Pith. sign in

REVIEW 3 major objections 6 minor 36 references

Beyond the Cube: Overlapping Grid Methods for Debris Collision Risk Assessment

T0 review · 3 major / 6 minor · reviewed 2026-07-13 · grok-4.5

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A half-cell-shifted second grid recovers boundary-missed debris collisions at linear cost and unmasks a geometric overestimation in the cube formula that two independent corrections then remove.

desk verdict Clean O(N) dual-grid fix for cube boundary blindness, with parameter-free calibration that works on Rush-In; transfer to real orbital rates is deferred, not faked. read the letter →

arxiv 2607.09634 v1 pith:Q52L6HSN submitted 2026-07-10 astro-ph.EP astro-ph.IMphysics.comp-ph

Yacob Medhin , Simone Servadio This is my paper · ORCID
classification astro-ph.EPastro-ph.IMphysics.comp-ph
keywords orbitaldebrisconjunctionscreeningcubemethodboundaryblindnessDoublecollisionprobabilitypair-distancecorrectionLEO
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Long-term orbital debris models must screen huge object populations for collisions without paying quadratic cost. The classic cube method only scores pairs that share a grid cell, which is fast but assigns zero risk to close approaches that straddle cell walls. This paper shows that a second grid shifted by half a cell recovers those pairs with only integer bin lookups, cutting blindness from about 9.7 percent to 4.2 percent across thousands of Monte Carlo runs and to exactly zero when snapshots are locked to the physics step. Clearing those false zeros reveals that the cube probability formula itself systematically overestimates each detected pair—an excess the zeros had been canceling, so net rates looked too low. Two corrections, a power-law ratio to the expected mean separation inside a cell and a parameter-free Gaussian built from the analytic mean and spread of that separation, bring per-pair calibration nearly into line, with the Gaussian residual near 0.08 percent. Both are already wired into a long-term debris Monte Carlo tool, so the choice of grid and correction can change multi-decade population forecasts.

What carries the argument

Double Cube: a primary cubic grid plus a secondary grid shifted by L/2 on each axis; a pair is evaluated if it shares a cell in either grid, so wall-straddling objects are recovered by index lookup alone without Euclidean distance tests, preserving O(N) conjunction screening.

What would settle it

Run Double Cube with the Gaussian correction against a dense deterministic hard-body propagation of a realistic LEO catalog and compare predicted collision rate to counted intersections; a persistent multi-percent mismatch would show the geometric corrections do not restore absolute calibration outside the rush-in geometry.

Watch

Extended reading notes

Core claim

The Double Cube method recovers boundary-crossing conjunctions by scoring any pair that co-occupies either a primary cubic grid or a secondary grid offset by half a cell side in every direction, using only bin-index comparisons so screening cost stays linear in the number of objects. Across eight thousand Monte Carlo seeds it cuts the fraction of true collisions assigned zero probability from 9.70 percent to 4.21 percent; a synchronized-time experiment drives residual blindness to exactly zero, proving the dual-grid geometry is spatially complete. Removing that blindness exposes systematic per-pair overestimation in the standard cube formula. A power-law correction keyed to the Robbins mean

Load-bearing premise

The corrections assume that pairs inside a cell behave like two points drawn uniformly at random from a cube, so the analytic mean and spread of separations remain a good model under real orbital motion, not only in the paper’s isotropic rush-in test.

