arXiv subjects

Sezen Sekmen

Publications and source records attributed to Sezen Sekmen.

At least 19 recordsLinked to original sources

arXiv2025

Supersymmetry searches in CMS Run 2: A complete review

The Run 2 data-taking period of the CERN Large Hadron Collider during years 2015-2018 provided about 140 fb$^{-1}$ of proton-proton collisions at 13 TeV, offering an unprecedented opportunity to explore supersymmetry (SUSY) across a wide range of experimental signatures. CMS responded with a broad and diverse search program, carrying out dozens of analyses that probed a multitude of final states and systematically explored different regions of the SUSY parameter space. No significant deviations from standard model predictions were observed, and the results were used for constraining the SUSY landscape. In this review, I provide a comprehensive account of the CMS Run 2 SUSY program, covering its strategy, targeted models, and analysis methods. I then present the full set of searches and conclude with their combined impact through simplified model and phenomenological MSSM interpretations.

hep-ex↗

arXiv2025

Reinterpretation and preservation of data and analyses in HEP

Data from particle physics experiments are unique and are often the result of a very large investment of resources. Given the potential scientific impact of these data, which goes far beyond the immediate priorities of the experimental collaborations that obtain them, it is imperative that the collaborations and the wider particle physics community publish and preserve sufficient information to ensure that this impact can be realised, now and into the future. The information to be published and preserved includes the algorithms, statistical information, simulations and the recorded data. This publication and preservation requires significant resources, and should be a strategic priority with commensurate planning and resource allocation from the earliest stages of future facilities and experiments.

hep-ph↗

arXiv2024

Les Houches guide to reusable ML models in LHC analyses

With the increasing usage of machine-learning in high-energy physics analyses, the publication of the trained models in a reusable form has become a crucial question for analysis preservation and reuse. The complexity of these models creates practical issues for both reporting them accurately and for ensuring the stability of their behaviours in different environments and over extended timescales. In this note we discuss the current state of affairs, highlighting specific practical issues and focusing on the most promising technical and strategic approaches to ensure trustworthy analysis-preservation. This material originated from discussions in the LHC Reinterpretation Forum and the 2023 PhysTeV workshop at Les Houches.

hep-ph↗

arXiv2022

Second Analysis Ecosystem Workshop Report

The second workshop on the HEP Analysis Ecosystem took place 23-25 May 2022 at IJCLab in Orsay, to look at progress and continuing challenges in scaling up HEP analysis to meet the needs of HL-LHC and DUNE, as well as the very pressing needs of LHC Run 3 analysis. The workshop was themed around six particular topics, which were felt to capture key questions, opportunities and challenges. Each topic arranged a plenary session introduction, often with speakers summarising the state-of-the art and the next steps for analysis. This was then followed by parallel sessions, which were much more discussion focused, and where attendees could grapple with the challenges and propose solutions that could be tried. Where there was significant overlap between topics, a joint discussion between them was arranged. In the weeks following the workshop the session conveners wrote this document, which is a summary of the main discussions, the key points raised and the conclusions and outcomes. The document was circulated amongst the participants for comments before being finalised here.

hep-ex↗

arXiv2022

Highlights on Supersymmetry and Exotic Searches at the LHC

The Run 2 data taking period of the Large Hadron Collider (LHC) at CERN in years 2015-2018 has presented a great opportunity to search for physics beyond the standard model (BSM). It will be followed by the Run 3 period starting in 2022, and by the High-Luminosity LHC (HL-LHC) era starting in late 2020s, where the latter promises an unprecedented wealth of physics prospects due to very high expected integrated luminosity and improved detector features. The ATLAS, CMS and LHCb experiments pursued a rich physics program in Run 2, and are already assessing the physics expectations at the HL-LHC era. This report presents highlights from recent search results and HL-LHC studies on BSM physics by the ATLAS, CMS and LHCb experiments. Examples will be shown from model-independent generic searches, searches for supersymmetry, extended Higgs sectors, and new exotic fermions and bosons.

hep-ex↗

arXiv2022

Democratizing LHC Data Analysis with ADL/CutLang

Data analysis at the LHC has a very steep learning curve, which erects a formidable barrier between data and anyone who wishes to analyze data, either to study an idea or to simply understand how data analysis is performed. To make analysis more accessible, we designed the so-called Analysis Description Language (ADL), a domain specific language capable of describing the contents of an LHC analysis in a standard and unambiguous way, independent of any computing frameworks. ADL has an English-like highly human-readable syntax and directly employs concepts relevant to HEP. Therefore it eliminates the need to learn complex analysis frameworks written based on general purpose languages such as C++ or Python, and shifts the focus directly to physics. Analyses written in ADL can be run on data using a runtime interpreter called CutLang, without the necessity of programming. ADL and CutLang are designed for use by anyone with an interest in, and/or knowledge of LHC physics, ranging from experimentalists and phenomenologists to non-professional enthusiasts. ADL/CutLang are originally designed for research, but are also equally intended for education and public use. This approach has already been employed to train undergraduate students with no programming experience in LHC analysis in two dedicated schools in Turkey and Vietnam, and is being adapted for use with LHC Open Data. Moreover, work is in progress towards piloting an educational module in particle physics data analysis for high school students and teachers. Here, we introduce ADL and CutLang and present the educational activities based on these practical tools.

