arXiv subjects

Joanna Piotrowska

Publications and source records attributed to Joanna Piotrowska.

6 recordsLinked to original sources

arXiv2026

The Hunt for Red Dual AGNs I: Spatially-Resolved Mid-IR Dual AGNs in the DeCam Legacy Survey

Theoretical studies predict that dual AGNs are a critical stage of galaxy merger-driven supermassive black hole growth. Systematic searches for dual AGNs typically target late-stage mergers ($\leq10$ kpc nuclear separations) and select AGNs based on optical diagnostics. Yet, simulations predict that obscuration can occur early in the merger sequence, and that a significant fraction of dual AGNs can be found beyond $10$ kpc. Here, we report on a new sample of 157 spatially resolved mid-IR dual AGNs candidates selected based upon their mid-IR $W1-W2$ colors from the Wide-Field Infrared Survey Explorer and optically classified as galaxy merger candidates using imaging from the Dark Energy Camera Legacy Survey. Spectroscopic results are presented for approximately 2/3 of the sample. 76 candidates have been confirmed to reside in galaxy mergers; among these, 13 have been confirmed as bona fide mid-IR dual AGNs, while 63 represent strong dual AGN candidates that require further examination. 46 candidates have been rejected as non-merger contaminants (foreground-background AGNs, separations inconsistent with interacting galaxies, etc.). 35 candidates still await spectroscopic coverage. The confirmed and high confidence dual AGN candidates exhibit separations of 14.5-129 kpc; $>50$% reside at separations $>50$ kpc. Confirmed and high confidence candidates also exhibit a diversity of nuclear optical BPT classes. Seyfert-Seyferts and Seyfert-HIIs dominate the overall BPT pairs sample. 31% of confirmed mid-IR dual AGNs reside in multi-mergers involving three or more galaxies. The diversity in AGN properties and environments identified in this work highlights the importance of multiwavelength selection strategies and analyses in the quest to holistically understand dual AGNs as a population.

astro-ph.GA↗

arXiv2026

Understanding the regulation of star formation within TNG100 galaxies on kpc-scales using machine learning I: Global versus local

We apply Random Forest and XGBoost machine learning algorithms to determine which galaxy properties most effectively predict star formation and quenching in simulated galaxies. Using spatially-resolved data from approximately 63,000 annular bins across 6,189 TNG100 galaxies, we train classification models to predict quenching states and regression models to predict star formation rate surface densities. Despite their different algorithmic approaches, both methods produce consistent feature importance rankings, with XGBoost distributing importance more evenly among correlated features. For central galaxies and high-mass satellites, black hole mass dominates quenching predictions, consistent with quenching via active galactic nuclei (AGN) feedback. Classification of low-mass satellites shows overwhelming importance for halo mass, indicating environmental quenching. Star formation predictions are dominated by local stellar mass surface density across all star-forming galaxy types, confirming that active star formation is a local process while quenching is driven by global properties.

astro-ph.GA↗

arXiv2023

Variability as a predictor for the hard-to-soft state transition in GX 339-4

During the outbursts of black hole X-ray binaries (BHXRBs), their accretion flows transition through several states. The source luminosity rises in the hard state, dominated by non-thermal emission, before transitioning to the blackbody-dominated soft state. As the luminosity decreases, the source transitions back into the hard state and fades to quiescence. This picture does not always hold, as $\approx$ 40$\%$ of the outbursts never leave the hard state. Identifying the physics that govern state transitions remains one of the outstanding open questions in black hole astrophysics. In this paper we present an analysis of archival RXTE data of multiple outbursts of GX 339-4. We compare the properties of the X-ray variability and time-averaged energy spectrum and demonstrate that the variability (quantified by the power spectral hue) systematically evolves $\approx$ 10-40 days ahead of the canonical state transition (quantified by a change in spectral hardness); no such evolution is found in hard state only outbursts. This indicates that the X-ray variability can be used to predict if and when the hard-to-soft state transition will occur. Finally, we find a similar behavior in ten outbursts of four additional BHXRBs with more sparse observational coverage. Based on these findings, we suggest that state transitions in BHXRBs might be driven by a change in the turbulence in the outer regions of the disk, leading to a dramatic change in variability. This change is only seen in the spectrum days to weeks later, as the fluctuations propagate inwards towards the corona.

astro-ph.HE↗

arXiv2019

Towards a deeper understanding of the physics driving galaxy quenching -- inferring trends in the gas content via extinction

In order to investigate the importance of different proposed quenching mechanisms, we use an indirect method to estimate gas masses for ~62,000 SDSS DR7 galaxies. We infer gas surface densities from dust column densities as traced by extinction within the fibre, applying a metallicity correction to account for varying dust-to-gas ratios. We find that both gas fraction and star formation efficiency (SFE) decrease moving away from the star forming main sequence (MS) towards quiescence for all galaxy masses. We further show that both quantities correlate similarly strongly with the departure from the MS, implying the need for any physical model of quenching to invoke a change in $\textit{both}$ gas fraction and SFE. Our results call for a better understanding of the physical processes driving the decrease in star formation efficiency, which has received relatively little attention in the theory of quenching until now.

astro-ph.GA↗

arXiv2019

Spectral shock detection for dynamically developing discontinuities

Pseudospectral schemes are a class of numerical methods capable of solving smooth problems with high accuracy thanks to their exponential convergence to the true solution. When applied to discontinuous problems, such as fluid shocks and material interfaces, due to the Gibbs phenomenon, pseudospectral solutions lose their superb convergence and suffer from spurious oscillations across the entire computational domain. Luckily, there exist theoretical remedies for these issues which have been successfully tested in practice for cases of well defined discontinuities. We focus on one piece of this procedure---detecting a discontinuity in spectral data. We show that realistic applications require treatment of discontinuities dynamically developing in time and that it poses challenges associated with shock detection. More precisely, smoothly steepening gradients in the solution spawn spurious oscillations due to insufficient resolution, causing premature shock identification and information loss. We improve existing spectral shock detection techniques to allow us to automatically detect true discontinuities and identify cases for which post-processing is required to suppress spurious oscillations resulting from the loss of resolution. We then apply these techniques to solve an inviscid Burgers' equation in 1D, demonstrating that our method correctly treats genuine shocks caused by wave breaking and removes oscillations caused by numerical constraints.

math.NA↗

arXiv2019

Spectral Methods in the Presence of Discontinuities

Spectral methods provide an elegant and efficient way of numerically solving differential equations of all kinds. For smooth problems, truncation error for spectral methods vanishes exponentially in the infinity norm and $L_2$-norm. However, for non-smooth problems, convergence is significantly worse---the $L_2$-norm of the error for a discontinuous problem will converge at a sub-linear rate and the infinity norm will not converge at all. We explore and improve upon a post-processing technique---optimally convergent mollifiers---to recover exponential convergence from a poorly-converging spectral reconstruction of non-smooth data. This is an important first step towards using these techniques for simulations of realistic systems.

math.NA↗