arXiv subjects

C. T. Christy

Publications and source records attributed to C. T. Christy.

8 recordsLinked to original sources

arXiv2026

One year of broadband radio monitoring of the enigmatic transient GRB 250702B reveals the evolution of the relativistic jet

We present an extensive radio monitoring campaign of the unique extragalactic transient GRB 250702B, with observations spanning 0.65-233 GHz from 6-356 d (observer frame) post-discovery. The radio emission shows a smoothly evolving peaked synchrotron spectrum consistent with an adiabatic shock expanding into a stratified ambient medium ($n_e\propto R^{-k}$; $k= 1.5-2$). We detect significant variability in the low frequency ($\leq3$ GHz) light curves which we interpret as interstellar scintillation, placing an approximate bound on the blast wave image size of $1.2\times10^{16}\lesssim R_{\perp} \lesssim 5\times10^{17}$ cm. The temporal evolution of the flux density and critical synchrotron frequencies suggest the shock that powers the radio emission is potentially a wide-angle $θ_j\gtrsim15$ deg, low Lorentz factor ($Γ\lesssim10$) jet, or a narrow $θ_j\lesssim2$ deg highly relativistic jet. A narrow jet is expected for a stellar-mass black hole engine, such as a helium star merger, and the beaming-corrected kinetic energy in this scenario is consistent with the known distribution for long GRBs ($E_K\sim10^{51}$ erg). The wide-angle jet scenario would instead require a progenitor involving prolonged accretion. We derive and show an intermediate or stellar-mass black hole tidal disruption event are viable possibilities. The beaming-corrected kinetic energy in this scenario is on the low end of the known distribution for relativistic SMBH TDEs ($E_K\sim10^{50}$ erg). We disfavour an SMBH TDE due to lack of compatibility with the observed timescales. The detection of a jet shut off within the next year would favour a WD-IMBH TDE due to the shorter theoretical duration of super-Eddington accretion than the main-sequence TDE channels.

astro-ph.HE↗

arXiv2026

Early Multiwavelength Observations of AT 2026fgk: The Luminous Afterglow to Sub-luminous GRB 260310A, Identified Independently of a Gamma-ray Trigger

The origins of sub-luminous ($L_\mathrm{γ,\mathrm{iso}} < 10^{49.5}$\,erg\,s$^{-1}$) gamma-ray bursts (GRBs) associated with broad-lined Type~Ic supernovae (Ic-BL SNe) are poorly understood, in part due to the low discovery rate and faint afterglows. Here we present the identification of the optical afterglow of Fermi-GBM-detected GRB\,260310A (AT\,2026fgk) as a rapidly rising ($>1\,$mag\,d$^{-1}$), red ($g-r=0.4$\,mag) transient using the Gravitational-wave Optical Transient Observatory, Large Array Survey Telescope, and Zwicky Transient Facility (ZTF) data streams. We present multiwavelength follow-up observations from the first 50\,days, which reveal that GRB 260310A/AT\,2026fgk was sub-luminous ($L_\mathrm{γ,iso}=10^{48.8}\,$erg\,s$^{-1}$); it was the most nearby ($z=0.153$) afterglow identified blindly by an optical survey; and that it is one of the brightest afterglows ever observed at X-ray, optical, and radio (cm to mm) wavelengths. We spectroscopically confirm an underlying Ic-BL SN with properties typical of GRB-SNe ($M_\mathrm{ej}\approx3\,M_\odot$, $E_{\rm K}\approx 10^{52}\,$erg). With basic modeling of the afterglow, including the long optical rise ($\approx10^{3}\,$s), we infer either a low initial Lorentz factor ($Γ_0\approx40$) or a slightly off-axis viewing angle ($\lesssim3^\circ$). The host galaxy's mass and star formation rate are similar to the hosts of other sub-luminous GRBs. ZTF's flux-limited survey gives a volumetric rate of AT\,2026fgk-like events of $0.30^{+1.37}_{-0.29}\,$Gpc\,$^{-3}$\,yr$^{-1}$, which is consistent with the on-axis, high luminosity ($L_{\rm γ,iso}>10^{49.5}$\,erg\,s$^{-1}$) long-GRB rate. The similarity in the rates strongly constrains the prevalence of low-$Γ_0$ bursts and the beaming of the initial relativistic material in GRBs.

astro-ph.HE↗

arXiv2024

A second radio flare from the tidal disruption event AT2020vwl: a delayed outflow ejection?

