arXiv subjects

Nicholas Kern

Publications and source records attributed to Nicholas Kern.

17 recordsLinked to original sources

arXiv2026

The MegaWave Radio Surveyor

Several Decadal-level questions in astrophysics, exoplanets, astrobiology, and cosmology can be addressed only at low radio frequencies inaccessible from Earth. The MegaWave Radio Surveyor would open this largely-unexplored region of the electromagnetic spectrum with a space-based interferometer to (1)~Track the space weather of other stars; (2)~Detect magnetically-generated emission from exoplanets to probe their interiors and assess magnetic shielding of their atmospheres; (3)~Probe the Universe's evolution during the Dark Ages via the highly-redshifted HI hyperfine line; and (4)~Assess the role of cosmic rays and magnetic fields in the cosmic web. An Astrophysics Strategic Technology & Research Accelerator (ASTRA) Initiative concept, the MegaWave Radio Surveyor's science objectives respond to the Pathways to Discovery Decadal Survey and three other National Academies studies, and it would serve as a Formative Era mission in the Enduring Quests, Daring Visions roadmap. Developments in U.S. space industries enable this observatory to be realized. The MegaWave Radio Surveyor would offer a versatile, scalable, and resilient architecture capable of sensitive and simultaneous observations below 45~MHz and unprecedented angular resolution at these frequencies. The concept builds upon NASA's Sun Radio Interferometer Space Experiment (SunRISE), Star-Planet Activity Research CubeSat (SPARCS), and Lunar Surface Electromagnetics Experiment (LuSEE-Night). The MegaWave Radio Surveyor could leverage multiple elements of the Artemis program, such as access to and beyond cislunar space and communications, and there are opportunities to infuse new autonomy/AI modes for mission operations. By opening one of the last windows in the electromagnetic spectrum and pioneering space interferometry at unprecedented scales, the MegaWave Radio Surveyor would establish a transformational capability.

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arXiv2026

Machine Learning and the SKA for Cosmic Dawn and the Epoch of Reionization

When operational, the SKA will generate unprecedented amounts of data and provide exquisite sensitivity for 21 cm tomography of Cosmic Dawn (CD) and the Epoch of Reionization (EoR). With this comes opportunities for new data-driven algorithms that unlock new methods for instrument modelling, data analysis, theoretical simulation, and inference for understanding the high-redshift universe. In this chapter, we provide an overview of some machine learning algorithms that have been proposed for CD and EoR science with the SKA

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arXiv2026

Inferring Cosmology and Astrophysics from the High-redshift 21cm Signal with SKA-Low

The Square Kilometre Array's low frequency telescope (SKA-Low) will enable inference of astrophysical and cosmological parameters from the redshifted 21 cm signal, probing the Cosmic Dawn and Epoch of Reionisation. While the power spectrum is the primary target for initial detection, the inherently non-Gaussian nature of the 21 cm signal, driven by the patchy evolution of ionised regions and spin temperature fluctuations, encodes rich information accessible through higher-order statistics and morphological measurements. Extracting these constraints requires diverse inference tools, encompassing both sophisticated modelling frameworks (analytical, semi-numerical, numerical, and emulators) used to predict the 21 cm signal, and advanced inference techniques (Bayesian, simulation-based, field-level) to connect statistics to the underlying physics. This chapter reviews these tools and explores the constraining power of different statistical probes accessible with SKA-Low, including the power spectrum, statistics beyond order two, moments of the signal distribution, and morphological measures. Combining these complementary statistics is crucial for breaking parameter degeneracies and unveiling the properties of the early Universe. We specifically assess the potential of the initial SKA-Low configuration (AA*) to measure galaxy and IGM properties, demonstrating its capability for early science results. This chapter forms part of a comprehensive set detailing the Epoch of Reionisation and Cosmic Dawn science case for the SKA-Low telescope.

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arXiv2026

Observations of the Cosmic Dawn and Epoch of Reionization with the SKAO: Observational Lessons Learned from Precursors and Pathfinder Instruments

This chapter summarizes the observational lessons learned after two decades of observations of the Cosmic Dawn (CD) and Epoch of Reionization (EoR) with SKAO pathfinders and precursors. We will describe the effort towards building accurate simulation pipelines for actual observations and summarize the approaches that different groups have taken to calibrate and mitigate systematic effects such as sky model incompleteness, limited instrument models and antenna mutual coupling. We conclude by discussing the impact that these lessons may have on the design and analysis of upcoming SKAO observations of the Cosmic Dawn and Epoch of Reionization.

