arXiv subjects

Sudhir Malik

Publications and source records attributed to Sudhir Malik.

At least 19 recordsLinked to original sources

arXiv2026

Training on Data Analysis Reproducibility via Containerization with Apptainer

We present the material and resources developed for training physicists on containerization technologies enabled by Apptainer. In the context of analysis preservation using Apptainer's capabilities, we have developed examples that execute common tools in High Energy Physics (HEP) and Nuclear Physics within containers. Training physicists on containerization technologies is of utmost importance in today's research landscape. By embracing these technologies, users can achieve enhanced reproducibility, portability, collaboration, and resource efficiency, assuring the conditions and integrity of the scientific analysis process. This training module,``Introduction to Apptainer/Singularity'', is part of the HEP Software Foundation Training Center, which aims to equip newcomers to the field of High Energy Physics with the necessary software skills and best practices.

physics.ed-ph↗

arXiv2026

The Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS)

This community paper developed out of the NSF Workshop on the Future of Artificial Intelligence (AI) and the Mathematical and Physics Sciences (MPS), which was held in March 2025 with the goal of understanding how the MPS domains (Astronomy, Chemistry, Materials Research, Mathematical Sciences, and Physics) can best capitalize on, and contribute to, the future of AI. We present here a summary and snapshot of the MPS community's perspective, as of Spring/Summer 2025, in a rapidly developing field. The link between AI and MPS is becoming increasingly inextricable; now is a crucial moment to strengthen the link between AI and Science by pursuing a strategy that proactively and thoughtfully leverages the potential of AI for scientific discovery and optimizes opportunities to impact the development of AI by applying concepts from fundamental science. To achieve this, we propose activities and strategic priorities that: (1) enable AI+MPS research in both directions; (2) build up an interdisciplinary community of AI+MPS researchers; and (3) foster education and workforce development in AI for MPS researchers and students. We conclude with a summary of suggested priorities for funding agencies, educational institutions, and individual researchers to help position the MPS community to be a leader in, and take full advantage of, the transformative potential of AI+MPS.

cs.AI↗

arXiv2026

MEDIC: a network for monitoring data quality in collider experiments

Data Quality Monitoring (DQM) is a crucial component of particle physics experiments and ensures that the recorded data is of the highest quality, and suitable for subsequent physics analysis. Due to the extreme environmental conditions, unprecedented data volumes, and the sheer scale and complexity of the detectors, DQM orchestration has become a very challenging task. Therefore, the use of Machine Learning (ML) to automate anomaly detection, improve efficiency, and reduce human error in the process of collecting high-quality data is unavoidable. Since DQM relies on real experimental data, it is inherently tied to the specific detector substructure and technology in operation. In this work, a simulation-driven approach to DQM is proposed, enabling the study and development of data-quality methodologies in a controlled environment. Using a modified version of Delphes -- a fast, multi-purpose detector simulation -- the preliminary realization of a framework is demonstrated which leverages ML to identify detector anomalies as well as localize the malfunctioning components responsible. We introduce MEDIC (Monitoring for Event Data Integrity and Consistency), a neural network designed to learn detector behavior and perform DQM tasks to look for potential faults. Although the present implementation adopts a simplified setup for computational ease, where large detector regions are deliberately deactivated to mimic faults, this work represents an initial step toward a comprehensive ML-based DQM framework. The encouraging results underline the potential of simulation-driven studies as a foundation for developing more advanced, data-driven DQM systems for future particle detectors.

hep-ex↗

arXiv2025

Jet Image Tagging Using Deep Learning: An Ensemble Model

Jet classification in high-energy particle physics is important for understanding fundamental interactions and probing phenomena beyond the Standard Model. Jets originate from the fragmentation and hadronization of quarks and gluons, and pose a challenge for identification due to their complex, multidimensional structure. Traditional classification methods often fall short in capturing these intricacies, necessitating advanced machine learning approaches. In this paper, we employ two neural networks simultaneously as an ensemble to tag various jet types. We convert the jet data to two-dimensional histograms instead of representing them as points in a higher-dimensional space. Specifically, this ensemble approach, hereafter referred to as Ensemble Model, is used to tag jets into classes from the JetNet dataset, corresponding to: Top Quarks, Light Quarks (up or down), and W and Z bosons. For the jet classes mentioned above, we show that the Ensemble Model can be used for both binary and multi-categorical classification. This ensemble approach learns jet features by leveraging the strengths of each constituent network achieving superior performance compared to either individual network.

