arXiv subjects

Kushal Lodha

Publications and source records attributed to Kushal Lodha.

3 recordsLinked to original sources

arXiv2026

Vacuum Phase Transition and Decay to Cross the Phantom Divide

We show that the vacuum metamorphosis model with decay fits current baryon acoustic oscillation and supernova distance data and cosmic microwave background measurements significantly better than $Λ$CDM and nearly as well as $w_0w_a$CDM with the same number of parameters. Vacuum metamorphosis with decay provides a physically motivated model of dark energy and an explanation of current cosmological data. In this scenario, non-perturbative quantum effects of a massive scalar field freeze the Ricci scalar curvature when it reaches a critical value proportional to the mass squared, inducing a phase transition in the scalar field, giving a phantom dark energy equation of state $w<-1$. After the phase transition, vacuum energy decays into light particles, providing a natural mechanism for the dark-energy equation of state to cross the phantom divide from $w<-1$ to $w>-1$, in agreement with current cosmological indications, without requiring an explicit coupling to matter.

astro-ph.CO↗

arXiv2024

Detecting unresolved lensed SNe Ia in LSST using blended light curves

Strongly gravitationally lensed supernovae (LSNe) are promising probes for providing absolute distance measurements using gravitational-lens time delays. Spatially unresolved LSNe offer an opportunity to enhance the sample size for precision cosmology. We predict that there will be approximately three times as many unresolved as resolved LSNe Ia in the Legacy Survey of Space and Time (LSST) by the {\it Rubin} Observatory. In this article, we explore the feasibility of detecting unresolved LSNe Ia from a pool of preclassified SNe Ia light curves using the shape of the blended light curves with deep-learning techniques. We find that $\sim 30\%$ unresolved LSNe Ia can be detected with a simple 1D convolutional neural network (CNN) using well-sampled $rizy$-band light curves (with a false-positive rate of $\sim 3\%$). Even when the light curve is well observed in only a single band among $r$, $i$, and $z$, detection is still possible with false-positive rates ranging from $\sim 4$ to $7\%$ depending on the band. Furthermore, we demonstrate that these unresolved cases can be detected at an early stage using light curves up to $\sim20$ days from the first observation with well-controlled false-positive rates, providing ample opportunity to trigger follow-up observations. Additionally, we demonstrate the feasibility of time-delay estimations using solely LSST-like data of unresolved light curves, particularly for doubles, when excluding systems with low time delays and magnification ratios. However, the abundance of such systems among those unresolved in LSST poses a significant challenge. This approach holds potential utility for upcoming wide-field surveys, and overall results could significantly improve with enhanced cadence and depth in the future surveys.

astro-ph.IM↗

arXiv2024

Searching for local features in primordial power spectrum using genetic algorithms

We present a novel methodology for exploring local features directly in the primordial power spectrum using a genetic algorithm (GA) pipeline coupled with a Boltzmann solver and Cosmic Microwave Background data (CMB). After testing the robustness of our pipeline using mock data, we apply it to the latest CMB data, including Planck 2018 and CamSpec PR4. Our model-independent approach provides an analytical reconstruction of the power spectra that best fits the data, with the unsupervised machine learning algorithm exploring a functional space built off simple ``grammar'' functions. We find significant improvements upon the simple power-law behaviour, by $Δχ^2 \lesssim -21$, consistently with more traditional model-based approaches. These best-fits always address both the low$\ell$ anomaly in the TT spectrum and the residual high$\ell$ oscillations in the TT, TE and EE spectra. The proposed pipeline provides an adaptable tool for exploring features in the primordial power spectrum in a model-independent way, providing valuable hints to theorists for constructing viable inflationary models that are consistent with the current and upcoming CMB surveys.

astro-ph.CO↗