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arXiv2025

A Differentiable, End-to-End Forward Model for 21 cm Cosmology: Estimating the Foreground, Instrument, and Signal Joint Posterior

We present a differentiable, end-to-end Bayesian forward modeling framework for line intensity mapping cosmology experiments, with a specific focus on low-frequency radio telescopes targeting the redshifted 21 cm line from neutral hydrogen as a cosmological probe. Our framework is capable of posterior density estimation of the cosmological signal jointly with foreground and telescope parameters at the field level. Our key aim is to be able to optimize the model's high-dimensional, non-linear, and ill-conditioned parameter space, while also sampling from it to perform robust uncertainty quantification within a Bayesian framework. We show how a differentiable programming paradigm, accelerated by recent advances in machine learning software and hardware, can make this computationally-demanding, end-to-end Bayesian approach feasible. We demonstrate a proof-of-concept on a signal recovery problem for the Hydrogen Epoch of Reionization Array experiment, highlighting the framework's ability to build confidence in early 21 cm signal detections even in the presence of poorly understood foregrounds and instrumental systematics. We use a Hessian-preconditioned Hamiltonian Monte Carlo algorithm to efficiently sample our parameter space with a dimensionality approaching $N\sim10^5$, which enables joint, end-to-end nuisance parameter marginalization over foreground and instrumental terms. Lastly, we introduce a new spherical harmonic formalism that is a complete and orthogonal basis on the cut sky relevant to drift-scan radio surveys, which we call the spherical stripe harmonic formalism, and it's associated three-dimensional basis, the spherical stripe Fourier-Bessel formalism.

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arXiv2023

On the use of temporal filtering for mitigating galactic synchrotron calibration bias in 21 cm reionization observations

Precision antenna calibration is required for mitigating the impact of foreground contamination in 21 cm cosmological radio surveys. One widely studied source of error is the effect of missing point sources in the calibration sky model; however, poorly understood diffuse galactic emission also creates a calibration bias that can complicate the clean separation of foregrounds from the 21 cm signal. In this work, we present a technique for suppressing this bias with temporal filtering of radio interferometric visibilities observed in a drift-scan mode. We demonstrate this technique on mock simulations of the Hydrogen Epoch of Reionization Array (HERA) experiment. Inspecting the recovered calibration solutions, we find that our technique reduces spurious errors by over an order of magnitude. This improved accuracy approaches the required accuracy needed to make a fiducial detection of the 21 cm signal with HERA, but is dependent on a number of external factors that we discuss. We also explore different types of temporal filtering techniques and discuss their relative performance and tradeoffs.

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arXiv2022

The Astropy Project: Sustaining and Growing a Community-oriented Open-source Project and the Latest Major Release (v5.0) of the Core Package

The Astropy Project supports and fosters the development of open-source and openly-developed Python packages that provide commonly needed functionality to the astronomical community. A key element of the Astropy Project is the core package $\texttt{astropy}$, which serves as the foundation for more specialized projects and packages. In this article, we summarize key features in the core package as of the recent major release, version 5.0, and provide major updates for the Project. We then discuss supporting a broader ecosystem of interoperable packages, including connections with several astronomical observatories and missions. We also revisit the future outlook of the Astropy Project and the current status of Learn Astropy. We conclude by raising and discussing the current and future challenges facing the Project.

