arXiv subjects

Amanda Pagul

Publications and source records attributed to Amanda Pagul.

7 recordsLinked to original sources

arXiv2025

Spatially Resolved Physical Properties of Young Star Clusters and Star-forming Clumps in the Brightest z>6 Galaxy, the Strongly Lensed Cosmic Spear at z=6.2

We present spatially resolved analysis of stellar populations in the brightest $z>6$ galaxy known to date (AB mag 23), the strongly lensed MACS0308$-$zD1 (dubbed the ``Cosmic Spear'') at $z_{\rm spec}=6.2$. New JWST NIRCam imaging and high-resolution NIRSpec IFU spectroscopy span the rest-frame ultraviolet to optical. The NIRCam imaging reveals bright star-forming clumps and a tail consisting of three distinct, extremely compact star clusters that are multiply-imaged by gravitational lensing. The star clusters have effective radii of $R_{\rm{eff}} \sim 5$ pc, stellar masses of $M_{*} \sim 10^{6}-10^{7}\,M_{\odot}$, and high stellar mass surface densities of $\Sigma_{*} > 10^{4}\,M_{\odot}~\rm{pc}^{-2}$. While their stellar populations are very young ($\sim 5-9$ Myr), their dynamical ages exceed unity, consistent with the clusters being gravitationally bound systems. Placing the star clusters in the size vs.~stellar mass density plane, we find they occupy a region similar to other high-redshift star clusters within galaxies observed recently with JWST, being significantly more massive and denser than local star clusters. Spatially resolved analysis of the brightest clump reveals a compact, intensely star-forming core. The ionizing photon production efficiency ($\xi_{\rm{ion}}$) is slightly suppressed in this central region, potentially indicating a locally elevated Lyman continuum escape fraction facilitated by feedback-driven channels.

astro-ph.GA↗

arXiv2025

Analyzing Exoplanet Transits Observed with the WFC3/UVIS G280 Grism

Here we describe a Jupyter notebook demonstrating methods for the reduction and analysis of exoplanet transit observations taken with the WFC3/UVIS G280 grism. Released on Space Telescope's hst_notebooks GitHub repository, this notebook presents an example workflow for processing time-series observations taken with the G280 grism - from the calibrated flat-fielded spectra to transit light curves ready for fitting. The specific routines presented in the notebook are explained here, and are meant to highlight data reduction steps that users will typically apply to extract transit light curves. The steps include background subtraction, spatial and temporal cosmic ray correction, spectral trace fitting, spectral extraction, and light curve generation. The end products of the routines in the Jupyter notebook are the raw broadband and spectroscopic light curves, which can be ingested into publicly available light curve fitting tools to extract planetary transmission spectra.

astro-ph.IM↗

arXiv2023

Self-Driving Telescopes: Autonomous Scheduling of Astronomical Observation Campaigns with Offline Reinforcement Learning

Modern astronomical experiments are designed to achieve multiple scientific goals, from studies of galaxy evolution to cosmic acceleration. These goals require data of many different classes of night-sky objects, each of which has a particular set of observational needs. These observational needs are typically in strong competition with one another. This poses a challenging multi-objective optimization problem that remains unsolved. The effectiveness of Reinforcement Learning (RL) as a valuable paradigm for training autonomous systems has been well-demonstrated, and it may provide the basis for self-driving telescopes capable of optimizing the scheduling for astronomy campaigns. Simulated datasets containing examples of interactions between a telescope and a discrete set of sky locations on the celestial sphere can be used to train an RL model to sequentially gather data from these several locations to maximize a cumulative reward as a measure of the quality of the data gathered. We use simulated data to test and compare multiple implementations of a Deep Q-Network (DQN) for the task of optimizing the schedule of observations from the Stone Edge Observatory (SEO). We combine multiple improvements on the DQN and adjustments to the dataset, showing that DQNs can achieve an average reward of 87%+-6% of the maximum achievable reward in each state on the test set. This is the first comparison of offline RL algorithms for a particular astronomical challenge and the first open-source framework for performing such a comparison and assessment task.

astro-ph.IM↗

arXiv2023

Self-consistent Combined HST, K-band, and Spitzer Photometric Catalogs of the BUFFALO Survey Fields

