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ARGUS distribution

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(March 2011)
ARGUS
Probability density function

c = 1.
Cumulative distribution function

c = 1.
Parameters c > 0 {\displaystyle c>0} {\displaystyle c>0} cut-off (real)
χ > 0 {\displaystyle \chi >0} {\displaystyle \chi >0} curvature (real)
Support x ( 0 , c ) {\displaystyle x\in (0,c)\!} {\displaystyle x\in (0,c)\!}
PDF see text
CDF see text
Mean μ = c π / 8 χ e χ 2 4 I 1 ( χ 2 4 ) Ψ ( χ ) {\displaystyle \mu =c{\sqrt {\pi /8}}\;{\frac {\chi e^{-{\frac {\chi ^{2}}{4}}}I_{1}({\tfrac {\chi ^{2}}{4}})}{\Psi (\chi )}}} {\displaystyle \mu =c{\sqrt {\pi /8}}\;{\frac {\chi e^{-{\frac {\chi ^{2}}{4}}}I_{1}({\tfrac {\chi ^{2}}{4}})}{\Psi (\chi )}}}

where I1 is the Modified Bessel function of the first kind of order 1, and Ψ ( x ) {\displaystyle \Psi (x)} {\displaystyle \Psi (x)} is given in the text.
Mode c 2 χ ( χ 2 2 ) + χ 4 + 4 {\displaystyle {\frac {c}{{\sqrt {2}}\chi }}{\sqrt {(\chi ^{2}-2)+{\sqrt {\chi ^{4}+4}}}}} {\displaystyle {\frac {c}{{\sqrt {2}}\chi }}{\sqrt {(\chi ^{2}-2)+{\sqrt {\chi ^{4}+4}}}}}
Variance c 2 ( 1 3 χ 2 + χ ϕ ( χ ) Ψ ( χ ) ) μ 2 {\displaystyle c^{2}\!\left(1-{\frac {3}{\chi ^{2}}}+{\frac {\chi \phi (\chi )}{\Psi (\chi )}}\right)-\mu ^{2}} {\displaystyle c^{2}\!\left(1-{\frac {3}{\chi ^{2}}}+{\frac {\chi \phi (\chi )}{\Psi (\chi )}}\right)-\mu ^{2}}

In physics, the ARGUS distribution, named after the particle physics experiment ARGUS,[1] is the probability distribution of the reconstructed invariant mass of a decayed particle candidate in continuum background[clarification needed ].

Definition

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The probability density function (pdf) of the ARGUS distribution is:

f ( x ; χ , c ) = χ 3 2 π Ψ ( χ ) x c 2 1 x 2 c 2 exp { 1 2 χ 2 ( 1 x 2 c 2 ) } , {\displaystyle f(x;\chi ,c)={\frac {\chi ^{3}}{{\sqrt {2\pi }},円\Psi (\chi )}}\cdot {\frac {x}{c^{2}}}{\sqrt {1-{\frac {x^{2}}{c^{2}}}}}\exp {\bigg \{}-{\frac {1}{2}}\chi ^{2}{\Big (}1-{\frac {x^{2}}{c^{2}}}{\Big )}{\bigg \}},} {\displaystyle f(x;\chi ,c)={\frac {\chi ^{3}}{{\sqrt {2\pi }},円\Psi (\chi )}}\cdot {\frac {x}{c^{2}}}{\sqrt {1-{\frac {x^{2}}{c^{2}}}}}\exp {\bigg \{}-{\frac {1}{2}}\chi ^{2}{\Big (}1-{\frac {x^{2}}{c^{2}}}{\Big )}{\bigg \}},}

for 0 x < c {\displaystyle 0\leq x<c} {\displaystyle 0\leq x<c}. Here χ {\displaystyle \chi } {\displaystyle \chi } and c {\displaystyle c} {\displaystyle c} are parameters of the distribution and

Ψ ( χ ) = Φ ( χ ) χ ϕ ( χ ) 1 2 , {\displaystyle \Psi (\chi )=\Phi (\chi )-\chi \phi (\chi )-{\tfrac {1}{2}},} {\displaystyle \Psi (\chi )=\Phi (\chi )-\chi \phi (\chi )-{\tfrac {1}{2}},}

where Φ ( x ) {\displaystyle \Phi (x)} {\displaystyle \Phi (x)} and ϕ ( x ) {\displaystyle \phi (x)} {\displaystyle \phi (x)} are the cumulative distribution and probability density functions of the standard normal distribution, respectively.

