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A Simple Diagram to Classify Meteoroid Impacts

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A Simple Diagram to Classify Meteoroid Impacts A simple diagram to classify meteoroid impacts Maria Gritsevich and Eleanor Sansom Acknowledgments to Manuel Moreno, Esko Lyytinen, Elizabeth Silber, Desert Fireball Network & Finnish Fireball Network [email protected] Outline q Introduction q Method implemented for parameters’ identification q Model validation q General statistics and consequences Past terrestrial impacts ~300 km The largest verified impact crater on Earth (Vredefort) Past terrestrial impacts ~300 km ~1,2 km Different types of impact events, examples: formation of a massive single crater (Vredefort, Barringer) Past terrestrial impacts ~300 km ~1,2 km ~1,8 km Different types of impact events, examples: formation of a massive single crater (Vredefort, Barringer, Lonar Lake) Past terrestrial impacts ~1,2 km Dispersion of craters and meteorites over a large area (e.g. Sikhote-Alin) Past terrestrial impacts ~1,2 km ~1,8 km Tunguska Tunguska after 100 years… Area over 2000 km2 q Introduction q Method implemented for parameters’ identification q Model validation q General statistics and consequences Groundbased observations The information on meteor body entry into the atmosphere contains detailed dynamic and photometric observational data. The important input parameters are: the fireball brightness I (t), its height h (t) and velocity V (t) Interpretation of meteor observations Photometric Dynamical Usually simplified case used is: Low-variable dV approach = 0 dt “Full” scenario utilized e.g. in Ø No assumptions needed Gritsevich & Koschny, 2011 and Bouquet et al. 2014 Ø Based on mathematical fit Suitable parameterization is the key 2 1 ρ 0 h0 Se chVe α = cd , β = (1- μ) * , μ = log m s 2 M e sin γ 2cd H a characterizes the aerobraking efficiency, since it is proportional to the ratio of the mass of the atmospheric column along the trajectory, which has the cross section Sе, to the body’s mass b is proportional to the ratio of the fraction of the kinetic energy of the unit body’s mass to the effective destruction enthalpy μ characterizes the possible role of the meteoroid rotation in the course of the flight The key dimensionless parameters used (air-in- front-of -the-body) 1 ρ0h0 Se 1 M α = cd = cd 2 M e sin γ 2 M e a characterizes the aerobraking efficiency, since it is proportional to the ratio of the mass of the atmospheric column along the trajectory, which has the cross section Sе, to the body’s pre-atmospheric mass b is proportional to the ratio of the fraction of the kinetic energy of the unit body’s mass to the effective destruction enthalpy μ characterizes the possible role of the meteoroid rotation in the course of the flight (Gritsevich, Koschny, 2011; Bouquet et al. 2014) The key dimensionless parameters used 2 2 chVe chVe M e β = (1- μ) * = (1- μ) * = 2cd H 2cd H M e E = (1- μ)c / c kin h d E (required-to- fully-destroy-the-object-in-the-atmosphere) b is proportional to the ratio of the fraction of the kinetic energy of the unit body’s mass to the effective destruction enthalpy μ characterizes the possible role of the meteoroid rotation in the course of the flight (Gritsevich & Koschny 2011; Bouquet et al. 2014) Determination of α and β t, sec h, km V, km/sec On the right: 0,0 58,8 14,54 Data of observations of Innisfree 0,2 56,1 14,49 0,4 53,5 14,47 fireball (Halliday et al., 1981) 0,6 50,8 14,44 0,8 48,2 14,40 1,0 45,5 14,34 1,2 42,8 14,23 y(v) = ln 2a + b + 1,4 40,2 14,05 1,6 37,5 13,79 2 1,8 35,0 13,42 -ln(Ei(b)-Ei(bv )) 2,0 32,5 12,96 2,2 30,2 12,35 2,4 27,9 11,54 xez dz 2,6 25,9 10,43 Ei()x= 2,8 24,2 8,89 ò z -¥ 3,0 22,6 7,24 3,2 21,5 5,54 q The problem is solved by the least 3,3 21,0 4,70 squares method (Gritsevich, 2009) q Introduction q Method for parameters’ identification q Model validation q General statistics and consequences 1. Pre-atmospheric size estimate • When meteorites are recovered one can use the models by Leya and Masarik (2009) to translate cosmogenic radionuclide activities measured in the laboratory into meteoroid “sizes” (defined as physical radius x density, with a unit of g∙cm-2) • An impressive match is observed between the sizes obtained based on using described here and cosmogenic radionuclide analysis, see e.g. estimates given for Park Forest (Meier et al. 2017), Kosice (Povinec et al. 2015, Gritsevich et al. 2017), and a summary table with earlier results in (Gritsevich 2008). 