arXiv subjects

Aniket Jain

Publications and source records attributed to Aniket Jain.

5 recordsLinked to original sources

arXiv2026

The Carousel Lens II: Cosmological Constraints with GIGA-Lens

The nature of dark matter and dark energy are among the central questions in cosmology. Strong gravitational lenses with multiple source planes provide a geometric probe of cosmology: the ratio of deflection angles at different redshifts depends only on angular-diameter distances, constraining the matter density $\Omega_m$ and the dark energy equation of state $w$. However, constraints from this technique have historically lagged behind those from the CMB, SNe Ia, and BAO. In this work, we present new cosmological constraints from the Carousel Lens, a cluster-scale lens with more than 40 extended images from 11 spectroscopically confirmed sources. Its relaxed core and rich set of extended images behind the main halo make it particularly suitable for cosmological inference. Using the GIGA-Lens pipeline, we construct a pixel-level lens model including six HST-detected sources and four mass components. From this model, we obtain $w$CDM constraints of $\Omega_m = 0.34^{+0.16}_{-0.13}$ and $w = -1.31^{+0.35}_{-0.32}$ from the Carousel Lens alone, accounting for both statistical and systematic uncertainties. We further project that including four additional known higher-redshift sources, assuming similar fractional uncertainties, could improve the constraining power by ~80%, bringing the precision close to that of the CMB and SNe Ia. For an evolving dark energy model ($w_0w_a$CDM), the Carousel Lens alone yields constraints comparable to the CMB, providing an independent and complementary probe alongside SN Ia and BAO. While currently systematic uncertainties dominate, which we quantify through simulations, our results demonstrate that relaxed multi-source-plane cluster lenses can deliver competitive cosmological constraints. Further improvements are expected from reductions in systematics and from incorporating higher-redshift sources (known and new) with high-resolution imaging.

astro-ph.CO↗

arXiv2023

Rectangular Hull Confidence Regions for Multivariate Parameters

We introduce three notions of multivariate median bias, namely, rectilinear, Tukey, and orthant median bias. Each of these median biases is zero under a suitable notion of multivariate symmetry. We study the coverage probabilities of rectangular hull of $B$ independent multivariate estimators, with special attention to the number of estimators $B$ needed to ensure a miscoverage of at most $α$. It is proved that for estimators with zero orthant median bias, we need $B\geq c\log_2(d/α)$ for some constant $c > 0$. Finally, we show that there exists an asymptotically valid (non-trivial) confidence region for a multivariate parameter $θ_0$ if and only if there exists a (non-trivial) estimator with an asymptotic orthant median bias of zero.

math.ST↗

arXiv2018

Understanding Fashionability: What drives sales of a style?

We use customer demand data for fashion articles on Myntra, and derive a fashionability or style quotient, which represents customer demand for the stylistic content of a fashion article, decoupled with its commercials (price, offers, etc.). We demonstrate learning for assortment planning in fashion that would aim to keep a healthy mix of breadth and depth across various styles, and we show the relationship between a customer's perception of a style vs a merchandiser's catalogue of styles. We also backtest our method to calculate prediction errors in our style quotient and customer demand, and discuss various implications and findings.

cs.IR↗

arXiv2018

Unravelling Airbnb Predicting Price for New Listing

This paper analyzes Airbnb listings in the city of San Francisco to better understand how different attributes such as bedrooms, location, house type amongst others can be used to accurately predict the price of a new listing that optimal in terms of the host's profitability yet affordable to their guests. This model is intended to be helpful to the internal pricing tools that Airbnb provides to its hosts. Furthermore, additional analysis is performed to ascertain the likelihood of a listings availability for potential guests to consider while making a booking. The analysis begins with exploring and examining the data to make necessary transformations that can be conducive for a better understanding of the problem at large while helping us make hypothesis. Moving further, machine learning models are built that are intuitive to use to validate the hypothesis on pricing and availability and run experiments in that context to arrive at a viable solution. The paper then concludes with a discussion on the business implications, associated risks and future scope.

q-fin.GN↗

arXiv2018

Data-Driven Investigative Journalism For Connectas Dataset

The following paper explores the possibility of using Machine Learning algorithms to detect the cases of corruption and malpractice by governments. The dataset used by the authors contains information about several government contracts in Colombia from year 2007 to 2012. The authors begin with exploring and cleaning the data, followed by which they perform feature engineering before finally implementing Machine Learning models to detect anomalies in the given dataset.

cs.CL↗