solution was obtained by adding the worse than those without AGO70 with the worst value AGO70’s measurements corrected by the identified epoch obtained for the object E14328A getting from 1:600 to bias (Section 3.1) to the data set. For each determined 4:9700. solution we calculated root mean square Drms of O-C by For the solution without (w/o) AGO70 we got D 00 00 using Eqs. (5) and (6). The results in form of total rms Drms ¼ 1:3 0:4 in average, while for the solution with 00 00 of O-C residuals in astrometric position can be seen in (w/) AGO70 we got Drms ¼ 1:7 1:3 (omitting results D Fig. 6. For five objects the rms was lower for solution with for objects E10277A and E10305A). Results obtained for AGO70, namely for E10012B, E09231A, E08211A, solution w/ AGO70 are worse than solution w/o AGO70. E09206A and E14328F. The best improvement was for The reason is unknown at the moment and further orbit D object E08211A for which the rms decreased from value determination analysis would be necessary. This is planned 00 00 1:39 to 0:79 . For two objects, E10277A and E10305A, for the future work when more extensive data acquisition we could not find a solution which could be caused by will be performed.

Fig. 7. Distribution of 226 objects observed for photometry by AGO70 system in years 2017 to 2019 according to their orbital properties. Mean altitude of apogee versus mean altitude of perigee (a) and mean altitude vs orbital inclination (b). We covered three regions – GEO, GTO and Molniya. 2028 J. Sˇ ilha et al. / Advances in Space Research 65 (2020) 2018–2035

4. Aperture instrumental photometry (R/B), 18 were debris objects (DEB) and 15 were objects discovered by AIUB/ESA (DIS). The observation campaign dedicated to the photometry acquisition took place between 2017-05-09 and 2019-03-31. 4.2. Rotation properties During that period we acquired 285 light curves for 226 individual objects. Acquired light curves have been used All acquired light curves have been processed by using to build up an FMPI’s internal catalogue to be used for the methodology defined in Section 2.5. General statistics further scientific applications such as support of active deb- about rotational properties of observed objects can be seen ris removal missions, rotation axis determination, BVRI in Fig. 8. We plot the number of objects per given popula- photometry, object’s shape and albedo estimation, etc. This tion (P/L, R/B, DEB, DIS) with given rotation properties. catalogue is publicly available for scientific community Rotator stands for objects for which we were able to extract (FMPI, 2019) and is further discussed below. the period P and reconstruct their phase diagram. This has been done for 153 light curves belonging to 107 individual 4.1. Properties of targets objects, which is almost half of all the observed objects. The distribution of obtained measured periods and angular The observed objects were situated in GEO, GTO and rates can be seen in Fig. 9. The smallest P = 1.0541 s Molniya orbits, as shown in Fig. 7. From 226 observed 1.0e-4 has been measured for Breeze-M R/B 2002-062B objects 60 were marked in the public catalogue (Network, which corresponds to apparent angular rate of 341.5 deg/ 2019) as payloads (P/L), 133 were spent rocket bodies s. Slow stands for objects with slow rotation, rotation is

Fig. 8. Distribution of 226 objects observed by AGO70 system in years 2017 to 2019 according to their rotation properties and type. Plotted are rotators, slow rotators, stable objects and objects for which the light curve could not be processed (‘‘unknown”).