2. Terminal height calculation Calculated terminal heights (using described parameterization) vs. observed terminal heights for the MORP fireballs. See (Moreno-Ibáñez et al. 2015, 2017) for further details 3. Annama fireball //recently also the Ozerki J • The fireball was very bright and was witnessed in Russia, Finland, and Norway © Asko Aikkila, Ursa Fireball Network • Finnish Fireball Network imaged the fireball from Kuusamo, Mikkeli and Muhos sites (FN20140419) © Ursa, Taivaanvahti observation database. • Dash-board video made in Snezhnogorsk Annama fireball, April 19, 2014 © Asko Aikkila, Ursa Fireball Network © Ursa, Taivaanvahti observation database. Annama fireball, April 19, 2014 © Asko Aikkila, Ursa Fireball Network © Ursa, Taivaanvahti observation database. Annama fireball, April 19, 2014 © Asko Aikkila, Ursa Fireball Network © Ursa, Taivaanvahti observation database. Trigo-Rodríguez et al. 2015 ProjectionImpact of the sitefireball track Visualization of the fireball Illustration by Mikko Suominen / “Tähdet ja Avaruus” Height (km) vs Velocity (km/s) Velocity (km/s) Fb_entry program, Lyytinen & Gritsevich, 2013 & 2016 Fits well to the other meteorite falls fireball Ve, km/s sing α β Annama 24,23 0,55 8,33 1,61 Lost City 14,15 0,61 11,11 1,16 Innisfree 14,54 0,93 8,25 1,70 Neuschwanstein 20,95 0,76 3,92 2,57 NumericalImpact simulations site North à Color code of simulated fragments: blue are <0.3 kg, green 0.3 - 1 kg, yellow 1 - 3 kg, orange 3 - 10 kg, red >10 kg 5May days 29, meteorite first meteorite! expedition 120.35 g May, 29 first meteorite recovered J Annama meteorite • Analysis done at the Czech Geological Survey & University of Helsinki X • H5 ordinary chondrite • S2 shock level • W0 weathering degree • Bulk density 3,5 g/cm3 • Grain density 3,8 g/cm3 • Porosity 5 % Kohout et al. 2017 q Introduction q Method implemented for parameters’ identification q Model validation q General statistics and consequences Distribution of parameters α and β for MORP fireballs ln β 3 2 1 0 -1 0 1 2 3 4 5 6 7 ln α -1 -2 -3 ∆ Innisfree Looking for ‘Meteorite region’ M t ³ M min , vt << 1 ln β 3 2 1 0 -1 0 1 2 3 4 5 6 7 ln α -1 ba+3ln =- ln mmin -2 -3 sing = 0.7 and 1; M = 8 kg min ∆ Innisfree Some historical events Original mass, Collected meteorites, № Event a b t kg Canyon Diablo meteorite 1 > 106 > 30×103 0.1 ~ 0.1 (Barringer Crater) 2 Tunguska 0.2×106 - 0.3 30 3 Sikhote Alin 200 > 28×103 1.2 0.15 4 Neuschwanstein 0.5 6.2 3.9 2.5 5 Benešov 0.2 ? 7.3 1.8 6 Innisfree 0.18 4.58 8.3 1.7 7 Lost City 0.17 17.2 11.1 1.2 Same events on the plane (lnα, lnβ) lnβ (1) Crater Barringer ( 2) (2) Tunguska 3 (3) Sikhote Alin (4) Neuschwanstein 2 (5) Benešov (6) Innisfree 1 ( 4) (5 ) (7) Lost City (6) (7) 0 -3 -2 -1 0 1 2 lnα -1 ( 3 ) -2 (1) -3 ~1,2 km Same events on the plane (lnα, lnβ) lnβ ( 2) (1) Crater Barringer 3 (2) Tunguska (3) Sikhote Alin 2 α >1, β > 1 (4) Neuschwanstein 0 < α < 1, β > 1 (5) Benešov 1 ( 4) (5 ) (6) Innisfree (6) (7) Lost City (7) 0 -3 -2 -1 0 1 2 lnα -1 0 < α < 1, 0 < β < 1 α > 1, 0 < β < 1 ( 3 ) -2 (1) -3 ~1,2 km Meteorite / crater production criteria (1) Crater Barringer lnβ (2) Tunguska ( 2) (3) Sikhote Alin 3 (4) Neuschwanstein (5) Benešov 2 (6) Innisfree (7) Lost City 1 ( 4) † (5 ) (6) (*) Annama * (7) (†) Chelyabinsk 0 -3 -2 -1 0 1 2 lnα -1 ( 3 ) -2 (1) -3 ~1,2 km Meteorite / crater production criteria (1) Crater Barringer lnβ (2) Tunguska ( 2) (3) Sikhote Alin 3 (4) Neuschwanstein (5) Benešov 2 (6) Innisfree (7) Lost City 1 ( 4) † (5 ) (6) (*) Annama * (7) (†) Chelyabinsk 0 -3 -2 -1 0 1 2 lnα -1 ( 3 ) -2 (1) -3 ~1,2 km Using the criteria for the large datasets Conclusions α,β => consequences of an impact: » Meteorites » Craters » Airburst => success cases include meteorite recoveries: » Annama » Ozerki => applicable to big data (Sansom, Gritsevich et al. ApJ 2019) Several versions of the described algorithm were implemented and are available upon request (including C++, Python, Excel) Thank you very much! Further reading and references in: http://www.springer.com/us/book/9783319461786#aboutBook https://www.researchgate.net/profile/Maria_Gritsevich/publications Interpretation of meteor observations Photometric Dynamical dE dV 1 I = -t × M=- cr VS2 , dt dt 2 da Usually simplified case used is: dh dV =-V sin,g = 0 dt dt dM 1 “Full” scenario utilized e.g. in H* =- cr VS3 Gritsevich & Koschny, 2011 dt 2 ha and Bouquet et al.
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