This manuscript presents new astronomical source catalogs using data from the BUFFALO Survey. These catalogs contain detailed information for over 100,000 astronomical sources in the 6 BUFFALO clusters: Abell 370, Abell 2744, Abell S1063, MACS 0416, MACS 0717, and MACS 1149 spanning a total 240 arcmin^2. The catalogs include positions and forced photometry measurements of these objects in the F275W, F336W, F435W, F606W, F814W, F105W, F125W, F140W, and F160W HST -bands, Keck-NIRC2/VLT-HAWKI Ks band, and IRAC Channel 1 and 2 bands. Additionally, we include photometry measurements in the F475W, F625W, and F110W bands for Abell 370. This catalog also includes photometric redshift estimates computed via template fitting using LePhare. When comparing to spectroscopic reference, we obtain an outlier fraction of 9.2% and scatter, normalized median absolute deviation (NMAD), of 0.062. The catalogs are publicly available for their use by the community.

astro-ph.GA↗

arXiv2023

A new step forward in realistic cluster lens mass modelling: Analysis of Hubble Frontier Field Cluster Abell S1063 from joint lensing, X-ray and galaxy kinematics data

We present a new method to simultaneously/self-consistently model the mass distribution of galaxy clusters that combines constraints from strong lensing features, X-ray emission and galaxy kinematics measurements. We are able to successfully decompose clusters into their collisionless and collisional mass components thanks to the X-ray surface brightness, as well as using the dynamics of cluster members to obtain more accurate masses with the fundamental plane of elliptical galaxies. Knowledge from all observables is included through a consistent Bayesian approach in the likelihood or in physically motivated priors. We apply this method to the galaxy cluster Abell S1063 and produce a mass model that we publicly release with this paper. The resulting mass distribution presents a different ellipticities for the intra-cluster gas and the other large-scale mass components; and deviation from elliptical symmetry in the main halo. We assess the ability of our method to recover the masses of the different elements of the cluster using a mock cluster based on a simplified version of our Abell S1063 model. Thanks to the wealth of information provided by the mass model and the X-ray emission, we also found evidence for an on-going merger event with gas sloshing from a smaller infalling structure into the main cluster. In agreement with previous findings, the total mass, gas profile and gas mass fraction are consistent with small deviations from the hydrostatic equilibrium. This new mass model for Abell S1063 is publicly available as is the software used to construct it through the \textsc{Lenstool} package.

astro-ph.CO↗

arXiv2022

Noise2Astro: Astronomical Image Denoising With Self-Supervised NeuralNetworks

In observational astronomy, noise obscures signals of interest. Large-scale astronomical surveys are growing in size and complexity, which will produce more data and increase the workload of data processing. Developing automated tools, such as convolutional neural networks (CNN), for denoising has become a promising area of research. We investigate the feasibility of CNN-based self-supervised learning algorithms (e.g., Noise2Noise) for denoising astronomical images. We experimented with Noise2Noise on simulated noisy astronomical data. We evaluate the results based on the accuracy of recovering flux and morphology. This algorithm can well recover the flux for Poisson noise ( $98.13${\raisebox{0.5ex}{\tiny$^{+0.77}_{-0.90} $}$\large\%$}) and for Gaussian noise when image data has a smooth signal profile ($96.45${\raisebox{0.5ex}{\tiny$^{+0.80}_{-0.96} $}$\large\%$}).

astro-ph.IM↗

arXiv2020

The BUFFALO HST Survey

The Beyond Ultra-deep Frontier Fields and Legacy Observations (BUFFALO) is a 101 orbit + 101 parallel Cycle 25 Hubble Space Telescope Treasury program taking data from 2018-2020. BUFFALO will expand existing coverage of the Hubble Frontier Fields (HFF) in WFC3/IR F105W, F125W, and F160W and ACS/WFC F606W and F814W around each of the six HFF clusters and flanking fields. This additional area has not been observed by HST but is already covered by deep multi-wavelength datasets, including Spitzer and Chandra. As with the original HFF program, BUFFALO is designed to take advantage of gravitational lensing from massive clusters to simultaneously find high-redshift galaxies which would otherwise lie below HST detection limits and model foreground clusters to study properties of dark matter and galaxy assembly. The expanded area will provide a first opportunity to study both cosmic variance at high redshift and galaxy assembly in the outskirts of the large HFF clusters. Five additional orbits are reserved for transient followup. BUFFALO data including mosaics, value-added catalogs and cluster mass distribution models will be released via MAST on a regular basis, as the observations and analysis are completed for the six individual clusters.

astro-ph.GA↗