arXiv subjects

Prakruth Adari

Publications and source records attributed to Prakruth Adari.

8 recordsLinked to original sources

arXiv2026

Accurate Galaxy Cluster Shear and Mass Calibration for LSST with AnaCal

The observed abundance of galaxy clusters as a function of mass and redshift provides a powerful route to precision cosmology; a key challenge for cluster cosmology is to establish the relation between cluster observables and cluster masses, for which cluster weak gravitational lensing has become the standard tool. A key challenge for cluster lensing is that the shear signal near cluster centers can reach the non-linear regime, where many shear estimators rely on perturbative assumptions that must be explicitly validated. In this work, we use image simulations to test the performance of the shear estimator AnaCal for cluster weak lensing under conditions representative of the 10-year LSST data. We find that AnaCal recovers the input shear with minimal bias even at mildly high shear, $|g|\sim 0.15$. We discover a radially decreasing mean shear response as seen previously in data, driven by the radial dependence of the convergence field; if unmodeled, this effect can bias shear inference. We also find a positive shear-estimation bias at third order in the reduced shear near the cluster center. However, because only a small fraction of galaxies lie in the high-shear regime and those measurements are further downweighted by the covariance matrix, the resulting mean cluster-mass bias for cluster lens masses in $[10^{14} M_\odot, 10^{15} M_\odot]$ -- adopting a scale cut of $\sim 0.2$ Mpc at $z=0.25$ -- is $0.24 \pm 0.26\%$ under ideal settings. These results demonstrate that AnaCal is a robust tool for accurate cluster mass calibration in the LSST era.

astro-ph.CO↗

arXiv2026

Catalog-based detection of unrecognized blends in deep optical ground based imaging

In deep, ground-based imaging, about 15%-30% of object detections are expected to correspond to two or more true objects - these are called ``unrecognized blends''. We use Machine Learning algorithms to detect unrecognized blends in deep ground-based photometry using only catalog-level information: colors, magnitude, and size. We compare the performance of Self Organizing Map, Random Forest, k-Nearest Neighbors, and Anomaly Detection algorithms. We test all algorithms on 9-band ($uBVri^{+}z^{++}YJH$) and 1-size (flux_radius in $\textit{i}$-band) measurements of the ground-based COSMOS catalog, and use COSMOS HST data as the truth for unrecognized blend. We find that 17% of objects in the ground-based COSMOS catalog are unrecognized blends. We show that some unrecognized blends can be identified as such using only catalog-level information; but not all blends can be easily identified. Nonetheless, our methods can be used to improve sample purity, and can identify approximately 30% to 80% of unrecognized blends while rejecting 10% to 50% of all detected galaxies (blended or unblended). The results are similar when only optical bands ($uBVri^{+}z^{++}$) and the size information is available. We also investigate the ability of these algorithms to remove photo-z outliers (identified with spectroscopic redshifts), and find that algorithms targeting color outliers perform better than algorithms targeting unrecognized blends. Our method can offer a cleaner galaxy sample with lower blending rates for future cosmological surveys such as the Legacy Survey of Space and Time (LSST), and can potentially improve the accuracy on cosmological parameter constraints at a moderate cost of precision.

astro-ph.CO↗

arXiv2026

The Vera C. Rubin Observatory Data Preview 1

We present Rubin Data Preview 1 DP1, the first data from the NSF DOE Vera C Rubin Observatory, comprising raw and calibrated single epoch images, coadds, difference images, detection catalogs, and ancillary data products. DP1 is based on 1792 optical near infrared exposures acquired over 48 distinct nights by the Rubin Commissioning Camera LSSTComCam on the Simonyi Survey Telescope at the Summit Facility on Cerro Pachón Chile in late 2024. DP1 covers $\sim$15 deg$^2$ distributed across seven roughly equal-sized non-contiguous fields, each independently observed in six broad photometric bands $ugrizy$. The median FWHM of the point spread function across all bands is approximately 1.14 arcseconds, with the sharpest images reaching about 0.58 arcseconds. The 5$σ$ point source depths for coadded images in the deepest field the Extended Chandra Deep Field South are $u$ = 24.55, $g$ = 26.18, $r$ = 25.96, $i$ = 25.71, $z$ = 25.07, $y$ = 23.1. Other fields are no more than 2.2 magnitudes shallower in any band where they have nonzero coverage. DP1 contains approximately 2.3 million distinct astrophysical objects, of which 1.6 million are extended in at least one band in coadds and 431 solar system objects of which 93 are new discoveries. DP1 is approximately 3.5 TB in size and is available to Rubin data rights holders via the Rubin Science Platform a cloud based environment for the analysis of petascale astronomical data. While small compared to future LSST releases its high quality and diversity of data support a broad range of early science investigations ahead of full operations in 2026.

