Established in 2011, the Chancellor’s Entrepreneurial Achievement Award recognizes UW–Madison innovators and alumni who have contributed to economic growth and the social good, serving as entrepreneurial models for the UW community and inspiring the campus culture of entrepreneurship.
Read the full article at: https://news.wisc.edu/entrepreneur-award-winners-turn-ideas-into-impact-from-farming-to-fashion-to-fusion/Month: April 2025
Search for boosted Higgs advances our understanding of dark matter
This story, featuring physics graduate student Shivani Lomte, was originally published by the CMS collaboration
The CMS Collaboration hunts for Higgs bosons recoiling against dark matter particles
Dark matter is one of the most perplexing mysteries of our universe, accounting for roughly 27% of its total energy. Dark matter does not emit, absorb, or reflect light, and is thus invisible to telescopes. However, its effects on gravitation are unmistakable. Although dark matter’s elementary nature remains unknown, scientists hypothesize that it might be made up of weakly interacting massive particles (WIMPs) that rarely interact with ordinary matter.
In the CMS experiment, we use the fundamental law of momentum conservation to infer the possible presence of dark matter in the detector. In particular the momentum in the transverse plane should be conserved before and after the proton-proton (pp) collision – in other words, the sum of all particle momenta combined should balance out. If momentum is missing, then this suggests that an ‘invisible’ particle, for instance a dark matter particle, has carried that momentum away. Since dark matter doesn’t interact with the detectors, we can’t directly observe it. To detect its presence, we use a ‘visible’ known particle that recoils against the dark matter particle, providing a detectable signal in the experiment. An example of this type of process is shown in Fig. 1.
In pp collisions, a photon, ‘jet’, W or Z boson can be emitted from the initial quark within the proton, whereas radiating a Higgs boson is extremely rare given its small coupling to the quarks. Higgs bosons might be preferentially emitted through a new particle acting as a ‘mediator’ between the standard model and dark matter sector. There is a unique possibility at LHC to produce the mediator particle and study its interaction with the standard model and dark matter.
This analysis uses the “mono-Higgs” signature to search for dark matter particles, focusing on two scenarios that both involve Higgs bosons decaying to bottom quarks. If the Higgs boson is highly energetic (boosted), its decay products become collimated and can be reconstructed in a single large-radius ‘jet’. Alternatively, if the Higgs is not as energetic, we instead look for two small-radius jets, one from each bottom quark. The two scenarios are illustrated in Fig. 2.
“A key challenge in this search is that the dark matter signal is rare (at best) and well-known processes, as described in the standard model, produce very similar signatures. To reduce the backgrounds from known particles, we use distinguishing features like the momentum and energy distribution of the particles” says Shivani Lomte, a graduate student at the University of Wisconsin-Madison, leading this search. The precise estimation of the background is critical and is achieved using so called control regions in the data. Such control regions are dominated by background processes and this allows us to quantify the amount of backgrounds in the signal region where we search for dark matter.
In this analysis, once the backgrounds were well-understood, we looked for the dark matter signal by comparing the observed data distributions to the predicted backgrounds, looking for discrepancies. Unfortunately, the observed data agrees with the standard model predictions, and so we conclude that our result has no sign of dark matter. We can thus rule out those types of dark matter particles that would have been detected if they existed.
Regardless of the outcome, the search for dark matter is a journey that pushes the boundaries of human knowledge. Each step brings us closer to answering some of the most profound questions about the nature of the universe and our place within it.
Dark matter and pencil jets: The search for a low-mass Z’ boson using machine learning
This story, featuring physics grad student Abhishikth Mallampalli, was originally published by the CMS collaboration
The CMS experiment conducts the first search for dark matter particles produced in association with an energetic narrow jet—the pencil jet.
Dark matter remains one of physics’ greatest mysteries. Despite making up about 27% of the universe’s energy content, its true nature is unknown. Astonishingly, all ordinary matter—which includes stars, planets, our phones, the wires transferring data, the waves carrying WiFi, you and me—accounts for just 5% of the total energy content of our universe. If our known world is so diverse, the dark sector, which outweighs it 20-to-1, could be just as rich. At CERN’s CMS experiment, scientists are searching for dark matter particles, aiming to reveal their interactions and revolutionize our understanding of our universe.