Editorial extensions

If this is right

  • Debris evolution codes can recover boundary-crossing collisions without reverting to quadratic pair checks.
  • Once false zeros stop masking the formula’s per-pair excess, net predicted collision rates rise and can change which objects look highest risk.
  • The Gaussian pair-distance correction supplies a zero-parameter fix that needs only the already-computed snapshot separation.
  • In orbital settings a radial-range overlap gate is required so altitude-disjoint shells are not falsely paired by the shifted grid.
  • Multi-decade debris population projections become measurably sensitive to which correction and gate are applied.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If pair separations in real LEO shells deviate from the uniform-cube distribution, the Gaussian correction may need a mild density-dependent recalibration.
  • The same dual-grid plus mean-separation correction pattern could transfer to other kinetic-style spatial screens, including asteroid-belt or molecular cell methods that share boundary blindness.
  • With geometric blindness closed, residual error is dominated by snapshot timing, so adaptive snapshot intervals become the natural next lever for absolute rate accuracy.
  • Absolute agreement with deterministic orbital benchmarks—left open by the paper—will decide whether corrected Double Cube should replace default cube rates in capacity studies.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. The paper addresses boundary blindness in the classic cube method for O(N) conjunction screening in orbital-debris Monte Carlo models. It introduces Double Cube (DC): a primary cubic grid plus an L/2-shifted secondary grid that recovers boundary-crossing pairs by bin-index lookup alone, preserving linear complexity. Across 8,000 Rush-In seeds, blindness falls from β_Cube=9.70% to β_DC=4.21%; a synchronized Δt experiment drives β_DC to exactly 0.00%, isolating residual blindness as temporal. Removing blindness exposes systematic per-pair overestimation in the cube/NTC formula (Eq. 1). Two corrections are derived and validated on reliability diagrams: a DSMC-motivated power-law in the Robbins-normalized separation η (k=1,2), and a parameter-free Gaussian CDF correction built from analytic pair-distance moments μ=0.6617L and σ=0.2494L, achieving a slope residual of 0.08%. Both corrections and a radial-range overlap gate are implemented in MOCAT-MC, with illustrative 50-year ensembles; absolute-rate closure against Facchinetti’s deterministic orbital benchmark is deferred.

Significance. If the results hold under orbital dynamics, DC is a practically useful advance: it is the first published method that substantially reduces cube boundary blindness while remaining strictly O(N) and free of Euclidean neighbor searches (unlike I-cube or Smart Sieve). The synchronized β_DC=0.00% experiment is a clean geometric completeness test. The Gaussian correction is a genuine strength: μ and σ are derived from uniform-cube geometry alone (Robbins constant and E[d²]=L²/2), c=1.5 is fixed by neutrality at F(μ)=0.5 rather than fit, and the 8,000-seed reliability result (m=1.0008, A=1.10) is reproducible and falsifiable. Implementation in MOCAT-MC and the explicit radial-range gate further increase utility for capacity and ADR studies. The main open question—transfer of the uniform-cell moments and corrections to realistic LEO density and relative-velocity structure—is acknowledged by the authors and reserved for a follow-on paper.

major comments (3)
  1. [Application to Orbital Capacity] Application to Orbital Capacity / Fig. 7: The Rush-In analysis predicts only a ~6.1% increase in detected conjunctions from the blindness ratio (Eq. 22), yet MOCAT-MC reports a factor of 2.31. The radial-range overlap gate is introduced to suppress false co-cell pairs from disjoint altitude shells, but the manuscript gives no quantitative table of β, reliability slopes, ECE, or absolute collision counts with vs. without the gate under orbital propagation. Without those numbers, the claim that DC+corrections improve debris risk assessment in the operational setting is under-supported relative to the Rush-In rigor, and Fig. 7 remains illustrative only.
  2. [Gaussian Pair-Distance Correction] Gaussian Pair-Distance Correction, Eqs. (13)–(16) and Fig. 3: The true distribution of distances between two uniform points in a cube is known to be skewed (median ≠ mean), so F_true(μ) is not exactly 0.5. The neutrality condition that fixes c=1.5 therefore relies on the Gaussian ansatz, not on geometry alone. The paper should either (i) replace the Gaussian CDF by the exact cube interpoint CDF (or a skew-aware approximation) and recompute the reliability slope, or (ii) quantify how much F_emp(μ) deviates from 0.5 and show that the residual 0.08% is robust to that deviation. As written, “parameter-free and derived entirely from geometry” slightly overstates the status of Eq. (16).
  3. [Results] Results / Calibration: All Rush-In and MOCAT runs use a single cell size L=50 km. Because both corrections are functions of d_ij/(const·L), and because cube bias is known to be L-dependent (Lewis et al.; Alexander–Garcia–Alder (Δx/λ)² scaling), at least a two-point L sensitivity (e.g., 25 and 100 km) on m, A, and ECE for DC raw and the Gaussian correction is needed to support the claim that the bias is fully characterized by the pair-distance moments.
minor comments (6)
  1. [Introduction] Eq. (1) in the extracted text appears as “dU01)”; ensure the published PDF renders dU cleanly in the denominator.
  2. Section headings “NOMENCLATURE”, “SIMULATION ENVIRONMENT”, and “APPLICATION TO ORBITAL CAPACITY” appear with internal spaces in the source text; fix for production.
  3. [Adaptive Resolution Double Cube (ARDC)] ARDC Tier-1 volumes L³/2 and L³/4 are said to show “monotonic improvement” but no table or figure is given. Either add a short sensitivity table or drop the claim to a single sentence.
  4. [Results] Figure 6: add a short legend note that cube’s regression excludes P_ij=0 blind pairs (already stated in text) so readers do not over-interpret the lower cube slope as better calibration.
  5. [Nomenclature] Nomenclature lists both Δ⁽³⁾=0.661707 and ¯d=0.6617L; pick one rounding convention and use it consistently in Eqs. (5), (9), and (13).
  6. [Mean Collision Separation Validation] The d_min vs d_ij comparison is a useful negative finding; a one-line statement of the measured fraction (85%) already in the text would benefit from a small supplementary histogram or quantile table.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: DC geometry, analytic pair-distance moments, and independent physics-engine ground truth keep the derivation self-contained.

