arXiv subjects

Jyotirmoy Dey

Publications and source records attributed to Jyotirmoy Dey.

8 recordsLinked to original sources

arXiv2026

The dynamical environment of the high-mass star-forming region G28.288$-$0.364

Massive stars form within deeply embedded dust cores, and the development of hypercompact and ultracompact H II regions characterizes their early evolution. Identifying and analyzing such regions is essential for understanding the physical processes that govern massive star formation and the transitions between early evolutionary stages. In this article, we investigate the physical and kinematic properties of the massive star-forming region G28.288$-$0.364 to constrain the evolutionary stages of embedded H II regions and the surrounding cores. We analyze multiwavelength observations, including uGMRT radio continuum data; archival continuum and radio recombination line data from the GLOSTAR-D survey; high-angular-resolution ALMA Band 3 radio recombination line and 1.36-mm dust continuum data from the ALMAGAL survey; and complementary molecular line tracers. We derive spectral indices, measure linewidths and velocities of the radio recombination line emission, identify compact dust cores using dendrogram analysis, and estimate their physical properties. The radio continuum emission exhibits a positive spectral index, consistent with partially optically thick free-free emission. High-resolution observations resolve the ionized gas into two distinct components with physical sizes of $\sim$ 0.06 pc, and their RRL linewidths ($\sim$ 37 and 32 km s$^{-1}$, respectively) indicate that one component is in a transitional stage between hypercompact and ultracompact H II regions, while the other is more evolved. The 1.36-mm dust continuum data reveal five dust cores with surface densities consistent with the theoretical threshold for massive star formation. In summary, these results highlight the complex and sequential nature of massive star formation in clustered environments and demonstrate the importance of high angular resolution observations for resolving the early evolution of massive stars.

astro-ph.GA↗

arXiv2026

The Impact and Environment of Massive Stars and Stellar Clusters

Massive stars and stellar clusters shape galactic evolution through powerful feedback mechanisms including radiation pressure, photoionization, stellar winds, and cosmic ray acceleration. However, their impact remains poorly understood due to observational challenges: they are rare, distant on average, and deeply embedded within dense, dusty environments. Radio observations provide a unique window into these processes, as radio emission penetrates obscuring material and traces both thermal free-free emission from ionized gas and non-thermal synchrotron emission from shocks and particle acceleration. The Square Kilometre Array (SKA) will revolutionize massive star studies through unprecedented sensitivity and angular resolution. SKA observations will enable detailed characterization of hierarchical structures within HII regions, measurements of physical conditions through hydrogen, helium, and carbon radio recombination lines (RRLs), and detection of non-thermal emission from cosmic ray acceleration in star-forming regions. SKA will permit systematic measurements of stellar wind mass-loss rates, studies of photoionized gas kinematics and dynamics, and exploration of photodissociation regions surrounding ultracompact HII regions. Additionally, magnetic field strengths can be probed through Zeeman effect observations of RRLs. This chapter discusses the current understanding of massive stars and stellar clusters and their feedback processes. We highlight how SKA observations will advance our knowledge of massive star formation, stellar winds, hierarchical structures in HII regions, cosmic ray acceleration, and magnetic field regulation of star formation - providing crucial insights into feedback mechanisms governing the structure and evolution of the Milky Way and galaxies.

astro-ph.GA↗

arXiv2024

A multi-wavelength study of Galactic H II regions with extended emission

H II regions are the signposts of massive ($M\geq\,8\,M_\odot$) star-forming sites in our Galaxy. It has been observed that the ionizing photon rate inferred from the radio continuum emission of H II regions is significantly lower ($\sim$ 90%) than that inferred from far-infrared fluxes measured by IRAS. This discrepancy in the ionizing photon rates may arise due to there being significant amounts of dust within the H II regions or the presence of extended emission that is undetected by high-resolution radio interferometric observations. Here, we study a sample of eight compact and ultracompact H II regions with extended emission to explore its role in resolving the discrepancy. We have used observations at the uGMRT (1.25-1.45 GHz) and data from the GLOSTAR survey (4-8 GHz) to estimate the ionizing photon rate from the radio continuum emission. We have also estimated the ionizing photon rate from the infrared luminosity by fitting a spectral energy distribution function to the infrared data from the GLIMPSE, MIPSGAL, and Hi-GAL surveys. The excellent sensitivity of the radio observations to extended emission allows us to investigate the actual fraction of ionizing photons that are absorbed by dust in compact and ultracompact H II regions. Barring one source, we find a direct association between the radio continuum emission from the compact and diffuse components of the H II region. Our study shows that the ionizing photon rates estimated using the radio and infrared data are within reasonable agreement (5-28%) if we include the extended emission. We also find multiple candidate ionizing stars in all our sources, and the ionizing photon rates from the radio observations and candidate stars are in reasonable agreement.

astro-ph.GA↗

arXiv2021

Gas dynamics in the star forming region G18.148$-$0.283: Is it a manifestation of two colliding molecular clouds?

