The Miller Lab at The University of Chicago


The Miller Lab explores the properties of fundamental particles at the edge of current technologies, using the highest energy proton-proton collisions ever produced in a lab at the Large Hadron Collider at CERN in Geneva, Switzerland using the novel instrumentation of the ATLAS Experiment, dedicated high-speed electronics and real-time data processing, and cutting-edge data analysis algorithms.


 

Triggering on Lorentz-boosted objects

Complex objects often slip past our data filtering systems (triggers). By constructing extremely fast, sophisticated electronics systems that specifically search for these complex structures in real-time, we can recover these otherwise lost objects. This is the gFEX boosted object trigger for Run 3 of the LHC.

Exploring the Standard Model in extreme conditions

The Standard Model offers detailed predictions of the interactions of quarks and gluons with massive gauge bosons. Tests of these predictions at the most extreme energies accessible in the lab shed more light on our current understanding of the theoretical tools and the theory itself, as well as aiding in the search for new physics.

 

Searching for milli-charged particles

Though over a quarter of the mass-energy of the universe is widely thought to be some kind of non-luminous dark matter (DM), all experiments to date have failed to directly detect it, much less measure its properties. The milliQan experiment offers a new approach to detecting new fractionally-charged matter that could constitute a component of the dark matter.

Searching for axions and dark photons

Axions are a leading candidate for both Dark Matter and as a solution to the Strong CP problem in the Standard Model of Particle Physics. Our group is embarking on a new broadband (wide mass range) search for axions using a novel reflector concept that aims for sensitivity in the THz (meV) parameter range. As part of the R&D process, we've built a THz Fourier Transform Spectrometer to characterize the detector components.

 

Machine Learning for Particle Physics

Particle Physics has benefitted from, and in many ways strengthened and advanced, progress in AI/ML for decades due to its proliferation of enormous data sets, complex instrumentation, and computing infrastructure. Our group is engaged in efforts to target important problems relevant to the use of of machine learning, symmetries, and domain knowledge in particle physics.

Paper Accepted 03/04/2022

Jesse LiuKristin DonaGabe Hoshino

BREAD paper [arXiv:2111.12103] accepted to Physical Review Letters (PRL)!! Congratulations to Jesse Liu, Kristin Dona, and Gabe Hoshino, along with our close collaborators at Fermilab, Andrew Sonnenschein and Stefan Knirck, and all of the other contributors to this result!

Major milestone 02/24/2022

MillerLab

gFEX successfully included into a run with other ATLAS subdetectors (during milestone run M12)!

Welcome 01/02/2022

Benjamin Rosser

Welcome to new group member and McCormick Fellow Ben Rosser! Ben will primarily work with Mel Shochet and Young-Kee Kim on Tracking for the Event Filter trigger system, but will also liaise with our group on topics related to the Global Event Processor (GEP) trigger system for the HL-LHC.

11/24/2021

Emily Smith

Emily Smith integrates the gFEX Detector Control System (DCS) Project into the overall ATLAS control system!

Calorimetry

Calorimetry

High-Speed Electronics

High-Speed Electronics

Jet Substructure & Boosted Objects

Jet Substructure & Boosted Objects

Standard Model Measurements

Standard Model Measurements

Searches for New Physics

Searches for New Physics

MilliQan Experiment

MilliQan Experiment

Axion Searches

Axion Searches

Machine Learning for Particle Physics

Machine Learning for Particle Physics

Jesse Liu, Kristin Dona, Gabe Hoshino, Stefan Knirck, Noah Kurinsky, Matthew Malaker, David Miller, Andrew Sonnenschein, and other collaborators, "Broadband solenoidal haloscope for terahertz axion detection" Preprint available at arXiv:2111.12103, accepted at PRL on March 4th, 2022

Kristin Dona, Jesse Liu, Noah Kurinsky, David Miller, Pete Barry, Clarence Chang, Andrew Sonnenschein, "Design and performance of a multi-terahertz Fourier transform spectrometer for axion dark matter experiments" Preprint available at arXiv:2104.07157, to be submitted to the Journal of Infrared, Millimeter, and Terahertz Waves

Chinmaya Mahesh, Kristin Dona, David W. Miller, Yuxin Chen, "Towards an Interpretable Data-driven Trigger Systemfor High-throughput Physics Facilities" NeurIPS Workshop on Machine Learning and the Physical Sciences (also at arXiv:2104.06622)

Jona Bossio (McGill), Kate Pachal (Duke), David Miller (+ ATLAS collaborators), "Jet energy scale and resolution measured in proton-proton collisions at sqrt(s)=13 TeV with the ATLAS detector" Eur. Phys. J. C 81 (2021) 689 [arXiv:2007.02645]

Alexander Bogatskiy, Brandon Anderson, Jan T. Offermann, Marwah Roussi, David W. Miller, Risi Kondor, "Lorentz Group Equivariant Neural Network for Particle Physics" Accepted by ICML 2020 [arXiv:2006.04780]

milliQan Collaboration (incl. David Miller, Max Swiatlowski, and Henry Zheng from UChicago), "Search for millicharged particles in proton-proton collisions at sqrt(s)=13 TeV" Phys. Rev. D 102, 032002 (2020) [arXiv:2005.06518]

University of Chicago
Physical Sciences Division
Physics Department
Enrico Fermi Institute
College
NSF
DOE
UChicago
Neubauer
Chicago France Center
Center for Data and Applied Computing