Rikab Gambhir
Theoretical Physicist
About Me
I am a physics PhD student at the Massachusetts Institute of Technology (MIT). My background is in particle physics, mathematics, data science, and applied sciences in engineering.
Interests
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Particle Physics
Quantum Field Theory
Jets and QCD
Beyond the Standard Model
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Data Science
Machine Learning
Optimal Transport
Education
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PhD Candidate in Physics, 2020-
Massachusetts Institute of Technology
Advisor: Jesse Thaler -
Bachelor of Science in Physics, Mathematics, and Applied Science 2016-2020
Rutgers University
summa cum laude, Highest Honors
Advisor: Stephen Schnetzer
RESEARCH & PUBLICATIONS
I am primarily interested in the intersection between theoretical particle physics and modern machine learning methods. How can we use machine learning to understand particle physics, and how can we use particle physics to understand machine learning?
The New Physics Case for Beam-Dump Experiments with Accelerated Muon Beams
Cari Cesarotti and Rikab Gambhir
24 October 2023
[arXiv Preprint]
We demonstrate the utility of muon beam-dump experiments for new physics searches at energies from 10 GeV to 5 TeV. We find that, even at low energies like those accessible at staging or demonstrator facilities, it is possible to probe new regions of parameter space for a variety of generic BSM models.
SHAPER: Can You Hear the Shape of a Jet?
Demba Ba, Akshunna S. Dogra, Rikab Gambhir, Abiy Tasissa, and Jesse Thaler
26 February 2023
[arXiv Preprint]
We show that the Earth Mover's Distance is the natural structure for comparing collider events and constructing IRC-safe observables. We also present SHAPER, a framework for defining and computing shape-based observables, and show how these new observables may be useful for phenomenological studies.
Bias and Priors in Machine Learning Calibrations for High Energy Physics
Rikab Gambhir, Benjamin Nachman, and Jesse Thaler
11 May 2022
[arXiv Preprint] [INSPIRE-HEP]
We demonstrate how some recent proposals for both simulation-based and data-based calibrations inherit properties of the sample used for training, which can result in biases for downstream analyses, and argue that our recently proposed Gaussian Ansatz approach can avoid some of the pitfalls of prior dependence.
Learning Uncertainties the Frequentist Way: Calibration and Correlation in High Energy Physics
Rikab Gambhir, Benjamin Nachman, and Jesse Thaler
9 May 2022
[arXiv Preprint] [INSPIRE-HEP]
We present a machine learning framework for performing maximum likelihood inference with Guassian uncertainty estimation, which also quantifies the mutual informaiton between the unobservable and measured quantities.
Search for bottom-type, vectorlike quark pair production in a fully hadronic final state in proton-proton collisions at s= 13 TeV
The CMS Collaboration
7 December 2020
Physical Review D 102 (11), 112004
We search for evidence of Vector-Like Quark (VLQ) production in the CMS Detector of the Large Hadron Collider's entire second run, and set (as of publication) the highest mass limits on VLQ hadronic decays
PRESENTATIONS
Below is a list of all public presentations I have given, organized by topic (in no particular order). Note that while several talks share the same category and even the same title, the talks themselves can be very different, especially if the format (invited seminar versus talk, for example) is different! Unfortunately, many slides originally had GIFs and animations, which will not render here as PDFs.
Items marked with a red star (★) are my recommended versions for viewing!
Open Data
★ "How Do I Take my Cup of CMS Open Data?", Invited Talk, 11 July 2023, at Fermilab CMS Open Data Workshop 2023. Fermilab, Illinois, United States. Slides [PDF]
Can You Hear the Shape of a Jet?
★Can You Hear the Shape of a Jet?", Internal Seminar, 17 March 2022, for the MIT CTP Graduate Seminar. MIT: Massachusetts, United States. Slides [PDF]
"Can You Hear the Shape of a Jet?", Talk, 4 Novemebr 2022, at ML4Jets2022. Rutgers University: New Jersey, United States. Slides [PDF]
"Can You Hear the Shape of a Jet?", Talk, 15 August 2022, at BOOST 2022. University of Hamburg: Hamburg, Germany. Slides [PDF]
"Can You Hear the Shape of a Jet? An IAIFI Story", Local Presentation, 11 August 2022, for IAIFI/NSF Review. MIT: Massachusetts, United States. Slides [PDF]
"Can You Hear the Shape of a Jet?", Talk, 10 April 2022, at APS April Meeting 2022. New York City: New York, United States. Slides [PDF]
Moment Pooling
"Moment Pooling: Gaining Performance and Interpretability Through Physics Inspired Product Structures", Talk, 10 April 2023, at APS April Meeting 2023. Minneapolis, Minesota, United States. Slides [PDF]
Learning Uncertainties the Frequentist Way: Calibration and Correlation in High Energy Physics
"Learning Uncertainties the Frequentist Way: Calibration and Correlation in High Energy Physics", Poster, 3 December 2022, at Machine Learning and the Physical Sciences. NeurIPS (Virtual). Poster [PNG]
![Poster [PNG]](./assets/talks/frequentist_way_poster.png){kind=link}
★"Learning Uncertainties the Frequentist Way: Calibration and Correlation in High Energy Physics", Invited Talk, 22 Novemebr 2022, for ATLAS Jet/Etmiss. CERN (Virtual). Slides [PDF]
"Learning Uncertainties the Frequentist Way: Calibration and Correlation in High Energy Physics", Invited Seminar, 13 September 2022, for the UCI Physics Astro/Particle-ML Seminar series. University of Califoria, Irvine (Virtual). Slides [PDF]
"Learning Uncertainties the Frequentist Way: Calibration and Correlation in High Energy Physics", Talk, 7 July 2021, at ML4Jets2021. University of Heidelberg (Virtual). Slides [PDF]