arXiv subjects

Pooja Kumari

Publications and source records attributed to Pooja Kumari.

2 recordsLinked to original sources

arXiv2026

Comparing critical regions in Polyakov-quark-meson models: Effects of flavor (two versus 2+1) under on-shell renormalization, and on-shell versus curvature-mass parameter fixing for the two-flavor case

We compute contours of the enhanced quark number susceptibility ratio $R_q$, normalized by the free quark gas susceptibility, around the critical end point (CEP) using the on-shell renormalized two-flavor quark-meson (RQM) model and its two Polyakov-loop-extended variants: the Log RPQM and PolyLog-glue RPQM models, employing logarithmic and PolyLog-glue forms of the Polyakov-loop potential without and with quark back-reaction, respectively. These contours are compared with those from the curvature-mass parametrized quark-meson-with-vacuum-term (QMVT) model and its logarithmic Polyakov-loop extension (Log PQMVT) [117], to assess how different treatments of quark one-loop vacuum fluctuations affect the critical region. Although the critical fluctuation regions near the RQM/Log RPQM CEP are reduced in $T$-$μ$ extent relative to the QMVT/Log PQMVT case, the higher-$T$, lower-$μ$ CEP location shifts its enhanced-susceptibility domain toward higher $T$ and lower $μ$. Since the $σ$ and $π$ curvature and pole masses differ in the RQM/Log RPQM model but coincide in QMVT/Log PQMVT, we also compare contours of the enhanced scalar susceptibility ($\sim 1/m_σ^2$) around the respective CEPs. To isolate the roles of strange flavor and the $U_A(1)$ anomaly, we compare $R_q = 2, 3, 5$ contours from our two-flavor RQM/Log RPQM/PolyLog-glue RPQM model calculations with the corresponding 2+1 flavor RQM/RPQM model very recent results in Ref [146].

hep-ph↗

arXiv2025

Solving Scene Understanding for Autonomous Navigation in Unstructured Environments

Autonomous vehicles are the next revolution in the automobile industry and they are expected to revolutionize the future of transportation. Understanding the scenario in which the autonomous vehicle will operate is critical for its competent functioning. Deep Learning has played a massive role in the progress that has been made till date. Semantic Segmentation, the process of annotating every pixel of an image with an object class, is one crucial part of this scene comprehension using Deep Learning. It is especially useful in Autonomous Driving Research as it requires comprehension of drivable and non-drivable areas, roadside objects and the like. In this paper semantic segmentation has been performed on the Indian Driving Dataset which has been recently compiled on the urban and rural roads of Bengaluru and Hyderabad. This dataset is more challenging compared to other datasets like Cityscapes, since it is based on unstructured driving environments. It has a four level hierarchy and in this paper segmentation has been performed on the first level. Five different models have been trained and their performance has been compared using the Mean Intersection over Union. These are UNET, UNET+RESNET50, DeepLabsV3, PSPNet and SegNet. The highest MIOU of 0.6496 has been achieved. The paper discusses the dataset, exploratory data analysis, preparation, implementation of the five models and studies the performance and compares the results achieved in the process.

cs.CV↗