physics.ed-ph↗

arXiv2022

Analysis Description Language: A DSL for HEP Analysis

We propose to adopt a declarative domain specific language for describing the physics algorithm of a high energy physics (HEP) analysis in a standard and unambiguous way decoupled from analysis software frameworks, and argue that this approach provides an accessible and sustainable environment for analysis design, use and preservation. Prototype of such a language called Analysis Description Language (ADL) and its associated tools are being developed and applied in various HEP physics studies. We present the motivations for using a DSL, design principles of ADL and its runtime interpreter CutLang, along with current physics studies based on this approach. We also outline ideas and prospects for the future. Recent physics studies, hands-on workshops and surveys indicate that ADL is a feasible and effective approach with many advantages and benefits, and offers a direction to which the HEP field should give serious consideration.

hep-ph↗

arXiv2022

Data and Analysis Preservation, Recasting, and Reinterpretation

We make the case for the systematic, reliable preservation of event-wise data, derived data products, and executable analysis code. This preservation enables the analyses' long-term future reuse, in order to maximise the scientific impact of publicly funded particle-physics experiments. We cover the needs of both the experimental and theoretical particle physics communities, and outline the goals and benefits that are uniquely enabled by analysis recasting and reinterpretation. We also discuss technical challenges and infrastructure needs, as well as sociological challenges and changes, and give summary recommendations to the particle-physics community.

hep-ph↗

arXiv2021

Publishing statistical models: Getting the most out of particle physics experiments

The statistical models used to derive the results of experimental analyses are of incredible scientific value and are essential information for analysis preservation and reuse. In this paper, we make the scientific case for systematically publishing the full statistical models and discuss the technical developments that make this practical. By means of a variety of physics cases -- including parton distribution functions, Higgs boson measurements, effective field theory interpretations, direct searches for new physics, heavy flavor physics, direct dark matter detection, world averages, and beyond the Standard Model global fits -- we illustrate how detailed information on the statistical modelling can enhance the short- and long-term impact of experimental results.

hep-ph↗

arXiv2021

Recent advances in ADL, CutLang and adl2tnm

This paper presents an overview and features of an Analysis Description Language (ADL) designed for HEP data analysis. ADL is a domain specific, declarative language that describes the physics content of an analysis in a standard and unambiguous way, independent of any computing frameworks. It also describes infrastructures that render ADL executable, namely CutLang, a direct runtime interpreter (originally also a language), and adl2tnm, a transpiler converting ADL into C++ code. In ADL, analyses are described in human readable plain text files, clearly separating object, variable and event selection definitions in blocks, with a syntax that includes mathematical and logical operations, comparison and optimisation operators, reducers, four-vector algebra and commonly used functions. Recent studies demonstrate that adapting the ADL approach has numerous benefits for the experimental and phenomenological HEP communities. These include facilitating the abstraction, design, optimization, visualization, validation, combination, reproduction, interpretation and overall communication of the analysis contents and long term preservation of the analyses beyond the lifetimes of experiments. Here we also discuss some of the current ADL applications in physics studies and future prospects based on static analysis and differentiable programming.

physics.comp-ph↗

arXiv2021

Down type iso-singlet quarks at the HL-LHC and FCC-hh

We study the discovery potential of down type iso-singlet quarks, $D$, predicted by the $E_6$ GUT model in the ${pp\rightarrow D\bar{D}\rightarrow ZZd\bar{d} \rightarrow \ell^+\ell^-\ell^+\ell^- d\bar{d}}$ channel at the HL-LHC and FCC-hh colliders. The analysis is performed using a high level analysis description language and its runtime interpreter. The study shows that, using solely this channel, HL-LHC can discover $D$ quarks up to a mass of 710 GeV whereas FCC-hh up to 2430 GeV with data collected in their complete run periods.

hep-ph↗

arXiv2021

CutLang as an Analysis Description Language for Introducing Students to Analyses in Particle Physics

