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

Definition

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 }}\,\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 . Here χ {\displaystyle \chi } and 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}},}

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

Cumulative distribution function

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 )}}} .

Parameter estimation

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}}}} .

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 }}} is consistent and asymptotically normal.

Generalized ARGUS distribution

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,\,{\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,\,\chi >0,\,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,\,{\tfrac {1}{2}}\chi ^{2}\left(1-{\frac {x^{2}}{c^{2}}}\right)\right)-\Gamma (p 1,\,{\tfrac {1}{2}}\chi ^{2})}{\Gamma (p 1)-\Gamma (p 1,\,{\tfrac {1}{2}}\chi ^{2})}},\qquad 0\leq x\leq c,\qquad c>0,\,\chi >0,\,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}}}}}}

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\,p\,{\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,\,{\tfrac {1}{2}}\chi ^{2})}}}

where M(·,·,·) is the Kummer's confluent hypergeometric function.

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,\,{\tfrac {1}{2}}\chi ^{2})\right\}}{\chi ^{2}(p 1)\left(\Gamma (p 1)-\Gamma (p 1,\,{\tfrac {1}{2}}\chi ^{2})\right)}}-\mu ^{2}}

p = 0.5 gives a regular ARGUS, listed above.

References

Further reading

  • Albrecht, H. (1994). "Measurement of the polarization in the decay B → J/ψK*". Physics Letters B. 340 (3): 217–220. Bibcode:1994PhLB..340..217A. doi:10.1016/0370-2693(94)01302-0.
  • Pedlar, T.; Cronin-Hennessy, D.; Hietala, J.; Dobbs, S.; Metreveli, Z.; Seth, K.; Tomaradze, A.; Xiao, T.; Martin, L. (2011). "Observation of the hc(1P) Using e e Collisions above the DD Threshold". Physical Review Letters. 107 (4): 041803. arXiv:1104.2025. Bibcode:2011PhRvL.107d1803P. doi:10.1103/PhysRevLett.107.041803. PMID 21866994. S2CID 33751212.D Threshold&rft.volume=107&rft.issue=4&rft.pages=041803&rft.date=2011&rft_id=https://api.semanticscholar.org/CorpusID:33751212#id-name=S2CID&rft_id=info:bibcode/2011PhRvL.107d1803P&rft_id=info:arxiv/1104.2025&rft_id=info:pmid/21866994&rft_id=info:doi/10.1103/PhysRevLett.107.041803&rft.aulast=Pedlar&rft.aufirst=T.&rft.au=Cronin-Hennessy, D.&rft.au=Hietala, J.&rft.au=Dobbs, S.&rft.au=Metreveli, Z.&rft.au=Seth, K.&rft.au=Tomaradze, A.&rft.au=Xiao, T.&rft.au=Martin, L.&rfr_id=info:sid/en.wikipedia.org:ARGUS distribution">
  • Lees, J. P.; Poireau, V.; Prencipe, E.; Tisserand, V.; Garra Tico, J.; Grauges, E.; Martinelli, M.; Palano, A.; Pappagallo, M.; Eigen, G.; Stugu, B.; Sun, L.; Battaglia, M.; Brown, D. N.; Hooberman, B.; Kerth, L. T.; Kolomensky, Y. G.; Lynch, G.; Osipenkov, I. L.; Tanabe, T.; Hawkes, C. M.; Soni, N.; Watson, A. T.; Koch, H.; Schroeder, T.; Asgeirsson, D. J.; Hearty, C.; Mattison, T. S.; McKenna, J. A.; et al. (2010). "Search for Charged Lepton Flavor Violation in Narrow Υ Decays". Physical Review Letters. 104 (15): 151802. arXiv:1001.1883. Bibcode:2010PhRvL.104o1802L. doi:10.1103/PhysRevLett.104.151802. PMID 20481982. S2CID 14992286.

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