Fig. 9. Distribution of obtained measured periods (a) and apparent angular rates (b) for 107 objects observed by AGO70 system in years 2017 to 2019. Used bin widths were 5 s and 5 deg/s, respectively. J. Sˇ ilha et al. / Advances in Space Research 65 (2020) 2018–2035 2029 longer than the series duration (T), hence T < 1:2 P.We 4.3. Amplitude properties identiﬁed 29 of such objects in our data. Stable stands for objects for which we did not detect any signal in the light The distribution of measured Am can be seen in Fig. 10a. curve to be associated with rotation. That does not neces- For completeness we also plotted distribution of k param- sary mean that those objects are not rotating but it means eter in Fig. 10b. The largest Am=5.81 3.0E-02 mag with that the P is most likely much longer than the duration of k ¼ 41:9 was determined for Atlas Centaur R/B (1976- the data series T, hence PT. There were observed 29 of 073B) and the smallest Am=0.16 1.0E-02 mag with stable objects. Unknown stands for objects which light k ¼ 20:1 was determined for Breeze-M R/B (2014-064B). curves contained periodic signal but we were not able to For 29 P/Ls we got in average Am ¼ 2:2mag with standard reconstruct the phase diagram. This happened when the deviation of r ¼ 1:2mag and for 67 R/Bs we got in average time stamp had resolution of 1.0 s (see Section 3.1), when Am ¼ 1:8mag with r ¼ 1:1mag. the weather conditions were not favorable or the signal Intuitively we do not expect any relation between the was periodic but probably the spin axis was changing on measured amplitude Am and P, or apparent angular rate small time scales. There were 61 such objects which is x = 360 deg/P. However, in Fig. 11a and b, where we show 27.0 % of all observed objects. This is relatively high num- this relation for P/L and R/B type of objects, we clearly see ber, where majority of the objects were R/Bs, specially a pattern. Am rarely reaches values above 2.0 mag for angu- Breeze-M R/B. lar rates higher than around 20 deg/s, which corresponds to

Fig. 10. Distribution of measured amplitudes Am (a) and parameter k ¼ P=texp (b) obtained from 153 phase diagrams/light curves acquired by AGO70 system in years 2017 to 2019. Used bin width is 0.25 mag and 20, respectively.

Fig. 11. Measured amplitude as a function of apparent rotation rate obtained from 153 phase diagrams/light curves acquired by AGO70 system in years 2017 to 2019. Plotted are properties of P/L (a) and R/B (b). For sake of clarity the error bars have been omitted. 2030 J. Sˇ ilha et al. / Advances in Space Research 65 (2020) 2018–2035

Fig. 12. Measured Am (a) and real amplitude Ar (assumed relation Am=Ar ¼ 1 1=k) (b) as a function of apparent angular rate obtained from 20 phase diagrams/light curves of 16 individual objects of SL-12 R/B acquired by AGO70 system in years 2017 to 2019. The color indicates the total duration [year] that given object spent in space. For sake of clarity the error bars have been omitted.

P ¼ 18s. One could argue that this is a selection effect for the global navigation satellites we identified the astrometric given observed population types as seen for example for accuracy of our AGO70 system to be 0:800 0:200, and the population of SL-12 R/B (Fig. 11a). time bias of + 67.7 ms 6.8 ms. Another possibility is the influence of the measured We acquired astrometric data for 14 unique objects from amplitude problem for fast rotating objects as discussed the Astronomical Institute of the University of Bern (AIUB) in Section 2.6.7. Once we look on the k parameter plotted internal catalogue with observation arcs longer than 2 days. for all objects in Fig. 10b we see that more than 50% of We determined the orbits by using our and AIUB’s archive objects has this value below 50. Let’s assume that we cor- astrometric data. This analysis revealed that the results got rect Am values by assuming its relation with Ar defined by slightly worse once our data were added to the solution, in 00 00 00 00 Eq. (16). With this approach we get Ar values plotted in average from Drms ¼ 1:3 0:4 to Drms ¼ 1:7 1:3 . Fig. 12b for 16 individual SL-12 R/B objects. We also plot- We constructed 153 phase diagrams for 107 individual ted the age of the object indicated by the color of the point. objects along with their periods, amplitudes and related Even some amplitude values changed noticeably compared errors. Majority of the objects was observed only once. to Fig. 12a there is still a minimum number of objects with We identified smallest period to be P = 1.0541 s 1.0E- Am > 2:0mag and x < 20deg=s which indicates that in this 04 s. The largest determined amplitude was 5.81 3.0E- case the observation selection effect could play the major 02 mag and the smallest value was 0.16 1.0E-02 mag. role to shape the plotted distribution. For completeness, Tables A.4–A.7 in Appendix A list all Acknowledgments the obtained measured periods and amplitudes for light curves acquired by AGO70 system in years 2017, 2018 The presented work was performed under a programme of and 2019. ESA PECS activity ”Development of a Supporting Optical Sensor for HAMR Objects Cataloguing and Research 5. Conclusions (HamrOptSen)”, contract No. 4000117170/16/NL/NDe. Great gratitude goes to Jozef Vila´gi who was responsible for Large number of space debris objects poses a significant the development of the AGO70 telescope control and Ladislav threat to the space missions. In our work we presented Nova´k for his support during the light curve construction. application of an astronomical telescope in space debris research and space surveillance tracking. By observing Appendix A. Tables A.4–A.7

