<p>We consider real tensors of order <i>D</i>, that is <i>D</i>-dimensional arrays of real numbers <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="11005_2025_1983_Article_IEq1.gif" Format="GIF" Height="18" Rendition="HTML" Resolution="72" Type="Linedraw" Width="61" /> </InlineMediaObject> <EquationSource Format="TEX">\(T_{a^1a^2 \dots a^D}\)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>T</mi> <mrow> <msup> <mi>a</mi> <mn>1</mn> </msup> <msup> <mi>a</mi> <mn>2</mn> </msup> <mo>⋯</mo> <msup> <mi>a</mi> <mi>D</mi> </msup> </mrow> </msub> </math></EquationSource> </InlineEquation>, where each index <InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="11005_2025_1983_Article_IEq2.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="17" /> </InlineMediaObject> <EquationSource Format="TEX">\(a^c\)</EquationSource> <EquationSource Format="MATHML"><math> <msup> <mi>a</mi> <mi>c</mi> </msup> </math></EquationSource> </InlineEquation> can take <i>N</i> values. The tensor entries <InlineEquation ID="IEq3"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="11005_2025_1983_Article_IEq1.gif" Format="GIF" Height="18" Rendition="HTML" Resolution="72" Type="Linedraw" Width="61" /> </InlineMediaObject> <EquationSource Format="TEX">\(T_{a^1a^2 \dots a^D}\)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>T</mi> <mrow> <msup> <mi>a</mi> <mn>1</mn> </msup> <msup> <mi>a</mi> <mn>2</mn> </msup> <mo>⋯</mo> <msup> <mi>a</mi> <mi>D</mi> </msup> </mrow> </msub> </math></EquationSource> </InlineEquation> have no symmetry properties under permutations of the indices. The invariant polynomials built out of the tensor entries are called trace invariants. We prove that for a Gaussian random tensor with <InlineEquation ID="IEq4"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="11005_2025_1983_Article_IEq4.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="47" /> </InlineMediaObject> <EquationSource Format="TEX">\(D\ge 3\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi>D</mi> <mo>≥</mo> <mn>3</mn> </mrow> </math></EquationSource> </InlineEquation> indices (that is such that the entries <InlineEquation ID="IEq5"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="11005_2025_1983_Article_IEq1.gif" Format="GIF" Height="18" Rendition="HTML" Resolution="72" Type="Linedraw" Width="61" /> </InlineMediaObject> <EquationSource Format="TEX">\(T_{a^1a^2 \dots a^D}\)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>T</mi> <mrow> <msup> <mi>a</mi> <mn>1</mn> </msup> <msup> <mi>a</mi> <mn>2</mn> </msup> <mo>⋯</mo> <msup> <mi>a</mi> <mi>D</mi> </msup> </mrow> </msub> </math></EquationSource> </InlineEquation> are independent identically distributed Gaussian random variables) the cumulant, or connected expectation, of a product of trace invariants is <i>not always</i> suppressed in scaling in <i>N</i> with respect to the product of the expectations of the individual invariants. Said otherwise, <i>not all</i> the multi-trace expectations factor at large <i>N</i> in terms of the single-trace ones and the Gaussian scaling is <i>not</i> subadditive on the connected components. This is in stark contrast to the <InlineEquation ID="IEq6"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="11005_2025_1983_Article_IEq6.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="46" /> </InlineMediaObject> <EquationSource Format="TEX">\(D=2\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi>D</mi> <mo>=</mo> <mn>2</mn> </mrow> </math></EquationSource> </InlineEquation> case of random matrices in which the multi-trace expectations always factor at large <i>N</i>. The best one can do for <InlineEquation ID="IEq7"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="11005_2025_1983_Article_IEq4.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="47" /> </InlineMediaObject> <EquationSource Format="TEX">\(D\ge 3\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi>D</mi> <mo>≥</mo> <mn>3</mn> </mrow> </math></EquationSource> </InlineEquation> is to identify restricted families of invariants for which the large <i>N</i> factorization holds and we check that this indeed happens when restricting to the family of melonic observables, the dominant family in the large <i>N</i> limit.</p>

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The large N factorization does not hold for arbitrary multi-trace observables in random tensors

  • Razvan Gurau,
  • Felix Joos,
  • Benjamin Sudakov

摘要

We consider real tensors of order D, that is D-dimensional arrays of real numbers \(T_{a^1a^2 \dots a^D}\) T a 1 a 2 a D , where each index \(a^c\) a c can take N values. The tensor entries \(T_{a^1a^2 \dots a^D}\) T a 1 a 2 a D have no symmetry properties under permutations of the indices. The invariant polynomials built out of the tensor entries are called trace invariants. We prove that for a Gaussian random tensor with \(D\ge 3\) D 3 indices (that is such that the entries \(T_{a^1a^2 \dots a^D}\) T a 1 a 2 a D are independent identically distributed Gaussian random variables) the cumulant, or connected expectation, of a product of trace invariants is not always suppressed in scaling in N with respect to the product of the expectations of the individual invariants. Said otherwise, not all the multi-trace expectations factor at large N in terms of the single-trace ones and the Gaussian scaling is not subadditive on the connected components. This is in stark contrast to the \(D=2\) D = 2 case of random matrices in which the multi-trace expectations always factor at large N. The best one can do for \(D\ge 3\) D 3 is to identify restricted families of invariants for which the large N factorization holds and we check that this indeed happens when restricting to the family of melonic observables, the dominant family in the large N limit.