We present the discovery of a second radio flare from the tidal disruption event (TDE) AT2020vwl via long-term monitoring radio observations. Late-time radio flares from TDEs are being discovered more commonly, with many TDEs showing radio emission 1000s of days after the stellar disruption, but the mechanism that powers these late-time flares is uncertain. Here we present radio spectral observations of the first and second radio flares observed from the TDE AT2020vwl. Through detailed radio spectral monitoring, we find evidence for two distinct outflow ejection episodes, or a period of renewed energy injection into the pre-existing outflow. We deduce that the second radio flare is powered by an outflow that is initially slower than the first flare, but carries more energy and accelerates over time. Through modelling the long-term optical and UV emission from the TDE as arising from an accretion disc, we infer that the second radio outflow launch or energy injection episode occurred approximately at the time of peak accretion rate. The fast decay of the second flare precludes environmental changes as an explanation, while the velocity of the outflow is at all times too low to be explained by an off-axis relativistic jet. Future observations that search for any link between the accretion disc properties and late time radio flares from TDEs will aid in understanding what powers the radio outflows in TDEs, and confirm if multiple outflow ejections or energy injection episodes are common.

astro-ph.HE↗

arXiv2024

Citizen ASAS-SN Data Release II: Variable Star Classification Using Citizen Science

We present the second results from Citizen ASAS-SN, a citizen science project for the All-Sky Automated Survey for Supernovae (ASAS-SN) hosted on the Zooniverse platform. Citizen ASAS-SN tasks users with classifying variable stars based on their light curves. We started with 94975 new variable candidates and identified 4432 new variable stars. The users classified the new variables as 841 pulsating variables, 2995 rotational variables, 350 eclipsing binaries, and 246 unknown variables. We found 68% user agreement for user-classified pulsating variables, 51% for rotational variables, and 77% for eclipsing binaries. We investigate user statistics and compare new variables to known variables. We present a sample of variables flagged as interesting or unusual.

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arXiv2024

Eight Years of Light from ASASSN-15oi: Towards Understanding the Late-time Evolution of TDEs

We present the results from an extensive follow-up campaign of the Tidal Disruption Event (TDE) ASASSN-15oi spanning $δt \sim 10 - 3000$ d, offering an unprecedented window into the multiwavelength properties of a TDE during its first $\approx 8$ years of evolution. ASASSN-15oi is one of the few TDEs with strong detections at X-ray, optical/UV, and radio wavelengths and featured two delayed radio flares at $δt \sim 180$ d and $δt \sim 1400$ d. Our observations at $> 1400$ d reveal an absence of thermal X-rays, a late-time variability in the non-thermal X-ray emission, and sharp declines in the non-thermal X-ray and radio emission at $δt \sim 2800$ d and $\sim 3000$ d, respectively. The UV emission shows no significant evolution at $>400$ d and remains above the pre-TDE level. We show that a cooling envelope model can explain the thermal emission consistently across all epochs. We also find that a scenario involving episodic ejection of material due to stream-stream collisions is conducive to explaining the first radio flare. Given the peculiar spectral and temporal evolution of the late-time emission, however, constraining the origins of the second radio flare and the non-thermal X-rays remains challenging. Our study underscores the critical role of long-term, multiwavelength follow-up.

astro-ph.HE↗

arXiv2022

The ASAS-SN Catalog of Variable Stars X: Discovery of 116,000 New Variable Stars Using g-band Photometry

