Invariant mass reconstruction of heavy gauge bosons decaying to $$\tau $$ leptons using machine learning techniques

Abstract Many analyses are performed by the LHC experiments to search for heavy gauge bosons, which appear in several new physics models. The invariant mass reconstruction of heavy gauge bosons is difficult when they decay to $$\tau $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>τ</mml:mi> </mml:math> leptons due to missing neutrinos in the final state. Machine learning techniques are widely utilized in experimental high-energy physics, in particular in analyzing the large amount of data produced at the LHC. In this paper, we study various machine learning techniques to reconstruct the invariant mass of $$Z^{\prime }~\rightarrow ~\tau \tau $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msup> <mml:mi>Z</mml:mi> <mml:mo>′</mml:mo> </mml:msup> <mml:mspace/> <mml:mo>→</mml:mo> <mml:mspace/> <mml:mi>τ</mml:mi> <mml:mi>τ</mml:mi> </mml:mrow> </mml:math> and $$W^{\prime }~\rightarrow ~\tau \nu $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msup> <mml:mi>W</mml:mi> <mml:mo>′</mml:mo> </mml:msup> <mml:mspace/> <mml:mo>→</mml:mo> <mml:mspace/> <mml:mi>τ</mml:mi> <mml:mi>ν</mml:mi> </mml:mrow> </mml:math> decays, which can improve the sensitivity of these searches.