Cumulative distribution function

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The cumulative distribution function (cdf) of the ARGUS distribution is

F ( x ) = 1 Ψ ( χ 1 x 2 / c 2 ) Ψ ( χ ) {\displaystyle F(x)=1-{\frac {\Psi \left(\chi {\sqrt {1-x^{2}/c^{2}}}\right)}{\Psi (\chi )}}} {\displaystyle F(x)=1-{\frac {\Psi \left(\chi {\sqrt {1-x^{2}/c^{2}}}\right)}{\Psi (\chi )}}}.

Parameter estimation

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Parameter c is assumed to be known (the kinematic limit of the invariant mass distribution), whereas χ can be estimated from the sample X1, ..., Xn using the maximum likelihood approach. The estimator is a function of sample second moment, and is given as a solution to the non-linear equation

1 3 χ 2 + χ ϕ ( χ ) Ψ ( χ ) = 1 n i = 1 n x i 2 c 2 {\displaystyle 1-{\frac {3}{\chi ^{2}}}+{\frac {\chi \phi (\chi )}{\Psi (\chi )}}={\frac {1}{n}}\sum _{i=1}^{n}{\frac {x_{i}^{2}}{c^{2}}}} {\displaystyle 1-{\frac {3}{\chi ^{2}}}+{\frac {\chi \phi (\chi )}{\Psi (\chi )}}={\frac {1}{n}}\sum _{i=1}^{n}{\frac {x_{i}^{2}}{c^{2}}}}.

The solution exists and is unique, provided that the right-hand side is greater than 0.4; the resulting estimator χ ^ {\displaystyle \scriptstyle {\hat {\chi }}} {\displaystyle \scriptstyle {\hat {\chi }}} is consistent and asymptotically normal.

Generalized ARGUS distribution

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Sometimes a more general form is used to describe a more peaking-like distribution:

f ( x ) = 2 p χ 2 ( p + 1 ) Γ ( p + 1 ) Γ ( p + 1 , 1 2 χ 2 ) x c 2 ( 1 x 2 c 2 ) p exp { 1 2 χ 2 ( 1 x 2 c 2 ) } , 0 x c , c > 0 , χ > 0 , p > 1 {\displaystyle f(x)={\frac {2^{-p}\chi ^{2(p+1)}}{\Gamma (p+1)-\Gamma (p+1,,円{\tfrac {1}{2}}\chi ^{2})}}\cdot {\frac {x}{c^{2}}}\left(1-{\frac {x^{2}}{c^{2}}}\right)^{p}\exp \left\{-{\frac {1}{2}}\chi ^{2}\left(1-{\frac {x^{2}}{c^{2}}}\right)\right\},\qquad 0\leq x\leq c,\qquad c>0,,円\chi >0,,円p>-1} {\displaystyle f(x)={\frac {2^{-p}\chi ^{2(p+1)}}{\Gamma (p+1)-\Gamma (p+1,,円{\tfrac {1}{2}}\chi ^{2})}}\cdot {\frac {x}{c^{2}}}\left(1-{\frac {x^{2}}{c^{2}}}\right)^{p}\exp \left\{-{\frac {1}{2}}\chi ^{2}\left(1-{\frac {x^{2}}{c^{2}}}\right)\right\},\qquad 0\leq x\leq c,\qquad c>0,,円\chi >0,,円p>-1}
F ( x ) = Γ ( p + 1 , 1 2 χ 2 ( 1 x 2 c 2 ) ) Γ ( p + 1 , 1 2 χ 2 ) Γ ( p + 1 ) Γ ( p + 1 , 1 2 χ 2 ) , 0 x c , c > 0 , χ > 0 , p > 1 {\displaystyle F(x)={\frac {\Gamma \left(p+1,,円{\tfrac {1}{2}}\chi ^{2}\left(1-{\frac {x^{2}}{c^{2}}}\right)\right)-\Gamma (p+1,,円{\tfrac {1}{2}}\chi ^{2})}{\Gamma (p+1)-\Gamma (p+1,,円{\tfrac {1}{2}}\chi ^{2})}},\qquad 0\leq x\leq c,\qquad c>0,,円\chi >0,,円p>-1} {\displaystyle F(x)={\frac {\Gamma \left(p+1,,円{\tfrac {1}{2}}\chi ^{2}\left(1-{\frac {x^{2}}{c^{2}}}\right)\right)-\Gamma (p+1,,円{\tfrac {1}{2}}\chi ^{2})}{\Gamma (p+1)-\Gamma (p+1,,円{\tfrac {1}{2}}\chi ^{2})}},\qquad 0\leq x\leq c,\qquad c>0,,円\chi >0,,円p>-1}

where Γ(·) is the gamma function, and Γ(·,·) is the upper incomplete gamma function.

