Advisors: Dr. Elena
Cuoco (Scuola Normale Superiore and the European Gravitational Observatory (EGO), Italy), Dr. Alberto Iess (Scuola Normale Superiore, Italy)
Core-collapse supernovae (CCSNe) are a type of burst signals that have eluded detection so
far, and a significant effort is therefore being put into achieving a detection in coming
years. Compared to binary mergers, core-collapse supernovae cannot be modelled precisely,
and it is, therefore, impossible to apply classical matched filter techniques for detection
and analysis. In this project, I utilized a wavelet-based detection pipeline, the Wavelet
Detection Filter (WDF), to generate event triggers. I developed a clustering algorithm to
group triggers associated with a single event. Using WDF parameters, I successfully
reconstructed the injected signal with a good accuracy. My Master's thesis can be found here.
Advisor: Prof. Sourabh
Dube (IISER Pune)
I worked on a research project involving the development of a complex artificial neural
network, which incorporated 24 input variables like missing transverse energy and the count
of b-jets. My investigation primarily revolved around utilizing CMS data from 2017 and 2018,
specifically focusing on events characterized by multilepton final states. With the neural
network I created, I was able to categorize these events into three Standard Model
processes: ZZ, WZ, and the ttZ process. By studying the output distribution of the network,
I gained insights into the complex relationships among the input variables.
Advisor: Prof. Sourabh
Dube (IISER Pune)
I was tasked with reproducing work from a previous Master's thesis. Using a toy detector
model, I generated two sets of images pertaining to signal and background. My aim was to
train a convolutional neural network to classify between these two classes. Prior to this, I
designed and trained a binary neural network to distinguish Drell-Yan and ttbar
processes.