activity
20202026
most citedXRISM view of a stellar flare: High-resolution Fe K spectra of HR 1099, an RS CVn-type star

3 citations · 5 across the 7 of their papers we have counts for

collaborators

7 papers

astro-ph.IM2026

Lynx2030 Science Analysis Group: Final Report

Lynx2030 Science Analysis Group, Simon R. Bandler, Laura W. Brenneman +51

The Lynx2030 Science Analysis Group (SAG) was convened to reassess the scientific goals and technical drivers of the Lynx mission concept amid a rapidly evolving astrophysics lands…

stat.ME2026

A comparison of methods for Poisson regression in the presence of background

Massimiliano Bonamente, Vinay Kashyap, Xiaoli Li +1

This paper provides a statistical analysis of three common methods of regression for Poisson data in the presence of Poisson background, namely the joint fit with two parametric mo…

astro-ph.SR2025

Detecting stellar flares in the presence of a deterministic trend and stochastic volatility

Qiyuan Wang, Giovanni Motta, Genaro Sucarrat +1

We develop a new and powerful method to analyze time series to rigorously detect flares in the presence of an irregularly oscillatory baseline, and apply it to stellar light curves…

astro-ph.SR20253 cited

XRISM view of a stellar flare: High-resolution Fe K spectra of HR 1099, an RS CVn-type star

Miki Kurihara, Masahiro Tsujimoto, Michael Loewenstein +21

A high-resolution X-ray spectroscopic observation was made of the RS CVn-type binary star HR 1099 using the Resolve instrument onboard XRISM for its calibration purposes. During th…

astro-ph.IM2025

A simple, flexible method for timing cross-calibration of space missions

Matteo Bachetti, Yukikatsu Terada, Megumi Shidatsu +25

The timing (cross-)calibration of astronomical instruments is often done by comparing pulsar times-of-arrival (TOAs) to a reference timing model. In high-energy astronomy, the choi…

astro-ph.HE2024

Representation Learning for Time-Domain High-Energy Astrophysics: Discovery of Extragalactic Fast X-ray Transient XRT 200515

Steven Dillmann, Juan Rafael Martínez-Galarza, Roberto Soria +2

We present a novel representation learning method for downstream tasks like anomaly detection, unsupervised classification, and similarity searches in high-energy data sets. This e…