physics.data-an↗

arXiv2025

Jet Image Generation in High Energy Physics Using Diffusion Models

This article presents, for the first time, the application of diffusion models for generating jet images corresponding to proton-proton collision events at the Large Hadron Collider (LHC). The kinematic variables of quark, gluon, W-boson, Z-boson, and top quark jets from the JetNet simulation dataset are mapped to two-dimensional image representations. Diffusion models are trained on these images to learn the spatial distribution of jet constituents. We compare the performance of score-based diffusion models and consistency models in accurately generating class-conditional jet images. Unlike approaches based on latent distributions, our method operates directly in image space. The fidelity of the generated images is evaluated using several metrics, including the Fréchet Inception Distance (FID), which demonstrates that consistency models achieve higher fidelity and generation stability compared to score-based diffusion models. These advancements offer significant improvements in computational efficiency and generation accuracy, providing valuable tools for High Energy Physics (HEP) research.

hep-ph↗

arXiv2025

The Critical Importance of Software for HEP

Particle physics has an ambitious and broad global experimental programme for the coming decades. Large investments in building new facilities are already underway or under consideration. Scaling the present processing power and data storage needs by the foreseen increase in data rates in the next decade for HL-LHC is not sustainable within the current budgets. As a result, a more efficient usage of computing resources is required in order to realise the physics potential of future experiments. Software and computing are an integral part of experimental design, trigger and data acquisition, simulation, reconstruction, and analysis, as well as related theoretical predictions. A significant investment in computing and software is therefore critical. Advances in software and computing, including artificial intelligence (AI) and machine learning (ML), will be key for solving these challenges. Making better use of new processing hardware such as graphical processing units (GPUs) or ARM chips is a growing trend. This forms part of a computing solution that makes efficient use of facilities and contributes to the reduction of the environmental footprint of HEP computing. The HEP community already provided a roadmap for software and computing for the last EPPSU, and this paper updates that, with a focus on the most resource critical parts of our data processing chain.

hep-ex↗

arXiv2024

A multicategory jet image classification framework using deep neural network

Jet point cloud images are high dimensional data structures that needs to be transformed to a separable feature space for machine learning algorithms to distinguish them with simple decision boundaries. In this article, the authors focus on jet category separability by particle and jet feature extraction, resulting in more efficient training of a simple deep neural network, resulting in a computational efficient interpretable model for jet classification. The methodology is tested with three to five categories of jets from the JetNet benchmark jet tagging dataset, resulting in comparable performance to particle flow network. This work demonstrates that high dimensional datasets represented in separable latent spaces lead to simpler architectures for jet classification.

hep-ph↗

arXiv2024

A novel internship program in HEP

The U.S. CMS collaboration has designed a novel internship program for undergraduates to enhance the participation of students from under-represented populations, including those at minority serving institutions, in High Energy Physics (HEP). These students traditionally face several barriers including lack of research infrastructure and opportunities, insufficient mentoring, lack of support networks, and financial hardship, among many others, resulting in a lack of participation in STEM fields. We had recently reported about a fully virtual 10-week internship pilot program called "U.S. CMS - PURSUE (Program for Undergraduate Research SUmmer Experience)" to address dismantling such barriers. The 2023 iteration of this program builds on it by imparting not only an in-person summer internship experience but extends it into the academic semester as well. Students are selected predominantly from Minority Serving Institutions with no research program in HEP and from under-represented groups. They experience a structured hands-on research experience with an initial two-week "bootcamp" on software training modules followed by an 8-week HEP project targeting physics analysis, software, computing or instrumentation work on the CMS Experiment. A subset of interns continue the experience into the academic semester, enabling a further in-depth knowledge of the field and a motivation to persist in STEM areas. In this paper, we describe our recent experience with this upgraded internship program. The paper is dedicated to the memory of Prof. Meenakshi Narain (Brown University) who was the driving force behind this internship program and U.S CMS diversity, equity and inclusion efforts.

physics.ed-ph↗

arXiv2022

Community Engagement Frontier

This is the summary report of the Community Engagement Frontier for the Snowmass 2021 study of the future of particle physics. The report discusses a number of general issues of importance to the particle physics community, including (1) the relation of universities, national laboratories, and industry, (2) career paths for scientists engaged in particle physics, (3) diversity, equity, and inclusion, (4) physics education, (5) public education and outreach, (6) engagement with the government and public policy, and (7) the environmental and social impacts of particle physics.

physics.soc-ph↗

arXiv2022

U.S. CMS - PURSUE (Program for Undergraduate Research SUmmer Experience)

