Reuse & Permissions

It is not necessary to obtain permission to reuse this article or its components as it is available under the terms of the Creative Commons Attribution 4.0 International license. This license permits unrestricted use, distribution, and reproduction in any medium, provided attribution to the author(s) and the published article's title, journal citation, and DOI are maintained. Please note that some figures may have been included with permission from other third parties. It is your responsibility to obtain the proper permission from the rights holder directly for these figures.

Export citation

Export citation

Choose format for download:

Download Citation
  • Open Access

Mean First Passage Times of Higher-Dimensional Velocity Jump Processes

Maria R. D’Orsogna*

Alan E. Lindsay

Thomas Hillen

  • Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, Indiana, 46656, USA

  • *Contact author: dorsogna@csun.edu

Phys. Rev. Lett. 136, 247102 – Published 18 June, 2026

DOI: https://doi.org/10.1103/9hhg-2ddm

Abstract

First passage phenomena arise across physics, biology, and finance when stochastic processes first reach a threshold, triggering downstream events. Examples include the irreversible exit from a domain, a biochemical reaction, and a financial selloff. While typical formulations involve diffusive motion, many stochastic processes are better described as velocity jump processes, characterized by persistent motion interrupted by stochastic velocity changes. Despite their ubiquity, first passage properties of velocity jump processes remain underdeveloped in higher dimensions, especially under directional bias. We present a general framework to estimate the mean first passage time (MFPT) and higher moments of the survival probability for fixed-speed velocity jump processes where possible reorientations range from strong alignment to full angular anisotropy. For low Knudsen numbers, when the mean free path is small compared to the distance to the target, we derive a universal form for the MFPT in which two bias functions encode broad classes of angular distributions, including von Mises-Fisher, wrapped Cauchy, and elliptical families. In the narrow-capture limit of a vanishingly small target, directional persistence induces anomalous scaling, including regimes where the MFPT remains finite whereas standard diffusion would predict divergence. Finally, we obtain a Langevin representation that accurately reproduces first passage statistics. Analytical predictions are confirmed by numerical simulations.

View figure in article

Physics Subject Headings (PhySH)

Article Text

Supplemental Material

References (69)