full rationale

The load-bearing chain does not reduce to its own inputs. Boundary blindness rates and reliability slopes are measured against A_ij from a deterministic sub-step physics engine independent of Eq. (1). The dual-grid completeness claim is geometric (L/2 offset) and is falsified/confirmed by the synchronized experiment reaching β_DC = 0.00%, not by definition. The Robbins mean μ = 0.6617L is taken from the classical uniform-cube integral / DSMC literature and only checked (not fitted) against Rush-In data (2% undershoot). The second moment E[d²] = L²/2 and σ ≈ 0.2494L follow from elementary coordinate variance under Uniform[0,L]; citation [34] is incidental, not a uniqueness theorem. Power-law exponents k = 1, 2 are imported from DSMC transport-error scaling, not optimized to the Rush-In slope. For the Gaussian CDF correction, c = 1.5 is fixed by the neutrality constraint α(μ) = 1 given the chosen family min(c − F, 1) and F(μ) = 0.5; that constrains the functional form but does not force the empirical reliability slope m = 1.0008 or residual 0.08%, which are outcomes against independent collision flags. No fitted parameter is renamed a prediction, no self-citation uniqueness theorem forbids alternatives, and absolute orbital-rate closure is explicitly deferred. Score 0 with empty steps is therefore the correct finding.

Assumptions & free parameters 2 free parameters · 5 assumptions · 2 invented entities

Load-bearing content is mostly standard kinetic-theory and uniform-cube geometry plus the modeling choice that snapshot cube probability with volume L³ is the right baseline. The Gaussian correction adds no fitted constants; k is theory-selected. The main extra assumption is that Rush-In-validated geometry transfers to orbital debris—flagged by the authors as unfinished for absolute rates.

