arXiv subjects

Sampriti Roy

Publications and source records attributed to Sampriti Roy.

3 recordsLinked to original sources

arXiv2026

Finite Temperature NLO Corrections in Relativistic Scatterings: Implications for Dark Matter Freeze-In

We study the next-to-leading order (NLO) virtual and thermal corrections to relativistic $2 \rightarrow 2$ scattering processes involving scalar particles in the early Universe thermal plasma. Taking the example of freeze-in production of scalar dark matter pairs through these scatterings, we evaluate the impact of the NLO corrections to the annihilation rate and the dark matter yield. We find that including only thermal mass corrections to a leading order interaction rate can overestimate the reduction in these rates, and the full NLO corrections can modify the DM abundance predictions by $\mathcal{O}(30\%)$. It is also observed that while the virtual NLO effects are larger, the finite temperature NLO corrections to the matrix elements in the relativistic regime can modify the DM abundance by $\mathcal{O}(10\%)$, in comparison to the virtual NLO corrections.

hep-ph↗

arXiv2025

Distribution Testing Meets Sum Estimation

We study the problem of estimating the sum of $n$ elements, each with weight $w(i)$, in a structured universe. Our goal is to estimate $W = \sum_{i=1}^n w(i)$ within a $(1 \pm \epsilon)$ factor using a sublinear number of samples, assuming weights are non-increasing, i.e., $w(1) \geq w(2) \geq \dots \geq w(n)$. The sum estimation problem is well-studied under different access models to the universe $U$. However, to the best of our knowledge, nothing is known about the sum estimation problem using non-adaptive conditional sampling. In this work, we explore the sum estimation problem using non-adaptive conditional weighted and non-adaptive conditional uniform samples, assuming that the underlying distribution ($D(i)=w(i)/W$) is monotone. We also extend our approach to to the case where the underlying distribution of $U$ is unimodal. Additionally, we consider support size estimation when $w(i) = 0$ or $w(i) \geq W/n$, using hybrid sampling (both weighted and uniform) to access $U$. We propose an algorithm to estimate $W$ under the non-increasing weight assumption, using $O(\frac{1}{\epsilon^3} \log{n} + \frac{1}{\epsilon^6})$ non-adaptive weighted conditional samples and $O(\frac{1}{\epsilon^3} \log{n})$ uniform conditional samples. Our algorithm matches the $\Omega(\log{n})$ lower bound by \cite{ACK15}. For unimodal distributions, the sample complexity remains similar, with an additional $O(\log{n})$ evaluation queries to locate the minimum weighted point in the domain. For estimating the support size $k$ of $U$, where weights are either $0$ or at least $W/n$, our algorithm uses $O\big( \frac{\log^3(n/\epsilon)}{\epsilon^8} \cdot \log^4 \frac{\log(n/\epsilon)}{\epsilon} \big)$ uniform samples and $O\big( \frac{\log(n/\epsilon)}{\epsilon^2} \cdot \log \frac{\log(n/\epsilon)}{\epsilon} \big)$ weighted samples to output $\hat{k}$ satisfying $k - 2\epsilon n \leq \hat{k} \leq k + \epsilon n$.

cs.DS↗

arXiv2023

Testing properties of distributions in the streaming model

We study distribution testing in the standard access model and the conditional access model when the memory available to the testing algorithm is bounded. In both scenarios, the samples appear in an online fashion and the goal is to test the properties of distribution using an optimal number of samples subject to a memory constraint on how many samples can be stored at a given time. First, we provide a trade-off between the sample complexity and the space complexity for testing identity when the samples are drawn according to the conditional access oracle. We then show that we can learn a succinct representation of a monotone distribution efficiently with a memory constraint on the number of samples that are stored that is almost optimal. We also show that the algorithm for monotone distributions can be extended to a larger class of decomposable distributions.

cs.DS↗