The All-Sky Automated Survey for Supernovae (ASAS-SN) is the first optical survey to monitor the entire sky, currently with a cadence of $\lesssim 24$ hours down to $g \lesssim 18.5$ mag. ASAS-SN has routinely operated since 2013, collecting $\sim$ 2,000 to over 7,500 epochs of $V$ and $g-$band observations per field to date. This work illustrates the first analysis of ASAS-SN's newer, deeper, higher cadence $g-$band data. From an input source list of ${\sim}55$ million isolated sources with $g<18$~mag, we identified $1.5\times10^6$ variable star candidates using a random forest classifier trained on features derived from $\textit{Gaia}$, 2MASS, and AllWISE. Using ASAS-SN $g-$band light curves, and an updated random forest classifier augmented with data from Citizen ASAS-SN, we classified the candidate variables into 8 broad variability types. We present a catalog of ${\sim}116,000$ new variable stars with high classification probabilities, including ${\sim}111,000$ periodic variables and ${\sim}5,000$ irregular variables. We also recovered ${\sim}263,000$ known variable stars.

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arXiv2021

Citizen ASAS-SN Data Release I: Variable Star Classification Using Citizen Science

We present the first results from Citizen ASAS-SN, a citizen science project for the All-Sky Automated Survey for Supernovae (ASAS-SN) hosted on the Zooniverse platform. Citizen ASAS-SN utilizes the newer, deeper, higher cadence ASAS-SN $g$-band data and tasks volunteers to classify periodic variable star candidates based on their phased light curves. We started from 40,640 new variable candidates from an input list of ${\sim} 7.4$ million stars with $δ< -60^\circ$ and the volunteers identified 10,420 new discoveries which they classified as 4,234 pulsating variables, 3,132 rotational variables, 2,923 eclipsing binaries, and 131 variables flagged as Unknown. They classified known variable stars with an accuracy of 89% for pulsating variables, 81% for eclipsing binaries, and 49% for rotational variables. We examine user performance, agreement between users, and compare the citizen science classifications with our machine learning classifier updated for the $g$-band light curves. In general, user activity correlates with higher classification accuracy and higher user agreement. We used the user's "Junk" classifications to develop an effective machine learning classifier to separate real from false variables, and there is a clear path for using this "Junk" training set to significantly improve our primary machine learning classifier. We also illustrate the value of Citizen ASAS-SN for identifying unusual variables with several examples.

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arXiv2018

Efficient Joint Sampling of Impact Parameters and Transit Depths in Transiting Exoplanet Light Curves

When fitting transiting exoplanet lightcurves, it is usually desirable to have ranges and/or priors for the parameters which are to be retrieved that include our degree of knowledge (or ignorance) in the routines which are being used. In Markov Chain Monte Carlo (MCMC) routines, for example, these enter as prior distributions. These can either represent our current knowledge of the distribution of such parameters (e.g., based on their observed values) or physically plausible parameters ranges to be sampled. Among the parameters that are constrained by transiting exoplanet lightcurves, there are two which are of much physical significance: the impact parameter of the orbit, $b = (a/R_*)\cos i $, and the planet-to-star radius ratio, $p = R_p/R_s$ (which defines the transit depth, $δ= p^2$). These two are natural parameters to extract and constrain as they usually have well defined limits. A common set of "uninformative" priors used for those two parameters are uniform priors. However, this poses a sampling problem especially important for grazing orbits: given that we sample a value $p_i$ from the prior on $p$, the only physically plausible values for $b$ to be sampled given $p_i$ are those that satisfy $b < 1 + p_i$. If we simply reject the sample if the sampled value of b is greater than $1 + p_i$, then we will reject points from a significant portion of the prior area depending on its size. It is desirable, thus, to have an algorithm that efficiently samples values from the physically plausible zone in the $(b,p)$ plane. Here we present such an algorithm.

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