The ATLAS experiment at the Large Hadron Collider successfully conducted a search for hadronic signatures of beyond the Standard Model (BSM) physics. The focus was on a hidden dark sector accessed through resonant production of a $Z'$ mediator. The dataset collected from 2015 to 2018 consisted of proton-proton collisions at $\sqrt{s}$ = 13 TeV with an integrated luminosity of 140 fb$^{-1}$. This dataset was carefully analyzed to identify any potential signals of new physics. The $Z'$ mediator was assumed to decay into two dark quarks, which then hadronized and decayed into showers containing both dark and Standard Model particles. This resulted in a "semi-visible" jet topology. To distinguish these dark showers from the dominant background of mismeasured multijet events, advanced machine learning techniques were employed. An anomaly detection approach was utilized to maintain sensitivity to various BSM scenarios. A resonance search was carried out by fitting the transverse mass spectrum using a functional form background estimation method. However, no significant excess over the expected background was observed in the analysis. The results were presented as limits on the production cross section of semi-visible jet signals, characterized by the fraction of invisible particles in the decay and the mass of the $Z'$ mediator. Additionally, any potential generic Gaussian-shaped mass peak in the anomaly region was quantified for significance. In total, this study included 45 pages with figures and tables providing detailed insights into the analysis process. These findings contribute valuable information to ongoing research efforts aimed at exploring new physics phenomena within high-energy particle collisions.
SummaryScientists at a big machine called the Large Hadron Collider looked for special signs of new science. They focused on finding a hidden dark world using a special particle called $Z'$. They used data from 2015 to 2018, which came from smashing tiny particles together really fast. The $Z'$ particle broke into two dark pieces, making a unique pattern in the data. They used smart computer programs to tell apart the dark patterns from normal ones. Even though they didn't find anything super new, they learned more about how to search for mysterious things.
Definitions
- Experiment: A test or investigation done to learn something new.
- Physics: The study of matter, energy, and how they interact.
- Collider: A big machine that smashes tiny particles together.
- Dataset: A collection of information or data.
- Mediator: Something that helps in communication or interaction between different parts.
- Quarks: Tiny particles that make up protons and neutrons.
- Topology: The arrangement or structure of something.
- Machine learning techniques: Methods that help computers learn and improve on their own.
- Anomaly detection: Finding something unusual or unexpected in a set of data.
- Resonance search: Looking for specific signals or patterns in data related to certain particles or phenomena.
The Large Hadron Collider (LHC) is the world's largest and most powerful particle accelerator, located at the European Organization for Nuclear Research (CERN) in Switzerland. It was built to study the fundamental building blocks of matter and their interactions, with the ultimate goal of understanding the origins of our universe. One of the key experiments conducted at the LHC is called ATLAS, which stands for A Toroidal LHC Apparatus. This experiment involves a collaboration of over 3,000 physicists from around the world who work together to analyze data collected from high-energy proton-proton collisions.
Recently, a team of researchers from ATLAS successfully conducted a search for new physics beyond what is predicted by the Standard Model (SM). The focus was on a hidden dark sector accessed through resonant production of a $Z'$ mediator. This research paper presents their findings in detail, providing valuable insights into ongoing efforts to explore new physics phenomena within high-energy particle collisions.
The dataset used in this study was collected between 2015 and 2018 from proton-proton collisions at $\sqrt{s}$ = 13 TeV with an integrated luminosity of 140 fb$^{-1}$. This large amount of data allowed for precise measurements and analysis to be carried out. The main objective was to identify any potential signals that could indicate new physics beyond what is currently known.
The $Z'$ mediator is a hypothetical particle that acts as an intermediary between dark matter particles and ordinary matter particles. In this study, it was assumed that the $Z'$ mediator would decay into two dark quarks, which would then hadronize and decay into showers containing both dark and SM particles. This resulted in what is known as a "semi-visible" jet topology - meaning that some particles are visible while others remain invisible.
To distinguish these semi-visible jets from other events such as mismeasured multijet events which make up the dominant background, advanced machine learning techniques were employed. These techniques allowed for a more precise and efficient analysis of the data. An anomaly detection approach was used to maintain sensitivity to various beyond the Standard Model (BSM) scenarios.
The researchers also carried out a resonance search by fitting the transverse mass spectrum using a functional form background estimation method. This involved comparing the observed data with what is expected from known SM processes. Any significant excess over the expected background would indicate the presence of new physics.
However, after careful analysis and comparison, no significant excess was observed in the data. This means that there is currently no evidence for new physics beyond the Standard Model in this particular dataset. The results were presented as limits on the production cross section of semi-visible jet signals, characterized by the fraction of invisible particles in the decay and the mass of the $Z'$ mediator.
In addition to these limits, any potential generic Gaussian-shaped mass peak in the anomaly region was also quantified for significance. This provides important information for future studies and experiments looking into similar BSM scenarios.
Overall, this research paper consisted of 45 pages filled with detailed figures and tables providing insights into every aspect of their analysis process. It showcases not only their findings but also their methodology and techniques used to achieve them.
In conclusion, this study conducted by ATLAS at CERN's Large Hadron Collider has contributed valuable information to ongoing efforts aimed at exploring new physics phenomena within high-energy particle collisions. While no significant excess was found in this particular dataset, it serves as an important step towards understanding our universe at its most fundamental level.
A search is presented for hadronic signatures of beyond the Standard Model (BSM) physics, with an emphasis on signatures of a strongly-coupled hidden dark sector accessed via resonant production of a $Z'$ mediator. The ATLAS experiment dataset collected at the Large Hadron Collider from 2015 to 2018 is used, consisting of proton-proton collisions at $\sqrt{s}$ = 13 TeV and corresponding to an integrated luminosity of 140 fb$^{-1}$. The $Z'$ mediator is considered to decay to two dark quarks, which each hadronize and decay to showers containing both dark and Standard Model particles, producing a topology of interacting and non-interacting particles within a jet known as ``semi-visible". Machine learning methods are used to select these dark showers and reject the dominant background of mismeasured multijet events, including an anomaly detection approach to preserve broad sensitivity to a variety of BSM topologies. A resonance search is performed by fitting the transverse mass spectrum based on a functional form background estimation. No significant excess over the expected background is observed. Results are presented as limits on the production cross section of semi-visible jet signals, parameterized by the fraction of invisible particles in the decay and the $Z'$ mass, and by quantifying the significance of any generic Gaussian-shaped mass peak in the anomaly region.