Students from under-represented populations, including those at minority serving institutions have traditionally faced many barriers that have resulted in their being under-represented in High Energy Physics. These barriers include lack of research infrastructure and opportunities, insufficient mentoring, lack of support networks, and financial hardship, among many others. Recently the U.S. CMS Collaboration launched a pilot program U.S. CMS - PURSUE (Program for Undergraduate Research SUmmer Experience) to address these barriers. A 10-week paid internship program, the very first of its kind in an HEP experiment, was organised during the summer of 2022. Students were selected predominantly from Minority Serving Institutions with no research program in HEP. This pilot program provided a structured hands-on research experience under the mentor-ship of U.S. CMS scientists from several collaborating institutions. In addition to emphasis on hands-on research, the program offered a set of software training modules for the first few weeks. These were interleaved with a series of lectures every week covering a broad range of topics. The students were exposed to cutting-edge particle physics research and developed a broad set of skills in software, computing, data science, and machine learning. The modality of this program was virtual, due to the unknown circumstances following the pandemic. There is plan to continue the internship program annually, with in-person training and research participation. In this paper, we describe the experience with the pilot program U.S. CMS - PURSUE.

physics.ed-ph↗

arXiv2022

Summary Report of the Topical Group on Career Pipeline and Development (CommF2) Community Engagement Frontier Snowmass 2021

The HEP faculty hire job market has stayed fairly plateaued over the years not keeping up with number of postdocs and PhD produced seeking such employment. Physicists who seek jobs outside this realm face challenges. For example those hired as faculties at predominantly undergraduate institutions (PUI) and community colleges (CC) face hurdles to keep with research due higher teaching load and funding challenges. At the same time those who seek employment in industry may find themselves in unprepared territory despite marketable skills. New job opportunities seeking HEP developed skills have appeared in data science, machine learning and quantum computing. Given that a vast majority transition to the industry jobs, we must strengthen the existing paths for this transition and develop new ways to facilitating it. A strong engagement between HEP and its alumni would boost this process. At the same time those who stay in academia but choose to work at PUIs or CCs must be enabled to continue to pursue research and receive support and guidance for funding. PUIs and CCs serve as a gateway to opportunities for inclusiveness beyond national labs and academic research institutions offering an early starting point in the pipeline that can mitigate issues of lack of diversity and underrepresented participation of different groups in HEP. This report summarises the study undertaken to investigate these issues in HEP community and provide findings and recommendations.

physics.ed-ph↗

arXiv2022

Summary Report of the Topical Group on Physics Education, Community Engagement Frontier (CEF4/CommF4) Snowmass 2021

An essential companion to the development and advancement of the field of Particle Physics is a strong program in physics education at all levels, that can attract entry level students across the full demographic spectrum and provide them with the education, training and skills needed to advance to successful careers in Science, Technology, Engineering and Mathematics (STEM) and other fields. This report summarizes the work of several investigative teams that have reviewed and assessed current opportunities in physics education across K-12, undergraduate, graduate and postdoctoral domains, including national and international linkages. From these assessments, recommendations have been put forward aimed to innovate educationally in strategic ways to strengthen ties between the research community and teachers, between the academic community and the private sector, and through both domestic and international connections.

physics.ed-ph↗

arXiv2022

Enhancing HEP research in predominantly undergraduate institutions and community colleges

The long-term success of HEP lies in expanding inclusiveness beyond national labs and academic research institutions to a vast community of predominantly undergraduate institutions (PUI) and community colleges (CC). Institutions such as PUIs and CCs offer an early starting point in the pipeline that can mitigate issues of lack of diversity and underrepresented participation of different groups in HEP. However, there are many underlying systemic, structural, and cultural challenges that need to be addressed collectively. Experimental collaborations are largely populated by national labs and research-focused academic institutions (non-PUIs). The faculty at PUIs and CCs have a high teaching load that is detrimental to their research participation. In addition, there is a lack of guidance, access, and tough competition for securing research funding. The students also suffer from a lack of research infrastructure and technical equipment that can only be found at national labs and larger universities. There are existing successful efforts to enhance the HEP research experience of students and faculty members. This paper discusses ways to leverage these to provide more research opportunities and establish a sustainable national program targeting specifically the issues faced by communities at PUIs and CCs. The need for research mentoring and skill building for faculty members is also laid out. The changes discussed in this paper would make a direct impact on the current spectrum of challenges.