Table A.4

Table A.4 (continued) rðÞ rðÞ COSPAR Date P P Am A texp qalt Name (UTC) [s] [s] [mag] [mag] [s] [km] 71006B 18-Jun-2018 21:27:50 24.84 5.2E03 3.92 5.2E02 1.0 659 ATLAS CENTAUR R/B 74039A 14-Aug-2017 02:24:36 40.21 1.3E02 0.81 5.6E03 0.9 35204 ATS 6 74039C 15-Oct-2017 22:47:17 25.24 6.1E04 1.2 3.1E03 0.98 35583 TITAN 3C TRANSTAGE R/B 74060F 14-Aug-2017 01:05:30 152.8 1.6E01 2.93 2.2E02 1.0 35740 SL-12 R/B(2) 74060F 16-Aug-2017 00:25:25 151.1 1.1E+00 3.25 5.4E02 1.0 35740 SL-12 R/B(2) 74060F 16-Aug-2017 23:41:05 153.3 8.5E02 3.13 1.9E02 1.0 35740 SL-12 R/B(2) 74060F 15-Oct-2017 19:03:22 155.57 4.3E01 2.72 3.6E02 1.0 35740 SL-12 R/B(2) 75036D 27-Apr-2018 23:04:47 109.82 5.4E02 1.8 1.6E02 1.04 633 SL-6 R/B(2) 75036D 16-Aug-2017 20:36:23 96.2 2.3E02 2.11 9.7E03 0.96 633 SL-6 R/B(2) 75063D 23-Mar-2018 03:23:19 129.8 1.1E01 1.46 6.2E03 1.1 331 SL-6 R/B(2) 75081D 14-May-2018 01:51:40 579.75 9.4E01 1.88 1.1E02 1.18 73 SL-6 R/B(2) 75091B 27-Feb-2019 23:48:20 18.54 1.3E03 3.99 3.4E02 2.0 536 ATLAS CENTAUR R/B 76073B 11-Jun-2018 22:28:41 49.47 5.6E03 5.81 3.4E02 1.18 589 ATLAS CENTAUR R/B 77021A 28-Apr-2018 21:37:33 15.31 1.6E03 1.55 2.4E02 1.14 426 MOLNIYA 1-36 77021D 15-Aug-2017 20:44:15 73.8 6.1E02 2.0 1.3E02 1.0 205 SL-6 R/B(2) 77021D 16-Aug-2017 20:14:43 74.1 1.6E01 1.83 2.5E02 1.0 205 SL-6 R/B(2) 77071A 15-Nov-2018 18:01:08 23.01 7.5E04 1.68 8.7E03 1.98 35735 RADUGA 3 77092A 16-Nov-2018 16:54:39 556.82 1.4E+00 1.56 1.8E02 1.52 35650 EKRAN 2 77092G 23-Sep-2017 01:53:22 5.46 1.5E04 0.68 9.2E03 1.04 35461 SL-12 R/B(2) 77092H 16-Aug-2017 01:08:25 19.78 2.8E02 2.74 7.1E02 1.0 35749 EKRAN 2 DEB 77092H 17-Aug-2017 02:36:04 39.65 1.3E02 2.94 5.8E02 1.16 35749 EKRAN 2 DEB 78055E 17-Jun-2018 23:47:03 76.83 2.3E02 2.25 1.6E02 2.0 1167 SL-6 R/B(2) 78073A 17-Aug-2017 01:03:14 18.69 2.2E03 1.49 1.4E02 1.0 35756 RADUGA 4 78073A 10-Oct-2017 19:10:38 17.17 9.9E05 1.06 3.1E03 1.16 35756 RADUGA 4 78095E 07-Apr-2018 23:53:10 75.41 3.9E02 3.2 5.8E02 1.89 315 SL-6 R/B(2) 78118A 13-Aug-2017 23:06:53 77.7 2.3E01 2.58 5.8E02 1.0 20692 GORIZONT 1 78118A 16-Aug-2017 23:01:25 77.2 1.4E01 2.17 2.8E02 1.2 20692 GORIZONT 1 78118A 02-Aug-2017 23:37:45 77.25 2.6E02 2.24 2.0E02 2.0 20692 GORIZONT 1 78118A 31-Jul-2017 23:27:59 77.14 1.1E02 1.9 1.4E02 0.6 20692 GORIZONT 1 79105A 16-Aug-2017 01:41:01 90.3 3.3E02 2.44 1.4E02 1.0 35765 GORIZONT 3 *object decayed, status to 2019-06-10