free parameters (2)
  • power-law exponent k = 1 and 2 (theory-selected bounds)
    Chosen as the linear and quadratic DSMC-motivated bounds (k=1,2), not optimized to Rush-In reliability data; still a discrete modeling choice that changes residual from 1.9% over to 4.0% under.
  • cell side length L = 50 km
    Simulation and MOCAT runs use L=50 km; cube methods are known to be L-sensitive (Lewis et al.), so reported β and calibration are conditional on this choice.
assumptions (5)
  • domain assumption Objects co-located in a cell may be treated with the kinetic-theory collision probability P_ij = π(R_i+R_j)² V_rel Δt / L³ (Liou cube / DSMC NTC form).
    Baseline Eq. (1); entire calibration program is relative to this formula.
  • standard math Mean separation of two uniform points in a cube is the Robbins constant 0.6617L; second moment yields σ≈0.2494L; pair-distance CDF may be approximated as Gaussian for the correction factor.
    Used to derive MCS and Gaussian corrections (Eqs. 5–16); Gaussian shape is an approximation, not exact cube-distance law.
  • standard math An L/2 Cartesian shift of a secondary grid makes every primary-boundary-adjacent pair co-cell in at least one grid, so dual bin equality is geometrically complete.
    Core DC claim; supported by synchronized β_DC=0 experiment.
  • ad hoc to paper Isotropic Rush-In initialization is a valid calibration benchmark for the uniform-cell assumption underlying both formula and corrections.
    Scenario is designed to match kinetic-gas assumptions; transfer to orbital altitude shells is not established here.
  • domain assumption Two objects with non-overlapping [r_p,r_a] altitude bands have zero collision rate (Kessler), justifying the radial-range overlap gate in MOCAT-MC.
    Invoked in Application to Orbital Capacity to suppress false DC detections across altitude shells.
invented entities (2)
  • Double Cube (DC) dual-grid screening architecture independent evidence
    purpose: Recover boundary-crossing pairs at O(N) via primary and L/2-shifted bin indices without Euclidean neighbor search.
    Algorithmic construct, not a physical entity; independent handle is the measurable blindness rate β and synchronized 0% test.
  • Adaptive Resolution Double Cube (ARDC) two-tier volume rule
    purpose: Diagnostic that assigns L³/8 to dual-grid intersection pairs to expose volume-driven overestimation.
    Instrumentation variant; paper concludes aggressive volume reduction is not a viable calibration path.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Beyond the Cube: Overlapping Grid Methods for Debris Collision Risk Assessment." pith.science (2026). https://pith.science/paper/Q52L6HSN

@misc{pith2026260709634,
  author       = {Pith},
  title        = {Pith review of: Beyond the Cube: Overlapping Grid Methods for Debris Collision Risk Assessment},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/Q52L6HSN}},
  note         = {Machine review of arXiv:2607.09634}
}
abstract

The cube method reduces conjunction screening in orbital debris simulations to $\mathcal{O}(N)$ cost by evaluating only object pairs sharing the same grid cell at each snapshot, but systematically assigns zero collision probability to pairs separated by a cell boundary at that epoch, a failure known as boundary blindness. This paper introduces the Double Cube (DC) method, which recovers boundary-crossing conjunctions through a spatially shifted secondary grid using bin-index lookup alone, preserving $\mathcal{O}(N)$ complexity. Validated across 8,000 Monte Carlo seeds, DC reduces the blindness rate from $\beta_{\mathrm{Cube}} = 9.70\%$ to $\beta_{\mathrm{DC}} = 4.21\%$; a synchronized experiment confirms the residual is temporal in origin by reaching exactly $0.00\%$. Removing blindness reveals a systematic per-pair overestimation in the cube formula that blind zero-probability assignments had been masking, suppressing the overall predicted collision rate below the true rate. Two independent corrections are derived and validated: a power-law correction motivated by the Direct Simulation Monte Carlo kinetic theory analogy reduces the calibration error from $12.9\%$ to $1.9\%$ at $k = 1$ and $4.0\%$ at $k = 2$, bracketing perfect calibration from opposite sides; a parameter-free Gaussian correction derived from the pair-distance distribution geometry achieves a residual of $0.08\%$. Both corrections have been implemented in MOCAT-MC.

Figures

Figures reproduced from arXiv: 2607.09634 by the authors.

