Scholarlink

<p>Recent research has indicated that the standard model (SM), while historically highly effective, is found to be insufficient due to its prediction of zero mass for neutrinos. With the exception of a few, the majority of the parameters related to neutrinos have been determined by neutrino oscillation experiments with excellent precision. Experiments on neutrino oscillation and neutrino mixing have shown that neutrinos are massive. To fill in gaps, discrete symmetries are becoming more common alongside continuous symmetries while describing the observed pattern of neutrino mixing. Here, we present a <InlineEquation ID="IEq4"> <EquationSource Format="TEX">\(T_7\)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>T</mi> <mn>7</mn> </msub> </math></EquationSource> </InlineEquation> flavour symmetry to explain the masses of charged leptons and neutrinos. The light neutrino mass matrix is derived using seesaw mechanism of type I, which involves the Dirac neutrino mass matrix as well as the right-handed neutrino mass matrix. For normal and inverted mass hierarchies, we estimate the Pontecorvo–Maki–Nakagawa–Sakata matrix (<InlineEquation ID="IEq5"> <EquationSource Format="TEX">\(U_{\textrm{PMNS}}\)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>U</mi> <mtext>PMNS</mtext> </msub> </math></EquationSource> </InlineEquation>), three mixing angles, <InlineEquation ID="IEq6"> <EquationSource Format="TEX">\(\theta _{12}\)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>θ</mi> <mn>12</mn> </msub> </math></EquationSource> </InlineEquation>, <InlineEquation ID="IEq7"> <EquationSource Format="TEX">\(\theta _{23}\)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>θ</mi> <mn>23</mn> </msub> </math></EquationSource> </InlineEquation> and <InlineEquation ID="IEq8"> <EquationSource Format="TEX">\(\theta _{13}\)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>θ</mi> <mn>13</mn> </msub> </math></EquationSource> </InlineEquation>, the masses of three neutrinos, effective Majorana neutrino mass parameter <InlineEquation ID="IEq9"> <EquationSource Format="TEX">\(\langle m_{ee} \rangle \)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mo stretchy="false">⟨</mo> <msub> <mi>m</mi> <mrow> <mi mathvariant="italic">ee</mi> </mrow> </msub> <mo stretchy="false">⟩</mo> </mrow> </math></EquationSource> </InlineEquation> and the other model parameters by using a powerful meta-heuristic and population-based optimisation algorithm, i.e. particle swarm optimisation (PSO).</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

\(T_7\) flavour symmetry gym: the key to unlock the neutrino mass puzzle

  • 期刊论文

  • 发表日期：

    2025年12月11日

查看全文
  • Muhammad Waheed Aslam,
  • Abrar Ahmad Zafar

摘要

Recent research has indicated that the standard model (SM), while historically highly effective, is found to be insufficient due to its prediction of zero mass for neutrinos. With the exception of a few, the majority of the parameters related to neutrinos have been determined by neutrino oscillation experiments with excellent precision. Experiments on neutrino oscillation and neutrino mixing have shown that neutrinos are massive. To fill in gaps, discrete symmetries are becoming more common alongside continuous symmetries while describing the observed pattern of neutrino mixing. Here, we present a \(T_7\) T 7 flavour symmetry to explain the masses of charged leptons and neutrinos. The light neutrino mass matrix is derived using seesaw mechanism of type I, which involves the Dirac neutrino mass matrix as well as the right-handed neutrino mass matrix. For normal and inverted mass hierarchies, we estimate the Pontecorvo–Maki–Nakagawa–Sakata matrix ( \(U_{\textrm{PMNS}}\) U PMNS ), three mixing angles, \(\theta _{12}\) θ 12 , \(\theta _{23}\) θ 23 and \(\theta _{13}\) θ 13 , the masses of three neutrinos, effective Majorana neutrino mass parameter \(\langle m_{ee} \rangle \) ⟨ m ee ⟩ and the other model parameters by using a powerful meta-heuristic and population-based optimisation algorithm, i.e. particle swarm optimisation (PSO).