Table A.5

List of observed objects, date of observation, measured apparent synodic period P, measured amplitude Am with their respective estimated errors rðÞP and rðÞ A , mean exposure time texp and perigee altitude qalt for space debris objects observed by AGO70 system during years 2017, 2018 and 2019. Listed are objects with COSPAR number between 1980 to 1999. rðÞ rðÞ COSPAR Date P P Am A texp qalt Name (UTC) [s] [s] [mag] [mag] [s] [km] 80016A 16-Aug-2017 02:31:28 23.32 9.0E03 1.65 1.8E02 1.1 35803 RADUGA 6 80016A 15-Oct-2017 23:22:14 23.3 1.3E03 1.17 6.4E03 0.97 35803 RADUGA 6 80016A 17-Aug-2017 01:26:53 23.3 3.1E03 1.29 1.1E02 1.1 35803 RADUGA 6 80060F 15-Nov-2018 18:42:50 6.03 2.7E04 0.81 6.4E03 1.0 35354 SL-12 R/B(2) 80060F 14-Nov-2018 21:04:09 6.03 7.5E05 0.84 4.8E03 1.14 35354 SL-12 R/B(2) 81057B 17-Aug-2017 01:44:50 11.11 1.0E03 2.59 3.5E02 1.58 35749 APPLE 81069A 16-Aug-2017 02:01:08 69.37 1.0E02 3.02 1.2E02 1.0 35792 RADUGA 9 82009A 14-Nov-2018 21:47:04 96.18 1.8E02 1.43 9.3E03 1.13 35771 EKRAN 8 83066A 17-Aug-2017 02:08:28 76.28 2.7E02 3.34 2.5E02 1.5 36281 GORIZONT 7 83118A 14-Nov-2018 23:02:35 64.59 1.2E02 1.5 1.5E02 1.13 36247 GORIZONT 8 83118A 15-Nov-2018 22:29:29 64.55 5.3E03 2.06 1.5E02 1.66 36247 GORIZONT 8 84028F 17-Oct-2017 03:16:23 13.45 7.9E04 1.19 9.7E03 0.84 35371 SL-12 R/B(2) 84089D 15-Oct-2017 19:41:37 72.1 1.4E02 1.72 8.0E03 0.85 1303 SL-6 R/B(2) 84114C 18-Apr-2018 20:35:55 357.6 2.9E01 1.07 5.2E03 0.97 376 ARIANE 3 R/B 85117F 16-Nov-2018 18:27:57 9.66 2.5E04 1.53 9.4E03 1.15 1903 SL-6 R/B(2) 86089A 16-Nov-2018 18:58:06 100.53 5.2E02 2.3 2.4E02 1.47 1952 MOLNIYA 1-69 86089D 28-May-2018 20:48:09 21.18 1.2E03 1.72 1.0E02 2.13 1739 SL-6 R/B(2) 88051C 05-Dec-2018 04:01:06 33.43 3.9E03 4.53 4.0E02 1.84 36010 PAS 1 89048D 17-Oct-2017 02:45:51 6.66 9.1E05 1.08 4.5E03 0.88 36373 SL-12 R/B(2) 90090C 29-Apr-2018 22:22:08 425.83 2.4E01 1.99 5.9E03 1.16 292 IUS R/B(1) 90090C 22-Mar-2018 00:01:26 413.2 4.2E01 1.35 6.7E03 1.0 292 IUS R/B(1) 90090C 09-Apr-2018 02:30:34 415.9 3.7E01 1.09 5.3E03 0.94 292 IUS R/B(1) 91037C 28-May-2018 23:23:40 28.4 6.8E03 2.0 3.9E02 2.59 1473 AURORA 2 R/B(PAM-D) 91064B 16-Oct-2017 03:57:04 4.93 8.7E05 1.17 9.1E03 0.97 35865 SL-12 R/B(2) 91074A 29-Jan-2019 21:47:16 221.85 5.7E01 2.01 3.3E02 1.07 36238 GORIZONT 24 2032 J. Sˇ ilha et al. / Advances in Space Research 65 (2020) 2018–2035