Figure 1
Figure 1. Comparison of three grid-based collision screening approaches. (a) Standard cube evaluates pairs sharing the same cell (dU = L 3 ); pairs in adjacent cells receive zero probability. (b) I-cube draws a search sphere of radius √ 3L centered on each object to recover boundary pairs at the cost of O(N2 ) distance computation. (c) Double Cube (DC) uses a shifted secondary grid offset by L/2 to recover the same pairs thro… view at source ↗
Figure 2
Figure 2. Illustration of the Double Cube (DC) dual-grid architecture. The primary grid (black) and shifted secondary grid (orange) are offset by L/2. Objects B and C fall in the adjacent primary grid cells and are missed by the standard cube method, but are captured together within the same secondary grid cell, recovering the conjunction. Object A lies outside the secondary grid coverage, while object D sits at the grid inte… view at source ↗
Figure 3
Figure 3. Pair-distance distribution for DC-detected pairs across 8,000 Rush-In Monte Carlo seeds. The dotted vertical line in both panels marks the Robbins mean ¯d = 0.6617L = 33.09 km. The correction factor α(dij ) ∈ (0, 1] is constructed to satisfy two requirements: it must equal unity for pairs at or below the Robbins mean (no suppression for close pairs) and decrease mono￾tonically for dij > µ. Consider the family α = mi… view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Correction factor α(dij ) for the three variants: power-law at k = 1 (solid), power-law at k = 2 (dashed), and Gaussian CDF (dash-dot) [PITH_FULL_IMAGE:figures/full_fig_p011_4.png]
Figure 5
Figure 5. Figure 5: Rush-In scenario geometry (Nobj = 200, L = 50 km) and convergence snapshot at t = 8 s grid snapshot is taken: cube and DC assign predicted probabilities to all detected pairs using the positions at that epoch. The binary ground-truth label Aij = ( 1 if pair (i, j) coll…
Figure 6
Figure 6. Figure 6: Pairwise reliability diagram for all five evaluated methods. The intercept values in [PITH_FULL_IMAGE:figures/full_fig_p017_6.png]
Figure 7
Figure 7. Figure 7: Representative 50-year MOCAT-MC projections for six correction configurations (4,000 seeds each, L = 50 km). 18 [PITH_FULL_IMAGE:figures/full_fig_p018_7.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

36 extracted references · 8 canonical work pages

  1. [1]

    Collision Frequency of Artificial Satellites: The Creation of a Debris Belt,

    D. J. Kessler and B. G. Cour-Palais, “Collision Frequency of Artificial Satellites: The Creation of a Debris Belt,”Journal of Geophysical Research: Space Physics, V ol. 83, No. A6, 1978, pp. 2637–2646. https://doi.org/10.1029/JA083iA06p02637

    doi:10.1029/ja083ia06p02637 1978

  2. [2]

    The Kessler Syndrome: Implications to Future Space Operations,

    D. J. Kessler, N. L. Johnson, J.-C. Liou, and M. Matney, “The Kessler Syndrome: Implications to Future Space Operations,”Advances in the Astronautical Sciences, Proceedings of the 33rd Annual AAS Rocky Mountain Guidance and Control Conference, V ol. 137, San Diego, CA, American Astronautical Society, Univelt Inc., 2010, pp. 47–62. Paper AAS 10-016https://...

    2010

  3. [3]

    ESA’s Annual Space Environment Report, 2025 Edition,

    European Space Agency, “ESA’s Annual Space Environment Report, 2025 Edition,” tech. rep., ESA Space Debris Office, European Space Operations Centre (ESOC), 2025.https://www.sdo.esoc. esa.int/environment_report/Space_Environment_Report_latest.pdf

    2025

  4. [4]

    Jonathan’s Space Report,

    J. C. McDowell, “Jonathan’s Space Report,”Jonathan’s Space Report, 2024.https:// planet4589.org/space/con/star/stats.html

    2024

  5. [5]

    FCC Partially Grants SpaceX Gen2 Broadband Satel- lite Application,

    Federal Communications Commission, “FCC Partially Grants SpaceX Gen2 Broadband Satel- lite Application,” Public Notice, FCC, 2022.https://www.fcc.gov/document/ fcc-partially-grants-spacex-gen2-broadband-satellite-application

    2022

  6. [6]

    An Active Debris Removal Parametric Study for LEO Environment Remediation,

    J.-C. Liou, “An Active Debris Removal Parametric Study for LEO Environment Remediation,”Ad- vances in Space Research, V ol. 47, No. 11, 2011, pp. 1865–1876.https://doi.org/10.1016/ j.asr.2011.02.003

    2011

  7. [7]