But if dark matter is so abundant, why haven’t we detected it? As the name suggests, dark matter does not interact with light (electromagnetism) with the same strength as ordinary matter (the behavior of which is explained by the Standard Model) and, so far, is only known to interact with known particles through gravity, the weakest of the four known fundamental forces. At CMS, scientists use momentum conservation to infer the presence of dark matter: missing momentum in particle collisions (after accounting for detector mismeasurements) could signal an invisible particle, possibly dark matter, slipping away undetected.
In addition to particles that make up dark matter, there could be as yet undetected particles that mediate interactions between the dark particles and the matter particles. These are creatively called, you guessed it, mediators. Such mediators are bosons, implying that they carry integer spin quantum numbers as opposed to fermions (e.g. electrons) which have half-integer spins. One such mediator is the hypothesized Z’ boson, which is electrically neutral and has spin quantum number of 1.
Typical CMS searches focus on heavy Z’ particles in the hundreds of GeV to TeV range, but a lighter Z’ boson could also exist in the dark sector. It is typically a lot more challenging to look for such light particles due to the overwhelming background from hadronic resonances and quantum chromodynamics (QCD) processes—related to the strong nuclear force—which are poorly modeled at low energies. This is where techniques like data augmentation and machine learning can be utilized, enhancing sensitivity to Z’ decays while suppressing known background processes.
The Z’ boson mass that we are looking for in this search is around 1 GeV, and because of the low mass and high boost (momentum) of the Z’ boson, it can only decay to light quarks (u, d, s), which then hadronize to form a jet (a spray of particles) with a lower number of constituents than usual. We then look for dark matter recoiling against such a narrow jet (called a pencil jet). This is the first search at the LHC for this signal. Various selections are applied to reduce the background processes while retaining the signal process and a combination of neural networks and boosted decision trees are used to further extract the signal. Multiprocessing techniques are used to speed up the processing time of the events.
“The main challenge in this analysis of real-world data was that the physically motivated input features aren’t typically well modeled in our simulations and so we had to take steps to ensure model robustness. We showed that using machine learning can help us achieve up to 10 times more sensitivity to these rare signal processes compared to traditional strategies” says Abhishikth Mallampalli, a graduate student at the University of Wisconsin-Madison, leading this search. Statistical hypothesis testing is used to determine whether the observed data agrees with the standard model prediction or suggests the presence of a dark matter signal.
We see that the data agrees well with the standard model expectation across the three years of proton-proton collision data analyzed. While this means that such a Z’ boson with the probed light masses might not exist in our universe at the 95% confidence level, null results in such searches for dark matter not only solidify the standard model but also serve as guidance to theorists in building new physics models for dark matter, and help experimentalists to identify the direction for future searches.
UW–Madison scientists part of team awarded Breakthrough Prize in Physics
A team of 13,508 scientists, including over 100 from the University of Wisconsin–Madison, won the 2025 Breakthrough Prize in Fundamental Physics, the Breakthrough Prize Foundation announced April 5. The Prize recognized work conducted at CERN’s Large Hadron Collider (LHC) between 2015 and 2024.
The Breakthrough Prize was created to celebrate the wonders of our scientific age. The $3 million prize will be donated to the CERN & Society Foundation, which offers financial support to doctoral students to conduct research at CERN.
Four LHC projects were awarded, including ATLAS and CMS, both of which UW–Madison scientists work on. ATLAS and CMS jointly announced the discovery of the Higgs boson in 2012, and its discovery opened up many new avenues of research. In the years since, LHC researchers have worked towards a better understanding of this important particle because it interacts with all matter and gives other particles their mass. Both teams are actively engaged in analyzing LHC data in search of exciting and new physics.
