Saswati Nandan

We present measurements of the reduction of light output by plastic scintillators irradiated in the CMS detector during the 8 TeV run of the Large Hadron Collider and show that they indicate a strong dose rate effect. The damage for a given dose is larger for lower dose rate exposures. The results agree with previous measurements of dose rate effects, but are stronger due to the very low dose rates probed. We show that the scaling with dose rate is consistent with that expected from diffusion effects.

Searches for non-resonant $HH$ production and measurements of the Higgs self-coupling ($\lambda$) in the channels $HH\rightarrow bbWW^*$, $HH\rightarrow WW^*\gamma\gamma$ and $VHH\rightarrow 4b$ have been presented. The analysis is based on data recorded at a center of mass energy of 13 TeV by the CMS detector during LHC Run2. The results are compatible with the Standard Model expectation. Exclusion limits on the $HH$ production cross section and on $\lambda$ have been presented.

Identifying and reconstructing hadronic τ decays (τh) is an important task at current and future high-energy physics experiments, as τh represent an important tool to analyze the production of Higgs and electroweak bosons as well as to search for physics beyond the Standard Model. The identification of τh can be viewed as a generalization and extension of jet-flavour tagging, which has in the recent years undergone significant progress due to the use of deep learning. Based on a granular simulation with realistic detector effects and a particle flow-based event reconstruction, we show in this paper that deep learning-based jet-flavour-tagging algorithms are powerful τh identifiers. Specifically, we show that jet-flavour-tagging algorithms such as LorentzNet and ParticleTransformer can be adapted in an end-to-end fashion for discriminating τh from quark and gluon jets. We find that the end-to-end transformer-based approach significantly outperforms contemporary state-of-the-art τh reconstruction and identification algorithms currently in use at the Large Hadron Collider.

Recent results on Higgs boson self-coupling, $\lambda_{HHH}$, performed in Higgs boson pair, $HH$, production in different final states are presented. The analysis is based on data recorded at a center-of-mass energy of 13 TeV by the CMS detector during LHC Run 2. Results on the non-resonant $HH$ production cross section in different Effective Field Theory scenarios are addressed as well. A projection study for $\lambda_{HHH}$ at the High-Luminosity LHC is also presented.

The results of a search for Higgs boson pair ($HH$) production in the final state $bbWW^*$ have been presented. The analysis is based on data recorded at a center of mass energy of 13 TeV by the CMS detector during LHC Run 2. Both non-resonant and resonant productions have been studied. The results are compatible with the standard model expectations. Exclusion limits on the $HH$ production cross section in both non-resonant and resonant production modes as well as limits in different Effective Field Theory scenarios have been presented.

The peer review process involved in evaluating academic papers submitted to journals and conferences is very perplexing as at times the scores given by the reviewer may be poor in contrast with the textual comments which are in a positive light. In such a case, it becomes difficult for the judging chair to come to a concrete decision regarding the accept or reject decision of the papers. In our paper, we aim to extract the sentiment from the reviewers’ opinions and use it along with the numerical scores to correlate that in order to predict the orientation of the review, i.e., the degree of acceptance. Our proposed methods include Machine learning models like Naive Bayes, Deep learning models involving LSTM and a Hybrid model with BiLSTM, LSTM, CNN, and finally Graph based model GCN. The dataset is taken from the UCI repository consisting of peer reviews in Spanish along with other parameters used for judging a paper. Bernoulli’s Naive Bayes was the model that fared the highest amongst all the approaches, with an accuracy of 75.61% after varying the parameters to enhance the accuracy.

Identifying and reconstructing hadronic $\tau$ decays ($\tau_{\textrm{h}}$) is an important task at current and future high-energy physics experiments, as $\tau_{\textrm{h}}$ represent an important tool to analyze the production of Higgs and electroweak bosons as well as to search for physics beyond the Standard Model. The identification of $\tau_{\textrm{h}}$ can be viewed as a generalization and extension of jet-flavour tagging, which has in the recent years undergone significant progress due to the use of deep learning. Based on a granular simulation with realistic detector effects and a particle flow-based event reconstruction, we show in this paper that deep learning-based jet-flavour-tagging algorithms are powerful $\tau_{\textrm{h}}$ identifiers. Specifically, we show that jet-flavour-tagging algorithms such as LorentzNet and ParticleTransformer can be adapted in an end-to-end fashion for discriminating $\tau_{\textrm{h}}$ from quark and gluon jets. We find that the end-to-end transformer-based approach significantly outperforms contemporary state-of-the-art $\tau_{\textrm{h}}$ reconstruction and identification algorithms currently in use at the Large Hadron Collider.