    Risk Index for the Optimal Ranking of Active Debris Removal Targets,

    S. Servadio, N. Simha, D. Gusmini, D. Jang, T. St. Francis, A. D’Ambrosio, G. Lavezzi, and R. Linares, “Risk Index for the Optimal Ranking of Active Debris Removal Targets,”Journal of Spacecraft and Rockets, V ol. 61, No. 2, 2024, pp. 407–420.https://arc.aiaa.org/doi/10.2514/1. A35752

    doi:10.2514/1 2024

  8. [8]

    New Monte Carlo Model for the Space Environment,

    D. Jang, D. Gusmini, P. M. Siew, A. D’Ambrosio, S. Servadio, P. Machuca, and R. Linares, “New Monte Carlo Model for the Space Environment,”Journal of Spacecraft and Rockets, V ol. 62, No. 4, 2025, pp. 1346–1367.https://arc.aiaa.org/doi/10.2514/1.A36137

    doi:10.2514/1.a36137 2025

Show all 36 references
  1. [9]

    MIT Monte Carlo Orbital Capacity Assessment Tool (MOCAT-MC),

    MIT ARC Lab, “MIT Monte Carlo Orbital Capacity Assessment Tool (MOCAT-MC),” GitHub Repos- itory, 2024.https://github.com/ARCLab-MIT/MOCAT-MC

    2024

  2. [10]

    LEGEND– A Three-Dimensional LEO-to- GEO Debris Evolutionary Model,

    J.-C. Liou, D. T. Hall, P. H. Krisko, and J. N. Opiela, “LEGEND– A Three-Dimensional LEO-to- GEO Debris Evolutionary Model,”Advances in Space Research, V ol. 34, No. 5, 2004, pp. 981–986. https://doi.org/10.1016/j.asr.2003.02.027

    2004 doi

  3. [11]

    Active Removal Study for On-Orbit De- bris Using DAMAGE,

    H. G. Lewis, G. G. Swinerd, R. J. Newland, and A. Saunders, “Active Removal Study for On-Orbit De- bris Using DAMAGE,”Proceedings of the 5th European Conference on Space Debris(H. Lacoste, ed.), ESA SP-672, Darmstadt, Germany, European Space Agency, ESA, 2009.https://conferenc...

    2009

  4. [12]

    DAMAGE: A Dedicated GEO Debris Model Framework,

    H. G. Lewis, G. Swinerd, N. Williams, and G. Gittins, “DAMAGE: A Dedicated GEO Debris Model Framework,”Proceedings of the 3rd European Conference on Space Debris, ESA SP-473, Darmstadt, Germany, European Space Agency, ESA, 2001.https://conference.sdo.esoc.esa.int/ proceedings/...

    2001

  5. [13]

    Optimal Active Debris Removal mission planning to inform policy decisions,

    N. Simha, S. Servadio, M. Lifson, G. Lavezzi, and R. Linares, “Optimal Active Debris Removal mission planning to inform policy decisions,”Acta Astronautica, V ol. 228, 2025, pp. 224–236.https:// doi.org/10.1016/j.actaastro.2024.11.050

    2025 doi

  6. [14]

    Development and Validation of an Enhanced Risk-Driven Index for Active Debris Removal Prioritization,

    Y . B. Medhin and S. Servadio, “Development and Validation of an Enhanced Risk-Driven Index for Active Debris Removal Prioritization,”Journal of Spacecraft and Rockets, June 2026.https:// doi.org/10.2514/1.A36649

    2026 doi

  7. [15]

    The Sustainability of the Leo Orbit Capacity via Risk-Driven Active De- bris Removal,

    Y . B. Medhin and S. Servadio, “The Sustainability of the Leo Orbit Capacity via Risk-Driven Active De- bris Removal,”arXiv preprint arXiv:2507.16101, July 2025.https://arxiv.org/abs/2507. 16101

    2025 arXiv

  8. [16]

    The All-Versus-All Low Earth Orbit Conjunction Problem,

    A. Lue, “The All-Versus-All Low Earth Orbit Conjunction Problem,”Proceedings of the Advanced Maui Optical and Space Surveillance Technologies Conference (AMOS), Maui, HI, Maui Economic Development Board, 2011, p. 260.https://amostech.com/TechnicalPapers/2011/ Astrodynamics/LUE.pdf

    2011

  9. [17]

    A High Performance Conjunction Analysis Technique for Cluster and Multi-Core Com- puters,

    E. R. George, “A High Performance Conjunction Analysis Technique for Cluster and Multi-Core Com- puters,”Proceedings of the Advanced Maui Optical and Space Surveillance Technologies Conference (AMOS), Maui, HI, Maui Economic Development Board, 2011, p. 270.https://ui.adsabs. h...

