arXiv subjects

Neha Singh

Publications and source records attributed to Neha Singh.

22 records · Page 2Linked to original sources

arXiv2021

Detectability of continuous gravitational waves from isolated neutron stars in the Milky Way: the population synthesis approach

Aims. We estimate the number of pulsars, detectable as continuous gravitational wave sources with the current and future gravitational-wave detectors, assuming a simple phenomenological model of evolving non-axisymmetry of the rotating neutron star. Methods. We employ a numerical model of the Galactic neutron star population, with the properties established by comparison with radio observations of isolated Galactic pulsars. We generate an arbitrarily large synthetic population of neutron stars and evolve their period, magnetic field, and position in space. We use a gravitational wave emission model based on exponentially decaying ellipticity - a non-axisymmetry of the star, with no assumption of the origin of a given ellipticity. We calculate the expected signal in a given detector for a 1 year observations and assume a detection criterion of the signal-to-noise ratio of 11.4 - comparable to a targeted continous wave search. We analyze the population detectable separately in each detector: Advanced LIGO, Advanced Virgo, and the planned Einstein Telescope. In the calculation of the expected signal we neglect signals frequency change due to the source spindown and the Earth motion with respect to the Solar barycentre. Results. With conservative values for the neutron stars evolution: supernova rate once per 100 years, initial ellipticity $ε_{0}$ = 1e-5 with no decay of the ellipticity $η$ = $t_\rm{hub}$ = 1e4 Myr, the expected number of detected neutron stars is below one: 0.15 (based on a simulation of 10 M stars) for the Advanced LIGO detector. A broader study of the parameter space ($ε_{0}$ , $η$) is presented. With the planned sensitivity for the Einstein Telescope, and assuming the same ellipiticity model, the expected detection number is: 26.4 pulsars during a 1-year long observing run.

gr-qc↗

arXiv2020

A single-cell RNA expression map of coronavirus receptors and associated factors in developing human embryos

To predict if developing human embryos are permissive to coronaviruses, we analyzed publicly available single cell RNA-seq datasets of zygotes, 4-cell, 8-cell, morula, inner cell mass, epiblast, primitive endoderm and trophectoderm for the coronavirus receptors (ACE2, BSG, DPP4 and ANPEP), the Spike protein cleavage enzymes (TMPRSS2, CTSL). We also analyzed the presence of host genes involved in viral replication, the endosomal sorting complexes required for transport (ESCRT) and SARS-Cov-2 interactions. The results reveal that ACE2, BSG, DPP4 and ANPEP are expressed in the cells of the zygote, to blastocyst including the trophectodermal lineage. ACE2, TMPRSS, BSG and CTSL are co-transcribed in a proportion of epiblast cells and most cells of the trophectoderm. The embryonic and trophectodermal cells also express genes for proteins ESCRT, viral replication and those that interact with SARS-CoV-2. We identified 1985 genes in epiblast and 1452 genes in the trophectoderm that are enriched in the ACE2 and TMPRSS2 co-expressing cells; 216 genes of these are common in both the cell types. These genes are associated with lipid metabolism, lysosome, peroxisome and oxidative phosphorylation pathways. Together our results suggest that developing human embryos could be permissive to coronavirus entry by both canonical and non-canonical mechanisms and they also express the genes for proteins involved in viral endocytosis and replication. This knowledge will be useful for evidence-based patient management for IVF during the COVID-19 pandemic.

q-bio.TO↗

arXiv2020

Asymmetric accretion and thermal `mountains' in magnetized neutron star crusts

Accreting neutron stars are one of the main targets for continuous gravitational wave searches, as asymmetric accretion may lead to quadrupolar deformations, or `mountains', on the crust of the star, which source gravitational wave emission at twice the rotation frequency. The gravitational wave torque may also impact on the spin evolution of the star, possibly dictating the currently observed spin periods of neutron stars in Low Mass X-ray Binaries and leading to the increased spindown rate observed during accretion in PSR J1023+0038. Previous studies have shown that deformed reaction layers in the crust of the neutron star lead to thermal and compositional gradients that can lead to gravitational wave emission. However, there are no realistic constraints on the level of asymmetry that is expected. In this paper we consider a natural source of asymmetry, namely the magnetic field, and calculate the density and pressure perturbations that are expected in the crust of accreting neutron stars. In general we find that only the outermost reaction layers of the neutron star are strongly perturbed. The mass quadrupole that we estimate is generally small and cannot explain the increase of spin-down rate of PSR J1023+0038. However, if strong shallow heating sources are present at low densities in the crust, as cooling observations suggest, these layers will be strongly perturbed and the resulting quadrupole could explain the observed spindown of PSR J1023+0038, and lead to observable gravitational wave signals from systems with higher accretion rates.

astro-ph.HE↗

arXiv2020

Localized Flood DetectionWith Minimal Labeled Social Media Data Using Transfer Learning

Social media generates an enormous amount of data on a daily basis but it is very challenging to effectively utilize the data without annotating or labeling it according to the target application. We investigate the problem of localized flood detection using the social sensing model (Twitter) in order to provide an efficient, reliable and accurate flood text classification model with minimal labeled data. This study is important since it can immensely help in providing the flood-related updates and notifications to the city officials for emergency decision making, rescue operations, and early warnings, etc. We propose to perform the text classification using the inductive transfer learning method i.e pre-trained language model ULMFiT and fine-tune it in order to effectively classify the flood-related feeds in any new location. Finally, we show that using very little new labeled data in the target domain we can successfully build an efficient and high performing model for flood detection and analysis with human-generated facts and observations from Twitter.

cs.CL↗