From the perspective of online education where the short-term connects, as well as the long-term associations are completely virtual, it is critical to develop a sustainable engagement plan for online learners with different demographical profiles and needs. A successful engagement strategy can ensure satisfaction, enhance motivation, reduce the sense of isolation and improve performance of the learners. In the long run it will build an affiliation within the learning community, emotional involvement with the programme and loyalty to the educator and provider. Hence, it is crucial to understand the various types of engagement dimensions and how these dimensions associate or differentiate across the segments. This study comprises of an empirical research, based on a survey of learners with different profile, age group and learning orientation. The research establishes several hypotheses in the context of different engagement types. It interprets how the behavioural, social, affective and cognitive engagement dimensions associate among themselves and if there are significant differences observed in these engagement dimensions considering the different learner segments. The inferences of this research would be valuable to create customized engagement initiatives for diverse categories of learners.

We study the light output, light collection efficiency and signal timing of a variety of organic scintillators that are being considered for the upgrade of the hadronic calorimeter of the CMS detector. The experimental data are collected at the H2 test-beam area at CERN, using a 150 GeV muon beam. In particular, we investigate the usage of over-doped and green-emitting plastic scintillators, two solutions that have not been extensively considered. We present a study of the energy distribution in plastic-scintillator tiles, the hit efficiency as a function of the hit position, and a study of the signal timing for blue and green scintillators.

The Phase I upgrade of the CMS Hadron Endcap Calorimeters consists of new photodetectors (Silicon Photomultipliers in place of Hybrid Photo-Diodes) and front-end electronics. The upgrade will eliminate the noise and the calibration drift of the Hybrid Photo-Diodes and enable the mitigation of the radiation damage of the scintillators and the wavelength shifting fibers with a larger spectral acceptance of the Silicon Photomultipliers. The upgrade also includes increased longitudinal segmentation of the calorimeter readout, which allows pile-up mitigation and recalibration due to depth-dependent radiation damage. As a realistic operational test, the responses of the Hadron Endcap Calorimeter wedges were calibrated with a 60Co radioactive source with upgrade electronics. The test successfully established the procedure for future source calibrations of the Hadron Endcap Calorimeters. Here we describe the instrumentation details and the operational experiences related to the sourcing test.

We have examined low energy BCl<SUB>2</SUB> and BBr<SUB>2</SUB> implants as a means of fabricating ultra-shallow P<SUP>+</SUP>-N junctions. Five keV and 9 keV BCl<SUB>2</SUB> implants and 18 keV BBr<SUB>2</SUB> implants have been compared to 5 keV BF<SUB>2</SUB> implants to study the benefits of using these species. BCl<SUB>2</SUB> and BBr<SUB>2</SUB>, being heavier species, have a lower projected range and produce more damage. The greater damage restricts channeling, resulting in shallower as-implanted profiles. The increased damage amorphizes the substrate at low implant doses which results in reduced transient enhanced diffusion (TED) during the post-implant anneal. Post-anneal SIMS profiles indicate a junction depth reduction of over 10 nm (at 5 X 10<SUP>17</SUP> cm<SUP>-3</SUP> background doping) for 5 keV BCl<SUB>2</SUB> implants as compared to 5 keV BF<SUB>2</SUB> implants. Annealed junctions as shallow as 10 nm have been obtained from the 18 keV BBr<SUB>2</SUB> implants. The increased damage degrades the electrical properties of these junctions by enhancing the leakage current densities. BCl<SUB>2</SUB> implanted junctions have leakage current densities of approximately 1 (mu) A/cm<SUP>2</SUP> as compared to 10 nA/cm<SUP>2</SUP> for the BF<SUB>2</SUB> implants. BBr<SUB>2</SUB> implants have a lower leakage density of approximately 50 nA/cm<SUP>2</SUP>. Low energy BBr<SUB>2</SUB> implants offer an exciting alternative for fabricating low leakage, ultra-shallow P<SUP>+</SUP>-N junctions.