“The LHC experiments have produced more than 3000 combined papers covering studies of electroweak physics and the Higgs boson, searches for dark matter, understanding quantum chromodynamics, and studying the symmetries of fundamental physics,” says CMS researcher Kevin Black, chair of the UW–Madison department of physics. “This work represents the combined contributions of many thousands of physicists, engineers, and computer scientists, and has taken decades to come to fruition. We are all very excited to be recognized with this award.”
ATLAS and CMS have generally the same research goals, but different technical ways of addressing them. Both detectors probe the aftermath of particle collisions at the LHC and use the detectors’ high-precision measurements to address questions about the Standard Model of particle physics, the building blocks of matter and dark matter, exotic particles, extra dimensions, supersymmetry, and more.
The ATLAS team at UW–Madison has taken a leadership role in both physics analyses and computing. They have spearheaded precision measurements of the Higgs boson’s properties and conducted extensive searches for new physics, including Dark Matter, achieving major sensitivity gains through advanced AI and machine learning techniques. In addition to leading developments in computing infrastructure, the team has played a crucial role in the High-Level Trigger system and simulation efforts using generative AI, further enhancing the experiment’s capabilities.
The CMS team at UW–Madison has played and continues to play key roles in trigger electronics systems, which are ways of sorting through the tens of millions of megabytes of data produced each second by a collider experiment and retaining the most meaningful events. They also manage a large computing cluster at UW-Madison, contribute to the building and operating of muon detectors, make key contributions to CMS trigger and computing operations, and develop physics analysis techniques including AI/ML. The CMS group efforts are well recognized in the recently published compendium of results, dubbed, the Stairway to Heaven.
CMS and ATLAS research at UW–Madison is largely supported by the U.S. Department of Energy, with additional support from the National Science Foundation.
The following people had a UW–Madison affiliation during the time noted by the Prize:
Current Professors
Kevin Black, Tulika Bose, Kyle Cranmer, Sridhara Dasu, Matthew Herndon, Sau Lan Wu
Current PhD Physicists
Pieter Everaerts, Matthew Feickert, Camilla Galloni, Alexander Held, Wasikul Islam, Charis Koraka, Abdollah Mohammadi, Ajit Mohapatra, Laurent Pétré, Deborah Pinna, Jay Sandesara, Alexandre Savin, Varun Sharma, Werner Wiedenmann
Current Graduate Students
Anagha Aravind, Alkaid Cheng, He He, Abhishikth Mallampalli, Susmita Mondal, Ganesh Parida, Minh Tuan Pham, Dylan Teague, Abigail Warden
Current Engineering Staff
Shaojun Sun
Current Emeriti
Sunanda Banerjee (Senior Scientist), Richard Loveless (Distinguished Senior Scientist), Wesley H. Smith (Professor)
Alumni
Michalis Bachtis (Ph.D. 2012), Swagato Banerjee (Postdoc 2015), Austin Belknap (Ph.D. 2015), James Buchanan (Ph.D. 2019), Cecile Caillol (Postdoc), Duncan Carlsmith (Professor), Maria Cepeda (Postdoc), Jay Chan (Ph.D. 2023), Stephane Cooperstein (B.S. 2014), Isabelle De Bruyn (Scientist), Senka Djuric (Postdoc), Laura Dodd (Ph.D. 2018), Keegan Downham (B.S. 2020), Evan Friis (Postdoc), Bhawna Gomber (Postdoc), Lindsey Gray (Ph.D. 2012), Monika Grothe (Scientist), Wen Guan (Engineer with PhD 2022), Andrew Straiton Hard (Ph.D. 2018), Yang Heng (Ph.D. 2019), Usama Hussain (Ph.D. 2020), Haoshuang Ji (Ph.D. 2019), Xiangyang Ju (Ph.D. 2018), Laser Seymour Kaplan (Ph.D. 2019), Lashkar Kashif (Postdoc 2019), Pamela Klabbers (Scientist), Evan Koenig (BS 2018, Intern), Amanda Kaitlyn Kruse (Ph.D. 2015), Armando Lanaro (Senior Scientist), Jessica Leonard (Ph.D. 2011), Aaron Levine (Ph.D. 2016), Andrew Loeliger (Ph.D. 2022), Kenneth Long (Ph.D. 2019), Jithin Madhusudanan Sreekala (Ph.D. 2022) Yao