physics.ed-ph↗

arXiv2022

Facilitating Non-HEP Career Transition

About two-third of Physics PhDs establish careers outside of academia and the national laboratories in areas like Software, Instrumentation, Data Science, Finance, Healthcare, Journalism, Public Policy and Non-Governmental Organization. Skills and knowledge developed during HEPA (High Energy Physics and Astrophysics) research as an undergraduate, graduate or a postdoc level (collectively called early career) have been long sought after in industry. These skills are complex problem solving abilities, software programming, data analysis, math, statistics and scientific writing, to name a few. Given that a vast majority transition to the industry jobs, existing paths for such transition should be strengthened and new ways of facilitating it be identified and developed. A strong engagement between HEPA and its alumni would be a pre-requisite for this. It might also lead to creative ways to reverse the "brain drain" by encouraging alumni to collaborate on HEPA research projects or possibly come back full time to research. We motivate and discuss below several actionable recommendations by which HEPA institutions as well as HEPA faculty mentors can strengthen both ability to identify non-HEP career opportunities for students and post-docs as well as help more fully develop skills such as effective networking, resume building, project management, risk assessment, budget planning, to name a few. This will help prepare early career HEPA scientists for successfully transitioning from academia to the diverse array of non-traditional careers available. HEPA alumni can play a pivotal role by engaging in this process.

physics.ed-ph↗

arXiv2022

Particle Physics Outreach to K-12 Schools and Opportunities in Undergraduate Education

To develop an increase in societal interest in the fundamental sciences of particle physics and particularly for maintaining the support structures needed to succeed in experiments that take several decades to develop and complete, requires strong educational back-grounding at all levels of the instructional system and notably at early stages in the process. While many (particularly young) students might show an early interest and aptitude for science and mathematics at the elementary level, the structures are not necessarily in place to capture, nurture and develop such nascent interests. To encourage and strengthen such interests, strong connections must be made at K-12 and Undergraduate levels. The paper discusses the on-going efforts and makes recommendations.

physics.ed-ph↗

arXiv2022

Broadening the scope of Education, Career and Open Science in HEP

High Energy Particle Physics (HEP) faces challenges over the coming decades with a need to attract young people to the field and STEM careers, as well as a need to recognize, promote and sustain those in the field who are making important contributions to the research effort across the many specialties needed to deliver the science. Such skills can also serve as attractors for students who may not want to pursue a PhD in HEP but use them as a springboard to other STEM careers. This paper reviews the challenges and develops strategies to correct the disparities to help transform the particle physics field into a stronger and more diverse ecosystem of talent and expertise, with the expectation of long-lasting scientific and societal benefits.

physics.ed-ph↗

arXiv2022

Software Training in High Energy Physics

Among the upgrades in current high energy physics (HEP) experiments and the new facilities coming online, solving software challenges has become integral for the success of the collaborations, The demand for human resources highly-skilled in both HEP and software domains is increasing. With a highly distributed environment in human resources, the sustainability of the HEP ecosystem requires a continuous effort in the equipment of physicists with the required abilities in software development. In this paper, the collective software training program in HEP and its activities led by the HEP Software Foundation (HSF) and the Institute for Research and Innovation in Software in HEP (IRIS-HEP) are presented. Experiment-agnostic, open, and accessible modules for training have been developed, focusing on common software material with ranges from core software skills needed by everyone to advanced training required to produce high-quality sustainable software. A basic software curriculum was built, and an introductory software training event has been prepared to serve HEP entrants. This program serves individuals with transferable skills that are becoming increasingly important to careers in the realm of software and computing, whether inside or outside HEP.

physics.ed-ph↗

arXiv2021

Software Training in HEP

Long term sustainability of the high energy physics (HEP) research software ecosystem is essential for the field. With upgrades and new facilities coming online throughout the 2020s this will only become increasingly relevant throughout this decade. Meeting this sustainability challenge requires a workforce with a combination of HEP domain knowledge and advanced software skills. The required software skills fall into three broad groups. The first is fundamental and generic software engineering (e.g. Unix, version control,C++, continuous integration). The second is knowledge of domain specific HEP packages and practices (e.g., the ROOT data format and analysis framework). The third is more advanced knowledge involving more specialized techniques. These include parallel programming, machine learning and data science tools, and techniques to preserve software projects at all scales. This paper dis-cusses the collective software training program in HEP and its activities led by the HEP Software Foundation (HSF) and the Institute for Research and Innovation in Software in HEP (IRIS-HEP). The program equips participants with an array of software skills that serve as ingredients from which solutions to the computing challenges of HEP can be formed. Beyond serving the community by ensuring that members are able to pursue research goals, this program serves individuals by providing intellectual capital and transferable skills that are becoming increasingly important to careers in the realm of software and computing, whether inside or outside HEP

hep-ex↗