    2011

  10. [18]

    A New Approach to Evaluate Collision Prob- abilities Among Asteroids, Comets, and Kuiper Belt Objects,

    J.-C. Liou, D. J. Kessler, M. Matney, and G. Stansbery, “A New Approach to Evaluate Collision Prob- abilities Among Asteroids, Comets, and Kuiper Belt Objects,”Proceedings of the 34th Lunar and Planetary Science Conference, V ol. 34, Houston, TX, Lunar and Planetary Institute,...

    2003

  11. [19]

    Collision Activities in the Future Orbital Debris Environment,

    J.-C. Liou, “Collision Activities in the Future Orbital Debris Environment,”Advances in Space Re- search, V ol. 38, No. 9, 2006, pp. 2102–2106.https://doi.org/10.1016/j.asr.2005.06. 021

    2006 doi

  12. [20]

    Limitations of the CUBE Method for As- sessing Large Constellations,

    H. G. Lewis, S. Diserens, T. Maclay, and J. P. Sheehan, “Limitations of the CUBE Method for As- sessing Large Constellations,”Proceedings of the 1st International Orbital Debris Conference, Hous- ton, TX, Universities Space Research Association, 2019.https://ui.adsabs.harvard....

    2019

  13. [21]

    Assessing Collision Algorithms for the NewSpace Era,

    S. Diserens, H. G. Lewis, and J. Fliege, “Assessing Collision Algorithms for the NewSpace Era,”Jour- nal of Space Safety Engineering, V ol. 7, No. 3, 2020, pp. 274–281.https://doi.org/10.1016/ j.jsse.2020.07.021

    2020

  14. [22]

    Collision Risk Assessment with a

    J. R. Alarc ´on-Rodr´ıguez, F. Mart´ınez Fadrique, and H. Klinkrad, “Collision Risk Assessment with a ”Smart Sieve” Method,”Proceedings of the Joint ESA–NASA Space-Flight Safety Conference, V ol. 486 ofESA SP-486, ESA/ESOC, Darmstadt, Germany, European Space Agency, 2002, pp. ...

    2002

  15. [23]

    An Introduction to a New Space Debris Evolution Model: SOLEM,

    X.-w. Wang and J. Liu, “An Introduction to a New Space Debris Evolution Model: SOLEM,”Advances in Astronomy, V ol. 2019, 2019, p. 2738276.https://doi.org/10.1155/2019/2738276

    2019 doi

  16. [24]

    A Collision Probability Estimation Algorithm Used in the Space Debris Evolutionary Model,

    X.-W. Wang, J. Liu, and S.-X. Cui, “A Collision Probability Estimation Algorithm Used in the Space Debris Evolutionary Model,”arXiv preprint, 2019.https://arxiv.org/abs/1903.12335

    2019 arXiv

  17. [25]

    G. A. Bird,Molecular Gas Dynamics and the Direct Simulation of Gas Flows. Oxford, UK: Oxford University Press, 1994.https://doi.org/10.1093/oso/9780198561958.001.0001

    1994 doi

  18. [26]

    Cell Size Dependence of Transport Coefficients in Stochastic Particle Algorithms,

    F. J. Alexander, A. L. Garcia, and B. J. Alder, “Cell Size Dependence of Transport Coefficients in Stochastic Particle Algorithms,”Physics of Fluids, V ol. 10, No. 6, 1998, pp. 1540–1542.https: //doi.org/10.1063/1.869674

    1998 doi

  19. [27]

    Erratum:

    F. J. Alexander, A. L. Garcia, and B. J. Alder, “Erratum: ”Cell Size Dependence of Transport Coeffi- cients in Stochastic Particle Algorithms” [Phys. Fluids10, 1540 (1998)],”Physics of Fluids, V ol. 12, No. 3, 2000, p. 731.https://doi.org/10.1063/1.870278

