arXiv subjects

Christopher Mann

Publications and source records attributed to Christopher Mann.

5 recordsLinked to original sources

arXiv2026

TIC-65910228 b / NGTS-38 b, a 180 day transiting warm super-Jupiter

We present the discovery of TIC-65910228 b / NGTS-38 b, a giant exoplanet with a radius of $1.081\pm0.047$ R$_\text{J}$ and a mass of $4.78_{-0.37}^{+0.39}$ M$_\text{J}$ on a long-period ($180.52797\pm0.00036$ day), moderately eccentric ($e=0.3086\pm0.010$) orbit transiting a bright (V=$10.230\pm0.020$ mag) metal rich ([Fe/H]=$0.33\pm0.09$, 'dex') F6V-F7V type host star. The planet was initially detected from a single transit in TESS Sector 33. A photometric monitoring campaign of 228 nights with NGTS detected a transit egress of the planet, which together with spectroscopic radial velocity monitoring with CORALIE and HARPS identified an orbital period of ~180.5,d. These radial velocity measurements also showed the mass of the companion to be planetary. Additional transit observations coordinated by the TESS follow-up observing program allowed further confirmation and refinement of this period. With its relatively cool equilibrium temperature of $457\pm11$ K, NGTS-38 b joins a small but growing population of well characterised transiting warm-Jupiters and has one of the longest periods of any discovered to date. The target is situated in the LOPS2 field of the upcoming PLATO mission which will allow for greater refinement of the system parameters and potential for the discovery of additional companions too small and/or too long-period to be seen by TESS or NGTS. NGTS-38 b's bright host star and wide orbital separation make it an attractive target for further study, including potential measurement of its spin-orbit alignment or targeted exomoon/ring searches.

astro-ph.EP↗

arXiv2022

Detection of Atmospheric Escape from Four Young Mini Neptunes

We use Keck/NIRSPEC to survey a sample of of young ($<$1 Gyr), short period mini Neptunes orbiting nearby K dwargs to measure their mass loss via the metastable helium line. We detect helium absorption from all four of the targets in our initial sample. The first detection, around TOI 560b, was announced in a previous paper. We now announce three additional detections around TOI 1430.01, 2076b, and TOI 1683.01. All four planets show an average in-transit excess absorption of 0.7--1.0%. However, the outflows differ in their kinematic properties. TOI 1430b exhibits pre-ingress absorption, while TOI 2076b's outflow is exceptionally optically thick and shows significant post-egress absorption. For all four planets, the width of the measured helium absorption signal is consistent with expectations for a photoevaporative outflow (10--30 km/s, 5000--10,000 K). Unless broadening mechanisms other than thermal velocity and the bulk outflow velocity are significant, our observations disfavor core-powered mass loss models, which predict much slower (1--3 km/s) outflows. We utilize both an isothermal Parker wind model and an order-of-magnitude method to estimate the mass loss timescale, and obtain $\sim$ a few hundred Myr for each planet. We conclude that many, if not all, of these planets will lose their hydrogen-rich envelopes and become super Earths. Our results demonstrate that most mini Neptunes orbiting sun-like stars have primordial atmospheres, and that photoevaporation is an efficient mechanism for stripping these atmospheres and transforming these planets into super Earths.

astro-ph.EP↗

arXiv2022

DH-GAN: A Physics-driven Untrained Generative Adversarial Network for 3D Microscopic Imaging using Digital Holography

Digital holography is a 3D imaging technique by emitting a laser beam with a plane wavefront to an object and measuring the intensity of the diffracted waveform, called holograms. The object's 3D shape can be obtained by numerical analysis of the captured holograms and recovering the incurred phase. Recently, deep learning (DL) methods have been used for more accurate holographic processing. However, most supervised methods require large datasets to train the model, which is rarely available in most DH applications due to the scarcity of samples or privacy concerns. A few one-shot DL-based recovery methods exist with no reliance on large datasets of paired images. Still, most of these methods often neglect the underlying physics law that governs wave propagation. These methods offer a black-box operation, which is not explainable, generalizable, and transferrable to other samples and applications. In this work, we propose a new DL architecture based on generative adversarial networks that uses a discriminative network for realizing a semantic measure for reconstruction quality while using a generative network as a function approximator to model the inverse of hologram formation. We impose smoothness on the background part of the recovered image using a progressive masking module powered by simulated annealing to enhance the reconstruction quality. The proposed method is one of its kind that exhibits high transferability to similar samples, which facilitates its fast deployment in time-sensitive applications without the need for retraining the network. The results show a considerable improvement to competitor methods in reconstruction quality (about 5 dB PSNR gain) and robustness to noise (about 50% reduction in PSNR vs noise increase rate).

cs.IR↗

arXiv2020

Deep DIH : Statistically Inferred Reconstruction of Digital In-Line Holography by Deep Learning

Digital in-line holography is commonly used to reconstruct 3D images from 2D holograms for microscopic objects. One of the technical challenges that arise in the signal processing stage is removing the twin image that is caused by the phase-conjugate wavefront from the recorded holograms. Twin image removal is typically formulated as a non-linear inverse problem due to the irreversible scattering process when generating the hologram. Recently, end-to-end deep learning-based methods have been utilized to reconstruct the object wavefront (as a surrogate for the 3D structure of the object) directly from a single-shot in-line digital hologram. However, massive data pairs are required to train deep learning models for acceptable reconstruction precision. In contrast to typical image processing problems, well-curated datasets for in-line digital holography does not exist. Also, the trained model highly influenced by the morphological properties of the object and hence can vary for different applications. Therefore, data collection can be prohibitively cumbersome in practice as a major hindrance to using deep learning for digital holography. In this paper, we proposed a novel implementation of autoencoder-based deep learning architecture for single-shot hologram reconstruction solely based on the current sample without the need for massive datasets to train the model. The simulations results demonstrate the superior performance of the proposed method compared to the state of the art single-shot compressive digital in-line hologram reconstruction method.

eess.IV↗

arXiv2018

A Multi-Mass Velocity Dispersion Model of 47 Tucanae Indicates No Evidence for an Intermediate Mass Black Hole

In this paper, we analyze stellar proper motions in the core of the globular cluster 47 Tucanae to explore the possibility of an intermediate-mass black hole (IMBH) influence on the stellar dynamics. Our use of short-wavelength photometry affords us an exceedingly clear view of stellar motions into the very center of the crowded core, yielding proper motions for $>$50,000 stars in the central 2'. We model the velocity dispersion profile of the cluster using an isotropic Jeans model. The density distribution is taken as a central IMBH point mass added to a combination of King templates. We individually model the general low-mass cluster objects (main sequence/giant stars), as well as the concentrated populations of heavy binary systems and dark stellar remnants. Using unbinned likelihood model fitting, we find that the inclusion of the concentrated populations in our model plays a crucial role in fitting for an IMBH mass. The concentrated binaries and stellar-mass black holes (BHs) produce a sufficient velocity dispersion signal in the core so as to make an IMBH unnecessary to fit the observations. We additionally determine that a stellar-mass BH retention fraction of $\gtrsim 8.5\%$ becomes incompatible with our observed velocities in the core.

astro-ph.GA↗