  1. N. G. van Kampen, Stochastic Processes in Physics and Chemistry (North-Holland, Amsterdam, 1981).
  2. R. Klages, G. Radons, and I. M. Sokolov, Anomalous Transport (John Wiley & Sons, New York, 2008).
  3. P. Hänggi, P. Talkner, and M. Borkovec, Rev. Mod. Phys. 62, 251 (1990).
  4. S. A. Iyaniwura and Z. Peng, New J. Phys. 27, 104401 (2025).
  5. P. C. Bressloff and J. M. Newby, Phys. Rev. E 83, 061139 (2011).
  6. A. Scacchi and A. Sharma, Mol. Phys. 116, 460 (2018).
  7. A. Dhar, A. Kundu, S. N. Majumdar, S. Sabhapandit, and G. Schehr, Phys. Rev. E 99, 032132 (2019).
  8. L. Angelani, R. Di Lionardo, and M. Paoluzzi, Eur. Phys. J. E 37, 59 (2014).
  9. J. Perelló, M. Gutiérrez-Roig, and J. Masoliver, Phys. Rev. E 84, 066110 (2011).
  10. J. Masoliver and J. Perelló, Phys. Rev. E 78, 056104 (2008).
  11. T. R. Bielecki and M. Rutkowski, in Credit Risk: Modeling, Valuation and Hedging (Springer Finance, Berlin, Heidelberg, 2004).
  12. V. Kurella, J. C. Tzou, D. Coombs, and M. Ward, Bull. Math. Biol. 77, 83 (2015).
  13. A. C. Costa, G. Sridhar, C. Wyart, and M. Vergassola, PRX Life 2, 023001 (2024).
  14. P. Fauchald and T. Torkild, Ecology 84, 282 (2003).
  15. O. Bénichou, C. Loverdo, M. Moreau, and R. Voituriez, Rev. Mod. Phys. 83, 81 (2011).
  16. O. Heaviside, Electrical Papers of Oliver Heaviside, Volume I (Chelsea, New York, 1970).
  17. L. Angelani, J. Phys. A 48, 495003 (2015).
  18. T. Hillen and K. J. Painter, in Dispersal, Individual Movement and Spatial Ecology (Springer, New York, 2013), pp. 177–222.
  19. T. Hillen, Math. Models Methods Appl. Sci. 12, 1007 (2002).
  20. A. Datta, C. Beta, and R. Großmann, Phys. Rev. Res. 6, 043281 (2024).
  21. S. Redner, A Guide to First-Passage Processes (Cambridge University Press, Cambridge, England, 2001).
  22. K. R. Ghusinga, J. J. Dennehy, and A. Singh, Proc. Natl. Acad. Sci. U.S.A. 114, 693 (2017).
  23. M. R. D’Orsogna and T. Chou, PLoS One 4, e8165 (2009).
  24. S. A. Nowak and T. Chou, Biophys. J. 96, 2624 (2009).
  25. P. C. Bressloff and J. M. Newby, Rev. Mod. Phys. 85, 135 (2013).
  26. S. Condamin, V. Tejedor, R. Voituriez, O. Bénichou, and J. Klafter, Proc. Natl. Acad. Sci. U.S.A. 105, 5675 (2008).
  27. C. W. Gardiner, Handbook of Stochastic Methods: For Physics, Chemistry and the Natural Sciences (Springer, Berlin, 2004).
  28. B. M. S. Arani, S. R. Carpenter, L. Lahti, E. H. van Nes, and M. Scheffer, Science 372, eaay4895 (2021).
  29. S. Mao, T. Chou, and M. R. D’Orsogna, Math. Biosci. 372, 109184 (2024).
  30. J. Klinger, R. Voituriez, and O. Bénichou, Phys. Rev. E 107, 054109 (2023).
  31. B. A. Camley and W. J. Rappel, Phys. Rev. E 89, 062705 (2014).
  32. A. Vezzani and R. Burioni, Phys. Rev. Lett. 132, 187101 (2024).
  33. R. Artuso, G. Cristadoro, M. Onofri, and M. Radice, J. Stat. Mech. (2018) 083209.
  34. F. J. Sevilla, A. V. Arzola, and E. P. Cital, Phys. Rev. E 99, 012145 (2019).
  35. D. S. Grebenkov, Phys. Rev. Lett. 117, 260201 (2016).
  36. F. Mori, P. Le Doussal, S. N. Majumdar, and G. Schehr, Phys. Rev. Lett. 124, 090603 (2020).
  37. J. F. Rupprecht, O. Bénichou, and R. Voituriez, Phys. Rev. E 94, 012117 (2016).
  38. See Supplemental Material at http://link.aps.org/supplemental/10.1103/9hhg-2ddm for the MFPT derivation in the low Knudsen limit, boundary conditions, diffusion tensor eigenstructure, validity range of the approximation, and physical applications.
  39. T. Hillen, M. R. D’Orsogna, J. Mantooth, and A. E. Lindsay, SIAM Appl. Math. 85, 78 (2025).
  40. P. C. Hemmer, Physica (Amsterdam) 27A, 79 (1961).
  41. M. Kac, Rocky Mt. J. Math. 4, 497 (1974).
  42. M. R. D’Orsogna, M. Suchard, and T. Chou, Phys. Rev. E 68, 021925 (2003).
  43. G. H. Weiss, Aspects and Applications of the Random Walk (North-Holland, Amsterdam, 1994).
  44. G. H. Weiss, Physica (Amsterdam) 311A, 381 (2002).
  45. C. M. Bender and S. A. Orszag, Advanced Mathematical Methods for Scientists and Engineers: Asymptotic Methods and Perturbation Theory (Springer, New York, 1999).
  46. T. Hillen, K. J. Painter, A. C. Swan, and A. Murtha, Math. Biosci. Eng. 14, 673 (2017).
  47. I. Bica, T. Hillen, and K. J. Painter, J. Theor. Biol. 427, 77 (2017).
  48. A. C. Swan, T. Hillen, J. Bowman, and A. Murtha, Bull. Math. Biol. 80, 1259 (2018).
  49. H. Wu, B. L. Li, T. A. Springer, and W. H. Neill, Ecol. Model. 132, 115 (2000).
  50. S. Chakraborty, T. Kolokolnikov, and A. E. Lindsay, arXiv:2506.17173.
  51. J. M. Stockie, SIAM Rev. 53, 349 (2011).
  52. F. Höfling and A. V. Straube, Phys. Rev. Res. 7, 043034 (2025).
  53. T. Narazaki et al., iScience 24, 102221 (2021).
  54. M. N. Popescu, W. E. Uspal, C. Bechinger, and P. Fischer, Nano Lett. 18, 5345 (2018).
  55. H. Karani, G. E. Pradillo, and P. M. Vlahovska, Phys. Rev. Lett. 123, 208002 (2019).
  56. E. Lauga, W. R. Di Luzio, G. M. Whitesides, and H. A. Stone, Biophys. J. 90, 400 (2006).
  57. F. Kümmel, B. ten Hagen, R. Wittkowski, I. Buttinoni, R. Eichhorn, G. Volpe, H. Löwen, and C. Bechinger, Phys. Rev. Lett. 110, 198302 (2013).
  58. T. C. Schneirla, Am. Mus. Novit. 1253, 1 (1944), https://digitallibrary.amnh.org/items/5f80439c-e431-4c18-9e5e-81fed85fce39.
  59. C. M. Buness, A. Rana, C. C. Maass, and R. Dey, Phys. Rev. Lett. 133, 158301 (2024).
  60. P. J. Mlynarczyk and S. M. Abel, Phys. Rev. E 99, 022406 (2019).
  61. H. W. McKenzie, M. A. Lewis, and E. H. Merrill, Bull. Math. Biol. 71, 107 (2009).
  62. T. M. Nieuwenhuizen, S. Klumpp, and R. Lipowsky, Phys. Rev. E 6, 061911 (2004).
  63. A. Codutti, K. Bente, D. Faivre, and S. Klumpp, PLoS Comput. Biol. 15, e1007548 (2019).
  64. E. Boissard, P. Degond, and S. Motsch, J. Math. Biol. 66, 1267 (2013).
  65. V. Sourjik and N. S. Wingreen, Curr. Opin. Cell Biol. 24, 262 (2012).
  66. K. Saito, R. Kawano, C. Sadamatsu, Y. Iwashita, and Y. Kimura, Phys. Rev. E 111, 045409 (2025).
  67. W. Wang, W. Duan, S. Ahmed, T. E. Mallouk, and A. Sen, Nano Today 8, 531 (2013).
  68. A. Vezzani, E. Barkai, and R. Burioni, Phys. Rev. E 100, 012108 (2019).
  69. M. R. D’Orsogna, A. E. Lindsay, and T. Hillen, MFPT code repository, https://github.com/dorsogna/MFPT, accessed March 30, 2026.

Outline

Information

Sign In to Your Journals Account

Filter

Filter

Article Lookup

Enter a citation