    1998 doi

  20. [28]

    Convergence Behavior of a New DSMC Algorithm,

    M. A. Gallis, J. R. Torczynski, D. J. Rader, and G. A. Bird, “Convergence Behavior of a New DSMC Algorithm,”Journal of Computational Physics, V ol. 228, No. 12, 2009, pp. 4532–4548.https: //doi.org/10.1016/j.jcp.2009.03.021

    2009 doi

  21. [29]

    Accuracy and Efficiency of the Sophisti- cated Direct Simulation Monte Carlo Algorithm for Simulating Noncontinuum Gas Flows,

    G. A. Bird, M. A. Gallis, J. R. Torczynski, and D. J. Rader, “Accuracy and Efficiency of the Sophisti- cated Direct Simulation Monte Carlo Algorithm for Simulating Noncontinuum Gas Flows,”Physics of Fluids, V ol. 21, No. 1, 2009, p. 017103.https://doi.org/10.1063/1.3067865

    2009 doi

  22. [30]

    Effect of Collision-Partner Selection Schemes on the Accuracy and Efficiency of the Direct Simulation Monte Carlo Method,

    M. A. Gallis and J. R. Torczynski, “Effect of Collision-Partner Selection Schemes on the Accuracy and Efficiency of the Direct Simulation Monte Carlo Method,”International Journal for Numerical Methods in Fluids, V ol. 67, No. 8, 2011, pp. 1057–1072.https://doi.org/10.1002/fld.2409

    2011 doi

  23. [31]

    Accuracy and Convergence of a New DSMC Algorithm,

    M. A. Gallis, J. R. Torczynski, D. J. Rader, and G. A. Bird, “Accuracy and Convergence of a New DSMC Algorithm,”40th Thermophysics Conference, Seattle, Washington, American Institute of Aero- nautics and Astronautics, June 2008. AIAA Paper 2008-3913.https://doi.org/10.2514/6. ...

    2008 doi

  24. [32]

    Probability of Collision Between Space Objects,

    M. R. Akella and K. T. Alfriend, “Probability of Collision Between Space Objects,”Journal of Guid- ance, Control, and Dynamics, V ol. 23, No. 5, 2000, pp. 769–772.https://arc.aiaa.org/doi/ 10.2514/2.4611

    doi

  25. [33]

    Average Distance between Two Points in a Box (Solution to Elementary Problem E2629),

    D. P. Robbins and T. S. Bolis, “Average Distance between Two Points in a Box (Solution to Elementary Problem E2629),”American Mathematical Monthly, V ol. 85, No. 4, 1978, pp. 277–278.https: //www.jstor.org/stable/2321177

    1978

  26. [34]

    Recursive polynomial minimum mean-square error estimation with appli- cations to orbit determination,

    S. Servadio and R. Zanetti, “Recursive polynomial minimum mean-square error estimation with appli- cations to orbit determination,”Journal of Guidance, Control, and Dynamics, V ol. 43, No. 5, 2020, pp. 939–954.https://doi.org/10.2514/1.G004544

    2020 doi

  27. [35]

    Analysis and Validation of the CUBE Algorithm: Assessing Gas Kinetic Theory to Model Collision Risk in Long-Term Debris Propagations,

    G. Facchinetti, “Analysis and Validation of the CUBE Algorithm: Assessing Gas Kinetic Theory to Model Collision Risk in Long-Term Debris Propagations,” Master’s thesis, Politecnico di Milano, 2024. https://www.researchgate.net/publication/382464290

    2024

  28. [36]

    Derivation of the Collision Probability between Orbiting Objects: The Lifetimes of Jupiter’s Outer Moons,

    D. J. Kessler, “Derivation of the Collision Probability between Orbiting Objects: The Lifetimes of Jupiter’s Outer Moons,”Icarus, V ol. 48, No. 1, 1981, pp. 39–48.https://doi.org/10.1016/ 0019-1035(81)90151-2. 21

    1981

Pith tools

Reviewed July 13, 2026 · model on record in the stance chip above.