The fifth edition of the "Computing Applications in Particle Physics" school was held on 3-7 February 2020, at Istanbul University, Turkey. This particular edition focused on the processing of simulated data from the Large Hadron Collider collisions using an Analysis Description Language and its runtime interpreter called CutLang. 24 undergraduate and 6 graduate students were initiated to collider data analysis during the school. After 3 days of lectures and exercises, the students were grouped into teams of 3 or 4 and each team was assigned an analysis publication from ATLAS or CMS experiments. After 1.5 days of independent study, each team was able to reproduce the assigned analysis using CutLang.

hep-ph↗

arXiv2021

Digging deeper into SUSY parameter space with the CMS experiment

The classic searches for supersymmetry have not given any strong indication for new physics. Therefore CMS is designing dedicated searches to target the more difficult and specific supersymmetry scenarios. This contribution present three such recent searches based on 13 TeV proton-proton collisions recorded with the CMS detector in 2016, 2017 and 2018: a search for heavy gluinos cascading via heavy next-to-lightest neutralino in final states with boosted Z bosons and missing transverse momentum; a search for compressed supersymmetry in final states with soft taus; and a search for compressed, long-lived charginos in hadronic final states with disappearing tracks.

hep-ex↗

arXiv2020

Analysis Description Languages for the LHC

An analysis description language is a domain specific language capable of describing the contents of an LHC analysis in a standard and unambiguous way, independent of any computing framework. It is designed for use by anyone with an interest in, and knowledge of, LHC physics, i.e., experimentalists, phenomenologists and other enthusiasts. Adopting analysis description languages would bring numerous benefits for the LHC experimental and phenomenological communities ranging from analysis preservation beyond the lifetimes of experiments or analysis software to facilitating the abstraction, design, visualization, validation, combination, reproduction, interpretation and overall communication of the analysis contents. Here, we introduce the analysis description language concept and summarize the current efforts ongoing to develop such languages and tools to use them in LHC analyses.

hep-ph↗

arXiv2019

CutLang: a cut-based HEP analysis description language and runtime interpreter

We present CutLang, an analysis description language and runtime interpreter for high energy collider physics data analyses. An analysis description language is a declerative domain specific language that can express all elements of a data analysis in an easy and unambiguous way. A full-fledged human readable analysis description language, incorporating logical and mathematical expressions, would eliminate many programming difficulties and errors, consequently allowing the scientist to focus on the goal, but not on the tool. In this paper, we discuss the guiding principles and scope of the CutLang language, implementation of the CutLang runtime interpreter and the CutLang framework, and demonstrate an example of top pair reconstruction.

hep-ph↗

arXiv2019

Beyond the Standard Model Physics at the High Luminosity LHC

The High-Luminosity Large Hadron Collider (HL-LHC) is expected to deliver an integrated luminosity of up to 3000 fb$^{-1}$. The very high instantaneous luminosity will lead to about 200 proton-proton collisions per bunch crossing (pileup) superimposed to each event of interest, thus providing extremely challenging experimental conditions, which will be addressed by accompanying improvements in the decetors. The sensitivity to find new physics Beyond the Standard Model (BSM) is significantly improved and will allow to extend the reach for SUSY, heavy exotic resonances, vector like quarks, dark matter and exotic long-lived signatures, to name a few. This note summarizes several ATLAS and CMS studies performed to asses HL-LHC sensitivity to various BSM models and signatures.

hep-ex↗

arXiv2018

CutLang: A Particle Physics Analysis Description Language and Runtime Interpreter

This note introduces CutLang, a domain specific language that aims to provide a clear, human readable way to define analyses in high energy particle physics (HEP) along with an interpretation framework of that language. A proof of principle (PoP) implementation of the CutLang interpreter, achieved using C++ as a layer over the CERN data analysis framework ROOT, is presently available. This PoP implementation permits writing HEP analyses in an unobfuscated manner, as a set of commands in human readable text files, which are interpreted by the framework at runtime. We describe the main features of CutLang and illustrate its usage with two analysis examples. Initial experience with CutLang has shown that a just-in-time interpretation of a human readable HEP specific language is a practical alternative to analysis writing using compiled languages such as C++.

hep-ph↗

arXiv2018

Optimizing Event Selection with the Random Grid Search

The random grid search (RGS) is a simple, but efficient, stochastic algorithm to find optimal cuts that was developed in the context of the search for the top quark at Fermilab in the mid-1990s. The algorithm, and associated code, have been enhanced recently with the introduction of two new cut types, one of which has been successfully used in searches for supersymmetry at the Large Hadron Collider. The RGS optimization algorithm is described along with the recent developments, which are illustrated with two examples from particle physics. One explores the optimization of the selection of vector boson fusion events in the four-lepton decay mode of the Higgs boson and the other optimizes SUSY searches using boosted objects and the razor variables.

hep-ph↗