Searching for new physics with the Higgs boson and dark matter

There are a number of shortcomings of the Standard Model, indicating the existence of new 'Beyond Standard Model' physics, including the lack of a satisfactory dark matter candidate.

The discovery of the Higgs boson gives us a powerful tool to search for new physics, dark matter, additional Higgs bosons and unexpectedly frequent Higgs boson pair ("di-Higgs") production which would imply new phenomena.

We use the ATLAS collision dataset from LHC Run-2 and the ongoing Run-3 to search for new particles and phenomena. These powerful datasets are very sensitive, opening up new channels and signatures for discovery.

Our explorations of Higgs pair production have focussed on the '4b' channel, in which both Higgs bosons decay to a pair of b-quarks.
Recent student thesis have centred around searches for new particles decaying to two Higgs bosons in the resolved, and higher-momentum 'boosted' channel; the first use of the vector boson fusion (VBF) process to constrain the non-resonant Higgs pair production; and the full Run-2 result and first exploration of effective field theory in this signature.

Dark Matter
There are a wide variety of well-motivated models for dark matter and how it might interact with the Standard Model particles we know well. Weakly interacting dark matter will be invisible to the detector, and its presence must be inferred from momentum imbalance ("missing ET").
One elegant means for dark matter to interact is through a Higgs boson. We have been exploring this possibility, through searches for mono-Higgs signatures, a complete signature exploration of the "2HDM+a" extended Higgs sector model, and searches for dark Higgs bosons.

Machine Learning & B-tagging
Our signatures involve invisible particles and Higgs boson decays to a b-quark jet pair. There are large backgrounds to these processes, and so advanced methods to reduce them are a priority.
We work on enhancing our ability to identify b-quark jets in the detector and determine the amount and significance of missing energy, employing a widening range of machine learning techniques, such as adversarial and graph neural networks, here and in analysis.

Contact: Todd Huffman and James Frost

Current Members

Todd Huffman

James Frost

Holly Pacey

Tim Brueckler

Maggie Chen

Tom Dingley

Former Members

Cigdem Issever

Bill Balunas

Lydia Beresford

Nufikri bin Norjoharuddeen

Beojan Stanislaus

Santiago Parades

Migle Stankaityte

James Grundy

Federico Celli

Iza Veliscek