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arXiv2021

SKA-Low Intensity Mapping Pathfinder Updates: Deeper 21 cm Power Spectrum Limits from Improved Analysis Frameworks

The Square Kilometre Array (SKA) is a planned radio interferometer of unprecedented scale that will revolutionize low-frequency radio astronomy when completed. In particular, one of its core science drivers is the systematic mapping of the Cosmic Dawn and Epoch of Reionization, which mark the birth of the first stars and galaxies in the Universe and their subsequent ionization of primordial intergalactic hydrogen, respectively. The SKA will offer the most sensitive view of these poorly understood epochs using the redshifted 21 cm hyperfine signal from intergalactic hydrogen. However, significant technical challenges stand in the way of realizing this scientific promise. These mainly involve the mitigation of systematics coming from astrophysical foregrounds, terrestrial radio interference, and the instrumental response. The Low Frequency Array, the Murchison Widefield Array and the Hydrogen Epoch of Reionization Array are SKA pathfinder experiments that have developed a variety of strategies for addressing these challenges, each with unique characteristics that stem largely from their different instrumental designs. We outline these various directions, highlighting key differences and synergies, and discuss how these relate to the future of low-frequency intensity mapping with the SKA. We also briefly summarize the challenges associated with modeling the 21 cm signal and discuss the methodologies being proposed for inferring constraints on astrophysical models.

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arXiv2021

HERA Phase I Limits on the Cosmic 21-cm Signal: Constraints on Astrophysics and Cosmology During the Epoch of Reionization

Recently, the Hydrogen Epoch of Reionization Array (HERA) collaboration has produced the experiment's first upper limits on the power spectrum of 21-cm fluctuations at z~8 and 10. Here, we use several independent theoretical models to infer constraints on the intergalactic medium (IGM) and galaxies during the epoch of reionization (EoR) from these limits. We find that the IGM must have been heated above the adiabatic cooling threshold by z~8, independent of uncertainties about the IGM ionization state and the nature of the radio background. Combining HERA limits with galaxy and EoR observations constrains the spin temperature of the z~8 neutral IGM to 27 K < T_S < 630 K (2.3 K < T_S < 640 K) at 68% (95%) confidence. They therefore also place a lower bound on X-ray heating, a previously unconstrained aspects of early galaxies. For example, if the CMB dominates the z~8 radio background, the new HERA limits imply that the first galaxies produced X-rays more efficiently than local ones (with soft band X-ray luminosities per star formation rate constrained to L_X/SFR = { 10^40.2, 10^41.9 } erg/s/(M_sun/yr) at 68% confidence), consistent with expectations of X-ray binaries in low-metallicity environments. The z~10 limits require even earlier heating if dark-matter interactions (e.g., through millicharges) cool down the hydrogen gas. Using a model in which an extra radio background is produced by galaxies, we rule out (at 95% confidence) the combination of high radio and low X-ray luminosities of L_{r,\nu}/SFR > 3.9 x 10^24 W/Hz/(M_sun/yr) and L_X/SFR<10^40 erg/s/(M_sun/yr). The new HERA upper limits neither support nor disfavor a cosmological interpretation of the recent EDGES detection. The analysis framework described here provides a foundation for the interpretation of future HERA results.

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arXiv2020

Gaussian Process Foreground Subtraction and Power Spectrum Estimation for 21 cm Cosmology

One of the primary challenges in enabling the scientific potential of 21 cm intensity mapping at the Epoch of Reionization (EoR) is the separation of astrophysical foreground contamination. Recent works have claimed that Gaussian process regression (GPR) can robustly perform this separation, particularly at low Fourier $k$ wavenumbers where the signal reaches its peak signal-to-noise ratio. We revisit this topic by casting GPR foreground subtraction (GPR-FS) into the quadratic estimator formalism, thereby putting its statistical properties on stronger theoretical footing. We find that GPR-FS can distort the window functions at these low k modes, which, without proper decorrelation, make it difficult to probe the EoR power spectrum. Incidentally, we also show that GPR-FS is in fact closely related to the widely studied optimal quadratic estimator. As a case study, we look at recent power spectrum upper limits from the Low Frequency Array (LOFAR) that utilized GPR-FS. We pay close attention to their normalization scheme, showing that it is particularly sensitive to signal loss when the EoR covariance is misestimated. This implies possible ramifications for recent astrophysical interpretations of the LOFAR limits, because many of the EoR models ruled out do not fall within the bounds of the covariance models explored by LOFAR. Being more robust to this bias (although not entirely free of it), we conclude that the quadratic estimator is a more natural framework for implementing GPR-FS and computing the 21 cm power spectrum.

