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Researcher profile

Manan Agarwal

Manan Agarwal contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
astro-ph.GAastro-ph.SRastro-ph.HEastro-ph.IMMachine LearningRobotics

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Published work

6 published item(s)

preprint2026arXiv

Asymmetric distribution of Fe-peak elements in Cassiopeia A revealed by XRISM

The elemental abundances of the Fe-peak elements (such as Cr, Mn, Fe and Ni) and Ti are important for understanding the environment of explosive nuclear burning for the core-collapse supernovae (CC SNe). In particular, the supernova remnant Cassiopeia A, which is well known for its asymmetric structure, contains three ``Fe-rich blobs,'' and the composition of the Fe-peak elements within these structures could be related to the asymmetry of the supernova explosion. We report a highly asymmetric distribution of the Fe-peak elements in Cassiopeia A as revealed by XRISM observations. We found that the southeastern Fe-rich region has a significant Mn emission above the 4$σ$ confidence level, while the northwestern Fe-rich region has no clear signature. In addition to the significant difference in Mn abundance across these regions, our observations show that the Ti/Fe, Mn/Cr, and Ni/Fe ratios vary from region to region. The observed asymmetric distribution of Fe-peak elements could be produced by (1) the mixing of materials from different burning layers of the supernova, (2) the asymmetric distribution of the electron fraction in the progenitor star and/or (3) the local dependence of the neutrino irradiation in the supernova innermost region. Future spatially resolved spectroscopy of Cassiopeia A using X-ray microcalorimeters will enable more detailed measurements of the distribution and composition of these elements, providing a unique tool for testing asymmetric supernova physics.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies

Generative control policies (GCPs), such as diffusion- and flow-based control policies, have emerged as effective parameterizations for robot learning. This work introduces Off-policy Generative Policy Optimization (OGPO), a sample-efficient algorithm for finetuning GCPs that maintains off-policy critic networks to maximize data reuse and propagate policy gradients through the full generative process of the policy via a modified PPO objective, using critics as the terminal reward. OGPO achieves state-of-the-art performance on manipulation tasks spanning multi-task settings, high-precision insertion, and dexterous control. To our knowledge, it is also the only method that can fine-tune poorly-initialized behavior cloning policies to near full task-success with no expert data in the online replay buffer, and does so with few task-specific hyperparameter tuning. Through extensive empirical investigations, we demonstrate the OGPO drastically outperforms methods alternatives on policy steering and learning residual corrections, and identify the key mechanisms behind its performance. We further introduce practical stabilizers, including success-buffer regularization, conservative advantages, $χ^2$ regularization, and Q-variance reduction, to mitigate critic over-exploitation across state- and pixel-based settings. Beyond proposing OGPO, we conduct a systematic empirical study of GCP finetuning, identifying the stabilizing mechanisms and failure modes that govern successful off-policy full-policy improvement.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

UOCS VII. Blue Straggler Populations of Open Cluster NGC 7789 with UVIT/AstroSat

NGC 7789 is a $\sim$1.6 Gyr old, populous open cluster located at $\sim$2000 pc. We characterize the blue straggler stars (BSS) of this cluster using the Ultraviolet (UV) data from the UVIT/AstroSat. We present spectral energy distributions (SED) of 15 BSS candidates constructed using multi-wavelength data ranging from UV to IR wavelengths. In 8 BSS candidates, a single temperature SED is found to be satisfactory. We discover hot companions in 5 BSS candidates. The hot companions with Teff $\sim$11750-15500 K, R $\sim$0.069-0.242 R$_{\odot}$, and L $\sim$0.25-1.55 L$_{\odot}$, are most likely extremely low mass (ELM) white dwarfs (WDs) with masses smaller than $\sim$0.18 M$_{\odot}$, and thereby confirmed post mass transfer systems. We discuss the implication of this finding in the context of BSS formation mechanisms. Two additional BSS show excess in one or more UV filters, and may have a hot companion, however, we are unable to characterize them. We suggest that at least 5 of the 15 BSS candidates (33%) studied in this cluster have formed via the mass transfer mechanism.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