Table A.5 (continued) rðÞ rðÞ COSPAR Date P P Am A texp qalt Name (UTC) [s] [s] [mag] [mag] [s] [km] 92060B 22-Mar-2018 22:35:46 124.4 2.0E01 3.47 4.4E02 0.9 36607 SATCOM C3 94007C 18-Jan-2019 21:26:20 356.89 1.4E01 1.88 6.6E03 1.19 301 H-2 R/B 94007C 16-Oct-2017 22:43:51 323.8 3.4E01 0.84 5.0E03 0.59 301 H-2 R/B 94049B 16-Nov-2018 01:47:40 16.82 1.5E03 1.62 1.9E02 2.16 36054 TURKSAT 1B 95044A 29-Nov-2018 00:42:44 486.42 5.7E01 2.94 1.6E02 1.15 36019 NSTAR A 96040C 30-Apr-2018 01:49:42 269.7 1.4E01 1.86 7.0E03 1.16 284 ARIANE 44L + 3 R/B 96058D 05-Feb-2019 20:00:22 42.07 2.3E02 0.37 3.4E03 2.0 35724 SL-12 R/B(2) 97076D 16-Oct-2017 19:10:36 104.52 2.3E02 0.84 3.5E03 0.72 10188 SL-12 R/B(2) 98028D 28-Nov-2018 16:21:30 6.17 7.9E05 1.15 5.2E03 1.15 8056 SL-12 R/B(2) 98054A 29-Apr-2018 22:51:59 108.07 5.0E02 1.45 1.2E02 1.47 186 MOLNIYA 1-91 98054D 05-Feb-2019 22:59:18 33.39 2.2E02 0.3 4.2E03 2.0 106 SL-6 R/B(2) 98063B 21-Jun-2017 23:51:59 605.1 3.1E+00 2.2 2.4E02 2.15 36029 AMC-5 (GE-5) 99027D 30-Apr-2018 01:17:40 8.4 3.7E04 0.77 9.3E03 1.14 7107 SL-12 R/B(2)