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arXiv2020

DAYENU: A Simple Filter of Smooth Foregrounds for Intensity Mapping Power Spectra

We introduce DAYENU, a linear, spectral filter for HI intensity mapping that achieves the desirable foreground mitigation and error minimization properties of inverse co-variance weighting with minimal modeling of the underlying data. Beyond 21 cm power-spectrum estimation, our filter is suitable for any analysis where high dynamic-range removal of spectrally smooth foregrounds in irregularly (or regularly) sampled data is required, something required by many other intensity mapping techniques. Our filtering matrix is diagonalized by Discrete Prolate Spheroidal Sequences which are an optimal basis to model band-limited foregrounds in 21 cm intensity mapping experiments in the sense that they maximally concentrate power within a finite region of Fourier space. We show that DAYENU enables the access of large-scale line-of-sight modes that are inaccessible to tapered DFT estimators. Since these modes have the largest SNRs, DAYENU significantly increases the sensitivity of 21 cm analyses over tapered Fourier transforms. Slight modifications allow us to use DAYENU as a linear replacement for iterative delay CLEANing (DAYENUREST). We refer readers to the Code section at the end of this paper for links to examples and code.

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arXiv2019

Optimizing Sparse RFI Prediction using Deep Learning

Radio Frequency Interference (RFI) is an ever-present limiting factor among radio telescopes even in the most remote observing locations. When looking to retain the maximum amount of sensitivity and reduce contamination for Epoch of Reionization studies, the identification and removal of RFI is especially important. In addition to improved RFI identification, we must also take into account computational efficiency of the RFI-Identification algorithm as radio interferometer arrays such as the Hydrogen Epoch of Reionization Array grow larger in number of receivers. To address this, we present a Deep Fully Convolutional Neural Network (DFCN) that is comprehensive in its use of interferometric data, where both amplitude and phase information are used jointly for identifying RFI. We train the network using simulated HERA visibilities containing mock RFI, yielding a known "ground truth" dataset for evaluating the accuracy of various RFI algorithms. Evaluation of the DFCN model is performed on observations from the 67 dish build-out, HERA-67, and achieves a data throughput of 1.6$\times 10^{5}$ HERA time-ordered 1024 channeled visibilities per hour per GPU. We determine that relative to an amplitude only network including visibility phase adds important adjacent time-frequency context which increases discrimination between RFI and Non-RFI. The inclusion of phase when predicting achieves a Recall of 0.81, Precision of 0.58, and $F_{2}$ score of 0.75 as applied to our HERA-67 observations.

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arXiv2016

Inferring Gravitational Potentials from Mass Densities in Cluster-sized Halos

We use N-body simulations to quantify how the escape velocity in cluster-sized halos maps to the gravitational potential in a LambdaCDM universe. Using spherical density-potential pairs and the Poisson equation, we find that the matter density inferred gravitational potential profile predicts the escape velocity profile to within a few percent accuracy for group and cluster-sized halos (10^13 < M_200 < 10^15 M_sun, with respect to the critical density). The accuracy holds from just outside the core to beyond the virial radius. We show the importance of explicitly incorporating a cosmological constant when inferring the potential from the Poisson equation. We consider three density models and find that the Einasto and Gamma profiles provide a better joint estimate of the density and potential profiles than the Navarro, Frenk and White profile, which fails to accurately represent the escape velocity. For individual halos, the 1 sigma scatter between the measured escape velocity and the density-inferred potential profile is small (<5%). Finally, while the sub-halos show 15% biases in their representation of the particle velocity dispersion profile, the sub-halo escape velocity profile matches the dark matter escape velocity profile to high accuracy with no evidence for velocity bias outside 0.4r_200.

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arXiv2016

Stacking Caustic Masses from Galaxy Clusters

Ongoing and future spectroscopic surveys will measure numerous galaxy redshifts within tens of thousands of galaxy clusters. However, the sampling within these clusters will be low, 15 < N < 50 per cluster. With such data, it will be difficult to achieve accurate and precise mass estimates for individual clusters using phase-space mass estimation techniques. We develop and test a new stacking algorithm based on the caustic technique, which reduces the mass scatter in for ensemble clusters from 70% for individual clusters to less than 10% for ensemble clusters with only 15 galaxies per cluster and 100 clusters per ensemble. With > 1000 galaxies per ensemble phase-space, the escape-velocity edge becomes readily identifiable and the presence of interloping galaxies is minimized. We develop and test an algorithm to trace the projected phase-space surface directly, which results in minimally biased dynamical mass estimates. We then quantify how binning and sampling affect the phase-space-based mass estimates when using an observational proxy that incorporates realistic mass scatter, like richness, and find the added uncertainty in the binning procedure has minimal influence on the resulting bias and scatter of the stacked mass estimates.