UOCS-VIII. UV Study of the open cluster NGC 2506 using ASTROSAT

We study an intermediate-age open cluster NGC 2506 using the \textit{ASTROSAT}/UVIT data and other archival data. We identified 2175 cluster members using a machine learning-based algorithm, ML--MOC, on Gaia EDR3 data. Among the cluster members detected in UVIT filters, F148W, F154W, and F169M, we detect 9 blue straggler stars (BSS), 3 yellow straggler stars (YSS) and 3 red clump (RC) stars. We construct multi-wavelength spectral energy distributions (SEDs) of these objects to characterize them and to estimate their parameters. We discovered hot companions to 3 BSS, 2 YSS and 3 RC candidates and estimated their properties. The hot companions with estimated temperatures, T$\mathrm{_{eff}}$ $\sim$ 13250--31000 K, are WDs of extremely low-mass ($\sim$ 0.20 M$_\odot$), low-mass ($\sim$ 0.20--0.40 M$_\odot$), normal mass ($\sim$ 0.40--0.60 M$_\odot$), and high-mass ($\sim$ 0.8 M$_\odot$). We suggest that systems with extremely low mass and low mass WDs as companions are formed via Case-A/Case-B mass transfer mechanism. A BSS is the likely progenitor of the high mass WD, as a star with more than the turn-off mass of the cluster is needed to form a high mass WD. Thus, systems with high mass WD are likely to be formed through merger in triple systems. We conclude that mass transfer as well as merger pathways of BSS formation are present in this cluster.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

ML-MOC: Machine Learning (kNN and GMM) based Membership Determination for Open Clusters

The existing open cluster membership determination algorithms are either prior dependent on some known parameters of clusters or are not automatable to large samples of clusters. In this paper, we present, ML-MOC, a new machine learning based approach to identify likely members of open clusters using the Gaia DR2 data, and no a priori information about cluster parameters. We use the k-Nearest Neighbours (kNN) algorithm and the Gaussian Mixture Model (GMM) on the high-precision proper motions and parallax measurements from Gaia DR2 data to determine the membership probabilities of individual sources down to G ~20 mag. To validate the developed method, we apply it on fifteen open clusters: M67, NGC 2099, NGC 2141, NGC 2243, NGC 2539, NGC 6253, NGC 6405, NGC 6791, NGC 7044, NGC 7142, NGC 752, Blanco 1, Berkeley 18, IC 4651, and Hyades. These clusters differ in terms of their ages, distances, metallicities, extinctions and cover a wide parameter space in proper motions and parallaxes with respect to the field population. The extracted members produce clean colour-magnitude diagrams and our astrometric parameters of the clusters are in good agreement with the values derived by the previous works. The estimated degree of contamination in the extracted members range between 2% and 12%. The results show that ML-MOC is a reliable approach to segregate the open cluster members from the field stars.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Blue Straggler Populations of Seven Open Clusters with Gaia DR2

Blue straggler stars (BSS) are well studied in globular clusters but their systematic study with secure membership determination is lacking in open clusters. We use Gaia DR2 data to determine accurate stellar membership for four intermediate-age open clusters, Melotte 66, NGC 2158, NGC 2506 and NGC 6819, and three old open clusters, Berkeley 39, NGC 188 and NGC 6791, to subsequently study their BSS populations. The BSS radial distributions of five clusters, Melotte 66, NGC 188, NGC 2158, NGC 2506, and NGC 6791, show bimodal distributions, placing them with Family II globular clusters which are of intermediate dynamical ages. The location of minima, $r_\mathrm{min}$, in the bimodal BSS radial distributions, varies from 1.5$r_c$ to 4.0$r_c$, where $r_c$ is the core radius of the clusters. We find a positive correlation between $r_\mathrm{min}$ and $N_{\mathrm{relax}}$, the ratio of cluster age to the current central relaxation time of the cluster. We further report that this correlation is consistent in its slope, within the errors, to the slope of the globular cluster correlation between the same quantities, but with a slightly higher intercept. This is the first example in open clusters that shows BSS radial distributions as efficient probes of dynamical age. The BSS radial distributions of the remaining two clusters, Berkeley 39 and NGC 6819, are flat. The estimated $N_{\mathrm{relax}}$ values of these two clusters, however, indicate that they are dynamically evolved. Berkeley 39 especially has its entire BSS population completely segregated to the inner regions of the cluster.

0 citations0 reviewsNo code linkedOpen access