Table A.6

List of observed objects, date of observation, measured apparent synodic period P, measured amplitude Am with their respective estimated errors rðÞP and rðÞ A , mean exposure time texp and perigee altitude qalt for space debris objects observed by AGO70 system during years 2017, 2018 and 2019. Listed are objects with COSPAR number between 2000 to 2009. rðÞ rðÞ COSPAR Date P P Am A texp qalt Name (UTC) [s] [s] [mag] [mag] [s] [km] 00011B 01-Aug-2017 00:17:23 7.87 5.4E04 1.02 1.2E02 0.77 6187 SL-12 R/B(2) 00049A 22-Mar-2018 18:36:11 18.55 1.1E03 1.16 7.7E03 0.96 35773 RADUGA 1-5 00051D 03-May-2018 23:44:18 96.16 4.2E02 1.72 2.0E02 2.0 2471 SL-12 R/B(2) 00082A 29-Nov-2018 00:13:02 31.37 2.3E03 5.28 3.2E02 1.86 35714 CHINASAT 31 (BEIDOU 1B) 01014A 13-Dec-2018 02:20:50 183.93 4.1E02 3.1 2.0E02 2.43 35841 EKRAN 21 01031A 26-Feb-2018 04:17:36 198.37 5.9E02 4.94 2.2E02 1.03 36075 GOES 12 01031A 26-Feb-2018 04:17:36 198.37 5.9E02 5.1 2.3E02 1.03 36075 GOES 12 01037A 22-Mar-2018 18:11:39 36.63 3.1E03 1.07 5.0E03 0.94 35752 COSMOS 2379 01037D 27-Feb-2019 17:46:29 11.73 2.3E03 0.7 8.6E03 1.0 35692 SL-12 R/B(2) 02003C 07-Apr-2018 21:48:06 32.9 5.4E03 3.17 2.5E02 0.95 1011 H-2A R/B 02051B 05-Dec-2018 04:28:47 145.14 2.0E01 2.12 2.5E02 1.13 483 DELTA 4 R/B 02062B 14-Aug-2017 01:59:23 1.05 1.3E05 0.23 2.8E03 1.1 8084 BREEZE-M R/B 03007B 29-Nov-2018 02:03:32 412.42 2.2E01 2.23 1.3E02 1.17 201 ARIANE 44L R/B 03024A 29-May-2018 00:54:21 110.06 2.6E02 2.79 1.7E02 2.14 36065 AMC-9 (GE-12) 03028C 28-Mar-2018 01:43:23 24.51 4.9E03 0.38 5.3E03 1.03 595 ARIANE 5 R/B 04010A 27-Mar-2018 21:48:14 182.9 2.0E01 2.02 1.5E02 1.0 35767 RADUGA 1-7 04015D 19-Jan-2019 05:14:02 6.71 3.5E04 0.94 1.2E02 1.17 35579 SL-12 R/B(2) 05046C 13-May-2018 20:54:36 26.79 4.6E03 0.93 1.3E02 1.16 463 ARIANE 5 R/B 06012B 25-Jul-2017 22:28:36 67.6 1.0E02 1.79 7.1E03 1.0 5962 ATLAS 5 CENTAUR R/B 06022D 25-Jul-2017 21:10:20 13.15 1.1E03 1.76 1.7E02 1.0 35359 SL-12 R/B(2) 06023B 12-May-2018 23:45:34 63.56 1.4E02 1.29 9.5E03 1.14 2164 BLOCK DM-SL R/B 06034B 13-Aug-2017 23:29:30 95.98 6.4E02 1.31 1.1E02 1.0 2850 BLOCK DM-SL R/B 08013B 29-Nov-2018 02:49:33 69.85 1.5E02 0.9 3.5E03 1.13 179 BLOCK DM-SL R/B 08022B 21-Mar-2018 21:32:46 130.44 4.7E02 0.6 3.7E03 0.94 34805 SL-23 R/B 08065C 19-Apr-2018 00:22:33 360.8 3.7E01 2.64 1.8E02 1.05 324 ARIANE 5 DEB (SYLDA) 09033B 05-Dec-2018 02:20:25 257.1 1.5E01 2.35 2.5E02 1.56 6631 DELTA 4 R/B 09042B 26-Feb-2018 03:45:03 1.66 1.0E05 0.34 3.3E03 1.0 14736 BREEZE-M R/B 09042B 26-Feb-2018 03:45:03 1.66 1.0E05 0.34 3.3E03 1.0 14736 BREEZE-M R/B