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arXiv2016

Probing Theories of Gravity with Phase Space-Inferred Potentials of Galaxy Clusters

Modified theories of gravity provide us with a unique opportunity to generate innovative tests of gravity. In Chameleon f(R) gravity, the gravitational potential differs from the weak-field limit of general relativity (GR) in a mass dependent way. We develop a probe of gravity which compares high mass clusters, where Chameleon effects are weak, to low mass clusters, where the effects can be strong. We utilize the escape velocity edges in the radius/velocity phase space to infer the gravitational potential profiles on scales of 0.3-1 virial radii. We show that the escape edges of low mass clusters are enhanced compared to GR, where the magnitude of the difference depends on the background field value |fR0|. We validate our probe using N-body simulations and simulated light cone galaxy data. For a DESI (Dark Energy Spectroscopic Instrument) Bright Galaxy Sample, including observational systematics, projection effects, and cosmic variance, our test can differentiate between GR and Chameleon f(R) gravity models, |fR0| = 4e-6 (2e-6) at > 5{\sigma} (> 2{\sigma}), more than an order of magnitude better than current cluster-scale constraints.

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arXiv2015

Radio Properties of Young Stellar Objects in the Core of the Serpens South Infrared Dark Cloud

We present deep radio continuum observations of the star-forming core of the Serpens South Infrared Dark Cloud with the Karl G. Jansky Very Large Array (VLA). Observations were conducted in two bands centered at 7.25 GHz (4.14 cm) and 4.75 GHz (6.31 cm) with an rms of 8.5 and 11.1 microJy/beam, respectively. We also use 2MASS, Spitzer and Herschel data to put our radio observations in the context of young stellar populations characterized by near and far infrared observations. Within a 5 arcmin x 5 arcmin region of interest around the central cluster, we detect roughly eighteen radio sources, seven of which we determine are protostellar in nature due to their radio spectral indices and their association with infrared sources. We find evidence for a previously undetected embedded Class 0 protostar and reaffirm Class 0 protostellar classifications determined by previous millimeter wavelength continuum studies. We use our infrared data to derive mid-infrared luminosities for three of our protostellar sources and find relative agreement between the known YSO radio luminosity vs bolometric luminosity correlation. Lastly, we marginally detect an additional six radio sources at the 2-3 sigma level that lie within two arcseconds of infrared YSO candidates, providing motivation for higher sensitivity studies to clarify the nature of these sources and further probe embedded and/or low luminosity YSOs in Serpens South.

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arXiv2013

A Systematic Analysis of Caustic Methods for Galaxy Cluster Masses

We quantify the expected observed statistical and systematic uncertainties of the escape velocity as a measure of the gravitational potential and total mass of galaxy clusters. We focus our attention on low redshift (z < 0.15) clusters, where large and deep spectroscopic datasets currently exist. Utilizing a suite of Millennium Simulation semi-analytic galaxy catalogs, we find that the dynamical mass, as traced by either the virial relation or the escape velocity, is robust to variations in how dynamical friction is applied to "orphan" galaxies in the mock catalogs (i.e., those galaxies whose dark matter halos have fallen below the resolution limit). We find that the caustic technique recovers the known halo masses (M_200) with a third less scatter compared to the virial masses. The bias we measure increases quickly as the number of galaxies used decreases. For N_gal > 25, the scatter in the escape velocity mass is dominated by projections along the line-of-sight. Algorithmic uncertainties from the determination of the projected escape velocity profile are negligible. We quantify how target selection based on magnitude, color, and projected radial separation can induce small additional biases into the escape velocity masses. Using N_gal = 150 (25), the caustic technique has a per cluster scatter in ln(M|M_200) of 0.3 (0.5) and bias 1+/-3% (16+/-5%) for clusters with masses > 10^14M_solar at z<0.15.

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