Schwartzman Lab
SLAC National Accelerator Laboratory
Stanford University
The Schwartzman lab explores the universe at its most fundamental level using the highest energy hadron collider experiment in the world and novel large-scale quantum atomic sensor networks. At he ATLAS Experiment at CERN, we look for new exotic decays of the Higgs boson that could be connected with the mystery of dark matter, develop deep learning algorithms for pattern recognition and physics event reconstruction with an emphasis in jets, and investigate ultrafast-timing detectors for future detectors. We also participate in the MAGIS-100 Experiment at Fermilab, the world largest atom interferometer (now under construction) that will search for ultralight wave dark matter, test quantum mechanics at record-breaking distances and times, and serve as a prototype detector for a future Km-scale gravitational wave detector in the mid frequency band which could probe the Higgs boson potential beyond the reach of future particle colliders. We are developing a novel single-shot light-field 3D imaging system for MAGIS-100, as well as its diagnostic imaging system for atom trajectory control and calibration. Our lab provides exciting opportunities for undergraduate and graduate students to learn and develop new detectors, novel machine learning algorithms, and innovative statistical analysis methods at the intersections of high energy physics, machine learning, and quantum information science.
Expanding the exploration of the Higgs boson and searches for new scalar particles with the ATLAS Detector at the LHC
We are pursuing novel searches for light scalar particles decaying to pairs of gluons, photons, and charm- and light-quarks never done before. For this goal, we are developing sophisticated deep learning algorithms that are able to capture the unique features of these Beyond the Standard Model physics signals.
Jet substructure and Machine Learning
Our group pioneered the concept of "jet-images" for the analysis of jet substructure. We utilize the latest advances in machine learning to explore new physics signals and extend the physics potential of the LHC.
Long-baseline Atomic Sensors for Ultralight wave dark matter and Gravitational Wave detection: MAGIS-100 Experiment
Over the last several years, the emergence of quantum sensors of unprecedented sensitivities has enabled new opportunities to probe the physics of the universe. Long-baseline atomic sensors can allow direct searches for ultralight wave-like dark matter at sensitivities orders of magnitude beyond current limits. Kilometer-scale baselines open the prospect of exploration of the gravitational wave spectrum in a new frequency range, between the peak sensitivities of LIGO and LISA, that is particularly sensitive to cosmological signals from the early universe. Our group is developing novel imaging hardware, software, simulation, and analysis methods for MAGIS-100, the largest next-generation atom interferometer to search for new physics.
4-Dimensional Tracking and Fast timing layers for Time-of-Flight and calorimetry for future collider experiments
Our group is designing and testing the next generation of fast-timing readout chips in 28nm technology to enable 10ps resolution in high-granularity detectors for future colliders. Potential applications are the Run 5/6 upgrade of the ATLAS inner pixel detector of the HL-LHC, timing layers for tracking, calorimetry, and particle-ID reconstruction at future lepton colliders detectors such as SiD, and integrated 4D (space-time) tracking layers for a future muon collider detector. We are involved in detector design, physics feasibility studies, and testing of prototypes. This effort builds on our previous experience developing the physics case of the ATLAS High Granularity Timing Detector (HGTD) and designing the time-to-digital convertor of its readout chip (ALTIROC2) with a time resolution of 20ps.