astro-ph.IM↗

arXiv2025

The Blending ToolKit: A simulation framework for evaluation of galaxy detection and deblending

We present an open source Python library for simulating overlapping (i.e., blended) images of galaxies and performing self-consistent comparisons of detection and deblending algorithms based on a suite of metrics. The package, named Blending Toolkit (BTK), serves as a modular, flexible, easy-to-install, and simple-to-use interface for exploring and analyzing systematic effects related to blended galaxies in cosmological surveys such as the Vera Rubin Observatory Legacy Survey of Space and Time (LSST). BTK has three main components: (1) a set of modules that perform fast image simulations of blended galaxies, using the open source image simulation package GalSim; (2) a module that standardizes the inputs and outputs of existing deblending algorithms; (3) a library of deblending metrics commonly defined in the galaxy deblending literature. In combination, these modules allow researchers to explore the impacts of galaxy blending in cosmological surveys. Additionally, BTK provides researchers who are developing a new deblending algorithm a framework to evaluate algorithm performance and make principled comparisons with existing deblenders. BTK includes a suite of tutorials and comprehensive documentation. The source code is publicly available on GitHub at https://github.com/LSSTDESC/BlendingToolKit.

astro-ph.IM↗

arXiv2025

SENSEI: First Direct-Detection Results on sub-GeV Dark Matter from SENSEI at SNOLAB

We present the first results from a dark matter search using six Skipper-CCDs in the SENSEI detector operating at SNOLAB. We employ a bias-mitigation technique of hiding approximately 46% of our total data and aggressively mask images to remove backgrounds. Given a total exposure after masking of 100.72 gram-days from well-performing sensors, we observe 55 two-electron events, 4 three-electron events, and no events containing 4 to 10 electrons. The two-electron events are consistent with pileup from one-electron events. Among the 4 three-electron events, 2 appear in pixels that are likely impacted by detector defects, although not strongly enough to trigger our "hot-pixel" mask. We use these data to set world-leading constraints on sub-GeV dark matter interacting with electrons and nuclei.

astro-ph.CO↗

arXiv2024

Searching for Parity Violation in SDSS DR16 Lyman-α Forest Data

The four-point correlation function is the lowest order correlation function for scalar fields that can be used to probe statistical parity invariance in an isotropic universe. There are intriguing claims of detection of parity violation in the 4-point function of BOSS galaxy clustering data. We apply the same estimator to the public SDSS Data Release 16 Lyman-$α$ forest data. Lyman-$α$ forest data probes a different redshift range and is sensitive to a different density regime using a completely different technique. A detection would therefore be a strong indication of new physics. We identify accurate covariance matrix as a crucial impediment to performing this measurement accurately, consistent with existing literature on galaxy 4-point function. We discuss several approaches to estimating the covariance matrix, several of which produce spurious detection. Using a robust, but very suboptimal, covariance matrix derived from subsample bootstrapping, we find no evidence for parity violation.

astro-ph.CO↗

arXiv2022

Charging up Boosted Black Holes

Contrary to a prevailing assumption that black holes would swiftly discharge, we argue that black holes can charge preferentially when boosted through an ambient magnetic field. Though the details are very different, the preference for charge is related to the precipitation of the Wald charge on a spinning black hole in an ambient magnetic field. The gravito-electrodynamics upstage naive arguments about screening electric fields in determining the value of the charge accrued. Charged test particles, which build up the black hole charge, exhibit chaotic behavior as evidenced by fractal basin boundaries between dynamical regions. Charged, boosted black holes will generate their own electromagnetic fields and thereby their own luminous signatures, even if they are initially bare. We therefore add boosted black holes to the growing list of potentially observable black hole signatures, alongside black hole batteries and black hole pulsars. The implications should be relevant for supermassive black holes that are boosted relative to a galactic magnetic field as well as black holes merging with magnetized neutron stars.

gr-qc↗

arXiv2022

Generalized Redundant Calibration of Radio Interferometers

Redundant calibration is a technique in radio astronomy that allows calibration of radio arrays whose antennas lie on a lattice by exploiting the fact that redundant baselines should see the same sky signal. Because the number of measured visibilities scales quadratically with the number of antennas but the number of unknowns describing the individual antenna responses and the available information about the sky scales only linearly with the array size, the problem is always over-constrained as long as the array is big and dense enough. This is true even for non-lattice array configurations. In this work we study a generalized algorithm in which a per-antenna gain is replaced with a number of gains. We show that it can successfully fit data from an approximately redundant array on square lattice with pointing and geometry errors, but that the models parameters are difficult to link to the quantities of interest. We discuss the parameterization, limitations, and possible extensions of this algorithm.

astro-ph.IM↗