Skip to main content

LUP Student Papers

LUND UNIVERSITY LIBRARIES

Energy reconstruction with artificial neural networks on LDMX simulations

Magdalinski, Daniel LU (2020) FYSK02 20201
Particle and nuclear physics
Department of Physics
Abstract
It is clear from evidence such as rotational curves and cosmic microwave background
measurements that dark matter exists. The light dark matter experiment (LDMX) will
search for dark matter in the sub-GeV range. It will do this using missing-momentum
measurements of electrons interacting with a Tungsten target. The electron will recoil and
be measured in the electromagnetic calorimeter (ECal) of the experiment. The accuracy of
this measurement is vital for the result of the experiment. Therefore, the ECal design will
draw from the Phase-II high granularity upgrade of the Compact muon solenoid (CMS)
forward ECal.
This thesis have investigated the possibility of using artificial neural networks (ANNs) to
improve the energy... (More)
It is clear from evidence such as rotational curves and cosmic microwave background
measurements that dark matter exists. The light dark matter experiment (LDMX) will
search for dark matter in the sub-GeV range. It will do this using missing-momentum
measurements of electrons interacting with a Tungsten target. The electron will recoil and
be measured in the electromagnetic calorimeter (ECal) of the experiment. The accuracy of
this measurement is vital for the result of the experiment. Therefore, the ECal design will
draw from the Phase-II high granularity upgrade of the Compact muon solenoid (CMS)
forward ECal.
This thesis have investigated the possibility of using artificial neural networks (ANNs) to
improve the energy resolution of the ECal. This was performed on simulation data based
on the LDMX framework. Both convolutional neural networks (CNNs) and dense neural
networks (DNNs) were trained on the data and compared with a linear fit between ECal
readout energy and the original electron energy.
The analysis have shown that CNNs can improve the energy resolution of the ECal compared to both the DNN and linear fit who perform similarly. Some inconsistencies in how
the models performed on different energies was discovered. Finally, solutions to this and
suggestions for future work is discussed. (Less)
Please use this url to cite or link to this publication:
author
Magdalinski, Daniel LU
supervisor
organization
course
FYSK02 20201
year
type
M2 - Bachelor Degree
subject
keywords
Dark matter, LDMX, electromagnetic calorimeter, neural networks, convolutional neural networks
language
English
id
9017561
date added to LUP
2020-06-16 09:22:49
date last changed
2020-06-16 09:22:49
@misc{9017561,
  abstract     = {{It is clear from evidence such as rotational curves and cosmic microwave background
measurements that dark matter exists. The light dark matter experiment (LDMX) will
search for dark matter in the sub-GeV range. It will do this using missing-momentum
measurements of electrons interacting with a Tungsten target. The electron will recoil and
be measured in the electromagnetic calorimeter (ECal) of the experiment. The accuracy of
this measurement is vital for the result of the experiment. Therefore, the ECal design will
draw from the Phase-II high granularity upgrade of the Compact muon solenoid (CMS)
forward ECal.
This thesis have investigated the possibility of using artificial neural networks (ANNs) to
improve the energy resolution of the ECal. This was performed on simulation data based
on the LDMX framework. Both convolutional neural networks (CNNs) and dense neural
networks (DNNs) were trained on the data and compared with a linear fit between ECal
readout energy and the original electron energy.
The analysis have shown that CNNs can improve the energy resolution of the ECal compared to both the DNN and linear fit who perform similarly. Some inconsistencies in how
the models performed on different energies was discovered. Finally, solutions to this and
suggestions for future work is discussed.}},
  author       = {{Magdalinski, Daniel}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Energy reconstruction with artificial neural networks on LDMX simulations}},
  year         = {{2020}},
}