Siyuan Sun (Harvard) "Gaining Sensitivity to New Physics with a Compressed Mass Spectra at the ATLAS Experiment"

ABSTRACT
The ATLAS experiment at Large Hadron Collider (LHC) searches for experimental ev-
idence of many new beyond the standard model physics at the TeV scale. As we collect
more data at the LHC we continue to extend our sensitivity to these new phenomenon,
particularly probing increasingly more massive new particles. Despite this progress there
are still regions of parameter space where constraints remain weak. One common cause of
this lack of sensitivity is because the new particle has a very small mass splitting between it
and its decay products. The particle then has little energy left over to give momenta to its
decay products and the low momenta decay products are difficult to experimentally detect.
These regions of small mass splitting are called compressed regions. We are able to gain
sensitivity to these difficult regions by searching for new particles produced in conjunction
with strong initial state radiation (ISR). The strong initial state radiation boosts the new
particle’s decay products and gives them momentum.
In this seminar, I will cover in detail the search for the supersymmetric partner to the
top quark (stop) in the region when the stop and its decay products are nearly degenerate in
mass. No searches prior to 2016 was sensitive to this region. We were able to exclude stops
up to a mass of 425 GeV in this region with the 2015 and summer 2016 ATLAS dataset. I will
demonstrate a new and more accurate technique for identifying whole initial state radiation
systems instead of a single ISR jet. As the LHC provides more data and traditional search
methods rule out parameter space at higher masses, it becomes more important that we also
gain sensitivity to these compressed regions that are still unconstrained at low masses. I will
show that this initial state radiation identification technique is completely generalizable and
useful for many other searches that target small mass splittings.