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大学・研究所にある論文を検索できる 「Search for the dimuon decay of the Higgs boson in 139 fb^-1 of pp collisions at √s=13 TeV with the ATLAS detector」の論文概要。リケラボ論文検索は、全国の大学リポジトリにある学位論文・教授論文を一括検索できる論文検索サービスです。
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Search for the dimuon decay of the Higgs boson in 139 fb^-1 of pp collisions at √s=13 TeV with the ATLAS detector

川口, 智美 名古屋大学

2021.06.21

概要

素粒子標準模型は現在までの観測事実をよく記述しているが、実験的には未だ確認されていない現象がある。その中でも、2012年に発見されたヒッグス粒子の現象には未観測のものが多く残されている。その一つは、物質を構成する３世代のフェルミオンがヒッグス場との湯川相互作用を通して結合の強さに比例した質量を獲得することである。標準模型では不自然なことに、唯一のヒッグス粒子により、511keVの電子から172GeVのトップクォークまで6桁の質量の違いを説明し、世代を区別している。この不自然さを解明し、フェルミオンの質量起源と世代構造の謎に迫るために、全世代のフェルミオンとの湯川結合を測定することが非常に重要である。これまでに第３世代のフェルミオンとの湯川結合は観測されているが、第１世代と第２世代のフェルミオンとの湯川結合は観測されていない。

本論文では、ヒッグス粒子が２つの𝜇粒子に崩壊する𝐻→𝜇𝜇過程の探索について述べる。この崩壊は第２世代のフェルミオンである𝜇粒子との湯川結合を含む。また、μ粒子の再構成は実験的に容易であるため、第２世代のフェルミオンとの湯川結合探索における最重要ターゲットである。一方で、他の第２世代フェルミオン（𝑐,𝑠）に比べて崩壊率が0.02%と小さく、𝐻→𝜇𝜇過程をドレル・ヤン事象由来の膨大な背景事象の中から探索しなければならないことが探索を困難にしている。LHCで2015年から2016年に取得した重心系エネルギー13TeVのデータ36.1fb－1を用いた先行研究では、この過程の有意な観測に至っていない。

本研究では、LHCで2015年から2018年に取得した重心系エネルギー13TeVのデータ139fb－1を用いて𝐻→𝜇𝜇過程の探索を行った。この解析では、𝜇粒子対の不変質量を再構成し、膨大な背景事象の中からヒッグス粒子の質量125GeV付近のピークを探索する。よって、探索感度の向上のためには𝜇粒子対の不変質量分解能の向上と背景事象の削減が非常に重要である。不変質量分解能を向上させるために、私は𝜇粒子が光子を放射する事象に着目し、終状態放射由来と思われる光子を選別する手法を開発した。得られた光子を不変質量計算に加味することで、不変質量分解能が2.8%向上し、不変質量120GeVから130GeVの領域における信号事象の数が1.4%増加した。また、背景事象を削減するために、ヒッグス粒子生成時に付随するジェットやレプトンの数、運動学的情報を用いた機械学習を使用して、ヒッグス粒子生成事象を20個のカテゴリーに分類した。これによって、𝐻→𝜇𝜇過程の探索感度が約20%向上した。また、信号事象数を大きなバイアスなく抽出するために、ドレル・ヤン過程の解析的な分布を基礎とする関数を用いたフィットによって背景事象数を導出した。

本解析の結果、𝐻→𝜇𝜇崩壊の兆候を２σの統計的有意度で得た。また、標準模型で期待される信号数に対する信号数の測定値（結合強度）1.2±0.6を得た。この値は、標準模型と無矛盾であった。これらの結果は、LHCを使用した他の実験の一つであるCMS実験とも無矛盾であり、ヒッグス粒子と𝜇粒子の結合強度が第３世代フェルミオンの結合強度よりもはるかに小さいことを示唆している。さらに、第２世代フェルミオンの質量起源がヒッグス機構の不自然さから生じているようだということを初めて示す結果である。これは、フェルミオンの質量起源と世代構造の謎を解明する上で重要な要素を提供する。

現状、結合強度の測定は統計誤差が支配的である。今後計画されている高輝度LHC実験では、さらなるデータ取得によって、第2世代フェルミオンの湯川結合は5σを超える有意度で検証されることが期待される。だが、高輝度LHC実験は、より高いパイルアップ環境下で実験を行うため、データ取得や物理解析が困難になると予想される。そこで、高いパイルアップ環境下においても効率よく高横運動量のμ粒子を含む事象を瞬時に選別できるμ粒子飛跡トリガーアルゴリズムを開発した。さらに、この研究において確立した終状態光子の再構成手法は、より高いパイルアップ環境下での将来の実験に役立つと考えられる。

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