Here parameters c, χ, p represent the cutoff, curvature, and power respectively.

The mode is:

c 2 χ ( χ 2 2 p 1 ) + χ 2 ( χ 2 4 p + 2 ) + ( 1 + 2 p ) 2 {\displaystyle {\frac {c}{{\sqrt {2}}\chi }}{\sqrt {(\chi ^{2}-2p-1)+{\sqrt {\chi ^{2}(\chi ^{2}-4p+2)+(1+2p)^{2}}}}}} {\displaystyle {\frac {c}{{\sqrt {2}}\chi }}{\sqrt {(\chi ^{2}-2p-1)+{\sqrt {\chi ^{2}(\chi ^{2}-4p+2)+(1+2p)^{2}}}}}}

The mean is:

μ = c p π Γ ( p ) Γ ( 5 2 + p ) χ 2 p + 2 2 p + 2 M ( p + 1 , 5 2 + p , χ 2 2 ) Γ ( p + 1 ) Γ ( p + 1 , 1 2 χ 2 ) {\displaystyle \mu =c,円p,円{\sqrt {\pi }}{\frac {\Gamma (p)}{\Gamma ({\tfrac {5}{2}}+p)}}{\frac {\chi ^{2p+2}}{2^{p+2}}}{\frac {M\left(p+1,{\tfrac {5}{2}}+p,-{\tfrac {\chi ^{2}}{2}}\right)}{\Gamma (p+1)-\Gamma (p+1,,円{\tfrac {1}{2}}\chi ^{2})}}} {\displaystyle \mu =c,円p,円{\sqrt {\pi }}{\frac {\Gamma (p)}{\Gamma ({\tfrac {5}{2}}+p)}}{\frac {\chi ^{2p+2}}{2^{p+2}}}{\frac {M\left(p+1,{\tfrac {5}{2}}+p,-{\tfrac {\chi ^{2}}{2}}\right)}{\Gamma (p+1)-\Gamma (p+1,,円{\tfrac {1}{2}}\chi ^{2})}}}

where M(·,·,·) is the Kummer's confluent hypergeometric function.[2] [circular reference ]

The variance is:

σ 2 = c 2 ( χ 2 ) p + 1 χ p + 3 e χ 2 2 + ( χ 2 2 ( p + 1 ) ) { Γ ( p + 2 ) Γ ( p + 2 , 1 2 χ 2 ) } χ 2 ( p + 1 ) ( Γ ( p + 1 ) Γ ( p + 1 , 1 2 χ 2 ) ) μ 2 {\displaystyle \sigma ^{2}=c^{2}{\frac {\left({\frac {\chi }{2}}\right)^{p+1}\chi ^{p+3}e^{-{\tfrac {\chi ^{2}}{2}}}+\left(\chi ^{2}-2(p+1)\right)\left\{\Gamma (p+2)-\Gamma (p+2,,円{\tfrac {1}{2}}\chi ^{2})\right\}}{\chi ^{2}(p+1)\left(\Gamma (p+1)-\Gamma (p+1,,円{\tfrac {1}{2}}\chi ^{2})\right)}}-\mu ^{2}} {\displaystyle \sigma ^{2}=c^{2}{\frac {\left({\frac {\chi }{2}}\right)^{p+1}\chi ^{p+3}e^{-{\tfrac {\chi ^{2}}{2}}}+\left(\chi ^{2}-2(p+1)\right)\left\{\Gamma (p+2)-\Gamma (p+2,,円{\tfrac {1}{2}}\chi ^{2})\right\}}{\chi ^{2}(p+1)\left(\Gamma (p+1)-\Gamma (p+1,,円{\tfrac {1}{2}}\chi ^{2})\right)}}-\mu ^{2}}

p = 0.5 gives a regular ARGUS, listed above.

References

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  1. ^ Albrecht, H. (1990). "Search for hadronic b→u decays". Physics Letters B. 241 (2): 278–282. Bibcode:1990PhLB..241..278A. doi:10.1016/0370-2693(90)91293-K. (More formally by the ARGUS Collaboration, H. Albrecht et al.) In this paper, the function has been defined with parameter c representing the beam energy and parameter p set to 0.5. The normalization and the parameter χ have been obtained from data.
  2. ^ Confluent hypergeometric function

Further reading

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Discrete
univariate
with finite
support
with infinite
support
Continuous
univariate
supported on a
bounded interval
supported on a
semi-infinite
interval
supported
on the whole
real line
with support
whose type varies
Mixed
univariate
continuous-
discrete
Multivariate
(joint)
Directional
Degenerate
and singular
Degenerate
Dirac delta function
Singular
Cantor
Families

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