<p>Given a compact planar region <Emphasis Type="BoldItalic">A</Emphasis>, let <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\varvec{\tau _{A}}\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <msub> <mi mathvariant="bold-italic">τ</mi> <mi mathvariant="bold-italic">A</mi> </msub> </mrow> </math></EquationSource> </InlineEquation> be the (random) time it takes for the Johnson-Mehl tessellation of <Emphasis Type="BoldItalic">A</Emphasis> to be complete, i.e. the time for <Emphasis Type="BoldItalic">A</Emphasis> to be fully covered by a spatial birth-growth process in <Emphasis Type="BoldItalic">A</Emphasis> with seeds arriving as a unit-intensity Poisson point process in <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\varvec{A \times [0,\infty )}\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi mathvariant="bold-italic">A</mi> <mo mathvariant="bold">×</mo> <mo mathvariant="bold" stretchy="false">[</mo> <mn mathvariant="bold">0</mn> <mo mathvariant="bold">,</mo> <mi mathvariant="bold-italic">∞</mi> <mo mathvariant="bold" stretchy="false">)</mo> </mrow> </math></EquationSource> </InlineEquation>, where upon arrival each seed grows at unit rate in all directions. We show that if <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(\varvec{\partial A}\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi mathvariant="bold-italic">∂</mi> <mi mathvariant="bold-italic">A</mi> </mrow> </math></EquationSource> </InlineEquation> is smooth or polygonal then <InlineEquation ID="IEq4"> <EquationSource Format="TEX">\(\mathbb {P}[\varvec{\pi \tau _{sA}^3 - 6 \log s - 4 \log \log s \le x}]\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi mathvariant="double-struck">P</mi> <mo stretchy="false">[</mo> <mrow> <mi mathvariant="bold-italic">π</mi> <msubsup> <mi mathvariant="bold-italic">τ</mi> <mrow> <mi mathvariant="bold-italic">sA</mi> </mrow> <mn mathvariant="bold">3</mn> </msubsup> <mo mathvariant="bold">-</mo> <mn mathvariant="bold">6</mn> <mo mathvariant="bold">log</mo> <mi mathvariant="bold-italic">s</mi> <mo mathvariant="bold">-</mo> <mn mathvariant="bold">4</mn> <mo mathvariant="bold">log</mo> <mo mathvariant="bold">log</mo> <mi mathvariant="bold-italic">s</mi> <mo mathvariant="bold">≤</mo> <mi mathvariant="bold-italic">x</mi> </mrow> <mo stretchy="false">]</mo> </mrow> </math></EquationSource> </InlineEquation> tends to <b>exp</b><InlineEquation ID="IEq5"> <EquationSource Format="TEX">\(\varvec{(-(\frac{81}{4\pi })^{1/3} |A|e^{-x/3} - (\frac{9}{2\pi ^2})^{1/3} |\partial {\textbf {A}}| e^{-x/6})}\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mo mathvariant="bold" stretchy="false">(</mo> <mo mathvariant="bold">-</mo> <msup> <mrow> <mo mathvariant="bold" stretchy="false">(</mo> <mfrac> <mn mathvariant="bold">81</mn> <mrow> <mn mathvariant="bold">4</mn> <mi mathvariant="bold-italic">π</mi> </mrow> </mfrac> <mo mathvariant="bold" stretchy="false">)</mo> </mrow> <mrow> <mn mathvariant="bold">1</mn> <mo mathvariant="bold" stretchy="false">/</mo> <mn mathvariant="bold">3</mn> </mrow> </msup> <mo mathvariant="bold" stretchy="false">|</mo> <mi mathvariant="bold-italic">A</mi> <mo mathvariant="bold" stretchy="false">|</mo> <msup> <mi mathvariant="bold-italic">e</mi> <mrow> <mo mathvariant="bold">-</mo> <mi mathvariant="bold-italic">x</mi> <mo mathvariant="bold" stretchy="false">/</mo> <mn mathvariant="bold">3</mn> </mrow> </msup> <mo mathvariant="bold">-</mo> <msup> <mrow> <mo mathvariant="bold" stretchy="false">(</mo> <mfrac> <mn mathvariant="bold">9</mn> <mrow> <mn mathvariant="bold">2</mn> <msup> <mi mathvariant="bold-italic">π</mi> <mn mathvariant="bold">2</mn> </msup> </mrow> </mfrac> <mo mathvariant="bold" stretchy="false">)</mo> </mrow> <mrow> <mn mathvariant="bold">1</mn> <mo mathvariant="bold" stretchy="false">/</mo> <mn mathvariant="bold">3</mn> </mrow> </msup> <mo mathvariant="bold" stretchy="false">|</mo> <mi mathvariant="bold-italic">∂</mi> <mi mathvariant="bold">A</mi> <mo mathvariant="bold" stretchy="false">|</mo> <msup> <mi mathvariant="bold-italic">e</mi> <mrow> <mo mathvariant="bold">-</mo> <mi mathvariant="bold-italic">x</mi> <mo mathvariant="bold" stretchy="false">/</mo> <mn mathvariant="bold">6</mn> </mrow> </msup> <mo mathvariant="bold" stretchy="false">)</mo> </mrow> </math></EquationSource> </InlineEquation> in the large-<Emphasis Type="BoldItalic">s</Emphasis> limit; the second term in the exponent is due to boundary effects, the importance of which was not recognized in earlier work on this model. We present similar results in higher dimensions (where boundary effects dominate). These results are derived using new results on the asymptotic probability of covering <Emphasis Type="BoldItalic">A</Emphasis> with a high-intensity spherical Poisson Boolean model <i>restricted to</i> <Emphasis Type="BoldItalic">A</Emphasis> with grains having iid small random radii, which generalize recent work of the first author that dealt only with grains of deterministic radius.</p>

错误:搜索内容不能为空,请输入英文关键词
错误:关键词超出字数限制,请精简
高级检索

Random coverage from within with variable radii, and Johnson-Mehl cover times

  • Mathew D. Penrose,
  • Frankie Higgs

摘要

Given a compact planar region A, let \(\varvec{\tau _{A}}\) τ A be the (random) time it takes for the Johnson-Mehl tessellation of A to be complete, i.e. the time for A to be fully covered by a spatial birth-growth process in A with seeds arriving as a unit-intensity Poisson point process in \(\varvec{A \times [0,\infty )}\) A × [ 0 , ) , where upon arrival each seed grows at unit rate in all directions. We show that if \(\varvec{\partial A}\) A is smooth or polygonal then \(\mathbb {P}[\varvec{\pi \tau _{sA}^3 - 6 \log s - 4 \log \log s \le x}]\) P [ π τ sA 3 - 6 log s - 4 log log s x ] tends to exp \(\varvec{(-(\frac{81}{4\pi })^{1/3} |A|e^{-x/3} - (\frac{9}{2\pi ^2})^{1/3} |\partial {\textbf {A}}| e^{-x/6})}\) ( - ( 81 4 π ) 1 / 3 | A | e - x / 3 - ( 9 2 π 2 ) 1 / 3 | A | e - x / 6 ) in the large-s limit; the second term in the exponent is due to boundary effects, the importance of which was not recognized in earlier work on this model. We present similar results in higher dimensions (where boundary effects dominate). These results are derived using new results on the asymptotic probability of covering A with a high-intensity spherical Poisson Boolean model restricted to A with grains having iid small random radii, which generalize recent work of the first author that dealt only with grains of deterministic radius.