Table A.7

Table A.7 (continued) rðÞ rðÞ COSPAR Date P P Am A texp qalt Name (UTC) [s] [s] [mag] [mag] [s] [km] 11022C 28-Nov-2018 19:56:12 3.42 1.8E05 0.42 2.1E03 1.0 229 ARIANE 5 R/B 11022C 12-May-2018 22:51:23 3.33 4.8E05 0.34 3.2E03 1.15 229 ARIANE 5 R/B 11022C 06-Feb-2019 17:13:18 3.46 9.6E05 0.55 4.0E03 1.0 229 ARIANE 5 R/B 11022C 28-Mar-2018 01:12:22 2.64 1.8E04 0.34 6.2E03 1.0 229 ARIANE 5 R/B 12012D 05-Feb-2019 04:55:08 20.0 8.7E04 0.74 4.2E03 1.17 35720 SL-12 R/B(2) 12035C 30-Apr-2018 02:13:07 44.93 7.0E03 1.15 4.2E03 1.3 258 ARIANE 5 R/B 13077B 23-May-2017 22:29:55 28.59 2.5E02 0.65 8.8E03 0.2 36782 BREEZE-M R/B 14011C 14-Nov-2018 18:57:26 95.75 2.6E02 0.37 3.6E03 1.0 233 ARIANE 5 R/B 14046B 28-Feb-2019 00:16:50 82.14 2.5E02 2.26 2.8E02 1.0 180 FALCON 9 R/B 14064B 10-May-2017 01:36:46 7.88 5.6E04 1.16 1.1E02 0.2 34716 BREEZE-M R/B 14064B 16-Oct-2017 19:43:18 14.1 6.7E03 0.16 3.4E03 0.7 34716 BREEZE-M R/B 14085A 30-May-2018 23:31:38 110.76 3.5E03 1.64 8.1E03 2.02 36141 DUMMY SAT 2/BREEZE-M 14085A 16-Aug-2017 00:02:34 111.6 5.8E02 0.31 2.3E03 0.5 36141 DUMMY SAT 2/BREEZE-M 14085A 25-Feb-2018 03:47:15 223.41 1.1E01 0.83 3.7E03 1.02 36141 DUMMY SAT 2/BREEZE-M 14085A 25-May-2017 22:35:26 110.15 2.7E01 1.22 9.5E03 0.1 36141 DUMMY SAT 2/BREEZE-M 15048B 10-Jun-2018 20:30:05 264.9 1.0E01 0.59 8.5E03 1.2 35853 BLOCK DM-SL R/B 16065C 19-Jan-2019 04:47:37 22.63 1.2E03 0.78 4.2E03 1.24 35612 YZ-2 R/B 16065C 12-May-2018 22:27:30 474.1 2.8E+00 1.29 1.4E02 1.0 35612 YZ-2 R/B 16071B 29-Apr-2018 01:52:33 21.59 2.7E03 2.74 1.6E02 1.3 7683 ATLAS 5 CENTAUR R/B 16071B 01-Aug-2017 21:19:43 13.5 3.4E04 1.49 7.5E03 0.12 7683 ATLAS 5 CENTAUR R/B 16071B 02-Aug-2017 22:52:33 13.5 3.2E04 1.4 7.4E03 0.66 7683 ATLAS 5 CENTAUR R/B 16080B 16-Nov-2018 19:24:17 15.38 4.8E04 1.8 8.2E03 1.23 179 EPSILON R/B 16080B 28-Nov-2018 20:33:08 15.43 1.8E04 2.04 8.6E03 1.87 179 EPSILON R/B 17017B 06-Feb-2019 05:08:08 372.82 2.1E+00 1.96 3.7E02 1.0 237 FALCON 9 R/B 17046C 25-Feb-2018 22:18:57 7.5 9.8E05 1.03 4.8E03 1.02 35767 BREEZE-M R/B 17046C 25-Feb-2018 22:18:57 7.5 9.8E05 1.03 4.8E03 1.02 35767 BREEZE-M R/B 17046C 28-Mar-2018 00:07:25 7.51 1.4E04 0.46 3.7E03 0.97 35767 BREEZE-M R/B 17048B 21-Mar-2018 21:59:15 67.08 1.2E02 3.44 1.8E02 1.0 265 H-2A R/B 17059D 18-Apr-2018 23:01:18 354.4 2.5E01 2.94 1.3E02 0.96 228 ARIANE 5 DEB (SYLDA) 17063B 10-Jun-2018 22:18:20 94.6 1.2E02 3.77 1.2E02 1.1 205 FALCON 9 R/B 17063B 11-Jun-2018 20:40:25 94.7 1.6E02 4.25 1.7E02 1.06 205 FALCON 9 R/B 17086B 17-Apr-2018 18:57:50 16.74 7.8E03 0.34 5.6E03 1.0 35800 FREGAT R/B

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