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Gourab Nandi

Publications and source records attributed to Gourab Nandi.

2 recordsLinked to original sources

Spiral arms across cosmic time: JWST measurements of the pitch angles of spiral galaxies at $z<3.5$

The properties of spiral galaxies in the early universe remain poorly studied and, as such, little is known about their nature and evolution. We use JWST data to measure the pitch angles of spiral galaxies across cosmic time. Our sample consists of 593 spiral galaxies with stellar masses ($M_*$) greater than $10^{10} M_\odot$ up to $z \sim 3.5$, drawn from the CEERS and JADES surveys. Spiral galaxies are identified by fine-tuning a Zoobot deep-learning model. We use SpArcFiRe to identify spiral arms and measure their pitch angles. We find no significant redshift evolution in the average pitch angle across the full sample. However, in the most massive systems (log$(M_*/M_\odot)=11-12$), spiral arms slightly wind up with time. We show that at $z>1.25$, pitch angle does not correlate with some key internal galaxy properties (stellar mass, bulge mass, disk mass, specific star formation rate [sSFR]). In contrast, at $z<1.25$, pitch angle shows a weak but statistically significant negative correlation with stellar mass, bulge mass, and disk mass, and a positive correlation with sSFR at $z<0.75$. We also find no dependence of pitch angle on the tidal strength applied by nearby companions. These results indicate a transition epoch at $z\sim1$: above this redshift, spiral structures appear to be primarily locally driven and not correlated with global galaxy properties; and below this redshift, spiral arms are regulated by global gravitational potential, consistent with the predictions of the density wave theory.

astro-ph.GA

Gaussian Process Reconstruction of Cosmological Parameters with Gravitational Wave Sirens using Machine Learning

Future gravitational wave (GW) standard siren catalogues will probe the late-time expansion history of the Universe across redshift ranges largely inaccessible to traditional electromagnetic observations. To determine how effectively this background distance information can distinguish between viable cosmological models, we introduce a model-independent reconstruction framework utilizing Gaussian Process Regression (GPR). Analyzing mock LISA and Einstein Telescope (ET) catalogues across six fiducial cosmological backgrounds-$\Lambda$CDM, CPL, CPL+$\Lambda$, interacting dark matter, interacting dark energy and axion inspired early dark energy. We reconstruct the comoving distance and its derivatives. Crucially, we propagated the full GP covariance, including derivative cross-covariances, to robustly evaluate the Hubble parameter $H(z)$ and other diagnostics such as $q(z)$, $\mathcal{O}_{m}(z)$ $w_{\rm total}(z)$ and $\kappa(z)$. While our analysis demonstrates that GW bright standard sirens faithfully recover fiducial expansion histories, applying pointwise marginal Hellinger distance reveals that background measurements alone do not provide decisive statistical separation among models. Instead, derivative sensitive diagnostics pinpoint specific redshift windows (e.g., $z\simeq1.6-1.8$ for ET and $z\simeq2.6-2.9$ for LISA) where future catalogues will maximize their discriminatory power. As machine learning methodologies become increasingly integral to astrophysics and cosmology, this Bayesian GPR pipeline offers a principled, nonparametric approach to precisely identifying where the most valuable cosmological information lies.

astro-ph.CO