Ming (Ph.D. 2018), Isobel Ojalvo (Ph.D. 2014, Postdoc), Lauren Melissa Osojnak (Ph.D. 2020), Tom Perry (Ph.D. 2016), Elois Petruska (BS, 2021), Yan Qian (Undergraduate Student 2023), Tyler Ruggles (Ph.D. 2018, Postdoc), Tapas Sarangi (Scientist), Victor Shang (Ph.D. 2024), Manuel Silva (Ph.D. 2019), Nick Smith (Ph.D. 2018), Amy Tee (Postdoc, 2023), Stephen Trembath-Reichert (M.S. 2020), Ho-Fung Tsoi (Ph.D. 2024), Devin Taylor (Ph.D. 2017), Wren Vetens (Ph.D. 2024), Alex Zeng Wang (Ph.D. 2023), Fuquan Wang (Ph.D. 2019), Nate Woods (Ph.D. 2017), Hongtao Yang (Ph.D. 2016), Fangzhou Zhang (Ph.D. 2018), Rui Zhang (Postdoc, 2025), Chen Zhou (Postdoc 2021)
Three grad students recognized as L&S Teaching Mentors
Physics PhD students Sam Kramer, Michelle Marrero Garcia, and Isaac Barnhill were recently named to the L&S Teaching Mentors program. The L&S Teaching Mentors are the heart of L&S’s Teaching Assistant (TA) Trainings. They are exceptionally passionate and knowledgeable teachers with proven track records for teaching excellence who work closely with the L&S TA Training and Support Team to facilitate various trainings and mentor L&S TAs.
Kramer and Marrero Garcia earned Lead Teaching Mentor designation, meaning that they have served as Teaching Mentors more than once and are taking on an additional leadership role within the program.
Learn more about the three Physics Teaching Mentors:
Isaac Barnhill, Teaching Mentor
Isaac began teaching as a peer mentor tutor in the UW Physics Learning Center during undergraduate studies. Now a PhD student in the Physics Department, Isaac has primarily taught electromagnetism, circuits, and optics at the introductory level. Isaac’s research is focused on increasing student agency and decision making in the laboratory component of their physics classes. By shifting the focus of lab activities from content reinforcement to engaging in authentic scientific practices, Isaac hopes to increase students’ sense of engagement and intellectual ownership in the classroom while simultaneously helping students build their data literacy and critical thinking skills. One of his favorite aspects of teaching is seeing students improve their ability to understand, describe, and predict the physical world around them. He always seeks to center the student by promoting active learning in the classroom, allowing students to work out their thoughts in an environment with both high expectations and high support.
Michelle Marrero Garcia, Lead Teaching Mentor
Michelle started teaching in her first semester of the Physics PhD program. She has taught either kinematics or electromagnetism at the introductory level (every semester since then), but she loves teaching any subject within Physics. Her favorite part is watching the face of her students light up as they explore the world through a new lens. In Michelle’s approach to teaching, she always tries to be empathic and put herself in the student’s position. She has found that having changed her field of study from mechanical engineering (as an undergrad) to physics (as a grad) gave her the ability to understand how students that are new to the subject think and feel.
Sam Kramer, Lead Teaching Mentor
Sam is a third-year Ph.D. candidate in the Department of Physics and has been teaching for Physics 202, a course for engineering major undergraduates that focuses on electricity, magnetism, and optics, since arriving in Madison. Sam also taught for a similar course as an undergraduate at Saint Louis University. In this role, he leads both discussions, which focus on problem solving, and labs, which provide hands-on experience with the concepts being taught. Physics can be an overwhelming subject, so Sam tries to distill the material into manageable chunks for the students, emphasizing the broader concepts underlying the formulas students use and drawing explicit connections between parts of the curricula. This is meant to develop the dynamic problem solving skills students need when encountering problems they have not seen before.