We report the results obtained from a multi-wavelength study of the HII region, G18.148$-$0.283, using the upgraded Giant Metre-wave Radio Telescope (uGMRT) at 1350 MHz along with other archival data. In addition to the radio continuum emission, we have detected the H169$α$ and H170$α$ radio recombination lines towards G18.148$-$0.283 using a correlator bandwidth of 100 MHz. The moment-1 map of the ionized gas reveals a velocity gradient of approximately 10 km s$^{-1}$ across the radio continuum peaks. The $^{12}$CO ($J$=3$-$2) molecular line data from the COHRS survey also shows the presence of two velocity components that are very close to the velocities detected in the ionized gas. The spectrum and position-velocity diagram from CO emission reveal molecular gas at an intermediate velocity range bridging the velocity components. We see mid-infrared absorption and far-infrared emission establishing the presence of a filamentary infrared dark cloud, the extent of which includes the targeted HII region. The magnetic field inferred from dust polarization is perpendicular to the filament within the HII region. We have also identified two O9 stars and 30 young stellar objects towards the target using data from the 2MASS, UKIDSS, and GLIMPSE surveys. Cumulatively, this suggests that the region is the site of a cloud-cloud collision that has triggered massive star formation and subsequent formation of an HII region.

astro-ph.GA↗

arXiv2021

Assessing contribution of treatment phases through tipping point analyses via counterfactual elicitation using rank preserving structural failure time models

This article provides a novel approach to assess the importance of specific treatment phases within a treatment regimen through tipping point analyses (TPA) of a time-to-event endpoint using rank-preserving-structural-failure-time (RPSFT) modelling. In oncology clinical research, an experimental treatment is often added to the standard of care therapy in multiple treatment phases to improve patient outcomes. When the resulting new regimen provides a meaningful benefit over standard of care, gaining insights into the contribution of each treatment phase becomes important to properly guide clinical practice. New statistical approaches are needed since traditional methods are inadequate in answering such questions. RPSFT modelling is an approach for causal inference, typically used to adjust for treatment switching in randomized clinical trials with time-to-event endpoints. A tipping-point analysis is commonly used in situations where a statistically significant treatment effect is suspected to be an artifact of missing or unobserved data rather than a real treatment difference. The methodology proposed in this article is an amalgamation of these two ideas to investigate the contribution of a specific component of a regimen comprising multiple treatment phases. We provide different variants of the method and construct indices of contribution of a treatment phase to the overall benefit of a regimen that facilitates interpretation of results. The proposed approaches are illustrated with findings from a recently concluded, real-life phase 3 cancer clinical trial. We conclude with several considerations and recommendations for practical implementation of this new methodology.

stat.ME↗

arXiv2020

Angular power spectrum of supernova remnants: effects of structure, geometry and diffuse foreground

The study of the intensity fluctuation power spectrum of individual supernova remnants (SNRs) can reveal the structures present at sub-pc scales, and also constrain the physical process that generates those structures. There are various effects, such as the remnant shell thickness, projection of a three-dimensional structure onto a two-dimensional observational plane, and the presence of diffuse "foreground" emission, which causes the observed power spectrum to deviate from the intrinsic power spectrum of the fluctuations. Here, we report results from a systematic study of these effects, using direct numerical simulations, in the measured power spectrum. For an input power-law power spectrum, independent of the power-law index, we see a break in the observed power law at a scale which depends on the shell thickness of a shell-type SNR, and the three-dimensional turbulence changes to two-dimensional turbulence beyond that scale. We also report how the estimated power spectrum is expected to deviate from the intrinsic SNR power spectrum in the presence of additional diffuse Galactic synchrotron emission (DGSE) around the remnant shell. For a reasonable choice of the parameters, if the intrinsic SNR power spectrum is shallower than the DGSE power spectrum, the SNR contribution dominates at small angular scales of the estimated power spectra. On the other hand, if the SNR power spectrum is relatively steeper, the original power spectra is recovered only over a small window of angular scales. This study shows how detailed modeling may be used to infer the true power spectrum from the observed SNR intensity fluctuations power spectrum, which in turn can be used to constrain the nature of the turbulence that gives rise to these small scale structures.

astro-ph.GA↗

arXiv2020

Assessing contribution of treatment phases through tipping point analyses using rank preserving structural failure time models

In clinical trials, an experimental treatment is sometimes added on to a standard of care or control therapy in multiple treatment phases (e.g., concomitant and maintenance phases) to improve patient outcomes. When the new regimen provides meaningful benefit over the control therapy in such cases, it proves difficult to separately assess the contribution of each phase to the overall effect observed. This article provides an approach for assessing the importance of a specific treatment phase in such a situation through tipping point analyses of a time-to-event endpoint using rank-preserving-structural-failure-time (RPSFT) modeling. A tipping-point analysis is commonly used in situations where it is suspected that a statistically significant difference between treatment arms could be a result of missing or unobserved data instead of a real treatment effect. Rank-preserving-structural-failure-time modeling is an approach for causal inference that is typically used to adjust for treatment switching in clinical trials with time to event endpoints. The methodology proposed in this article is an amalgamation of these two ideas to investigate the contribution of a treatment phase of interest to the effect of a regimen comprising multiple treatment phases. We provide two different variants of the method corresponding to two different effects of interest. We provide two different tipping point thresholds depending on inferential goals. The proposed approaches are motivated and illustrated with data from a recently concluded, real-life phase 3 cancer clinical trial. We then conclude with several considerations and recommendations.

stat.ME↗

arXiv2020

Statistical Issues and Recommendations for Clinical Trials Conducted During the COVID-19 Pandemic

The COVID-19 pandemic has had and continues to have major impacts on planned and ongoing clinical trials. Its effects on trial data create multiple potential statistical issues. The scale of impact is unprecedented, but when viewed individually, many of the issues are well defined and feasible to address. A number of strategies and recommendations are put forward to assess and address issues related to estimands, missing data, validity and modifications of statistical analysis methods, need for additional analyses, ability to meet objectives and overall trial interpretability.

q-bio.OT↗