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G. Ramsay

Publications and source records attributed to G. Ramsay.

At least 37 records · Page 2Linked to original sources

SN 2023tsz: A helium-interaction driven supernova in a very low-mass galaxy

SN 2023tsz is a Type Ibn supernova (SNe Ibn) discovered in an extremely low-mass host. SNe Ibn are an uncommon subtype of stripped-envelope core-collapse SNe. They are characterised by narrow helium emission lines in their spectra and are believed to originate from the collapse of massive Wolf-Rayet (WR) stars, though their progenitor systems still remain poorly understood. In terms of energetics and spectrophotometric evolution, SN 2023tsz is largely a typical example of the class, although line profile asymmetries in the nebular phase are seen, which may indicate the presence of dust formation or unshocked circumstellar material. Intriguingly, SN 2023tsz is located in an extraordinarily low-mass host galaxy that is in the 2nd percentile for SESN host masses and star formation rates (SFR). The host has a radius of 1.0 kpc, a $g$-band absolute magnitude of $-12.73$, and an estimated metallicity of $\log(Z_{*}/Z_{\odot}$) = $-1.56$. The SFR and metallicity of the host galaxy raise questions about the progenitor of SN 2023tsz. The low SFR suggests that a star with sufficient mass to evolve into a WR would be uncommon in this galaxy. Further, the very low-metallicity is a challenge for single stellar evolution to enable H and He stripping of the progenitor and produce a SN Ibn explosion. The host galaxy of SN 2023tsz adds another piece to the ongoing puzzle of SNe Ibn progenitors, and demonstrates that they can occur in hosts too faint to be observed in contemporary sky surveys at a more typical SN Ibn redshift.

astro-ph.HE↗

$\textit{Kilonova Seekers}$: the GOTO project for real-time citizen science in time-domain astrophysics

Time-domain astrophysics continues to grow rapidly, with the inception of new surveys drastically increasing data volumes. Democratised, distributed approaches to training sets for machine learning classifiers are crucial to make the most of this torrent of discovery -- with citizen science approaches proving effective at meeting these requirements. In this paper, we describe the creation of and the initial results from the $\textit{Kilonova Seekers}$ citizen science project, built to find transient phenomena from the GOTO telescopes in near real-time. $\textit{Kilonova Seekers}$ launched in July 2023 and received over 600,000 classifications from approximately 2,000 volunteers over the course of the LIGO-Virgo-KAGRA O4a observing run. During this time, the project has yielded 20 discoveries, generated a `gold-standard' training set of 17,682 detections for augmenting deep-learned classifiers, and measured the performance and biases of Zooniverse volunteers on real-bogus classification. This project will continue throughout the lifetime of GOTO, pushing candidates at ever-greater cadence, and directly facilitate the next-generation classification algorithms currently in development.

astro-ph.IM↗

Tracking the motion of a shock along a channel in the low solar corona

Shock waves are excited by coronal mass ejections (CMEs) and large-scale extreme-ultraviolet (EUV) wave fronts and can result in low-frequency radio emission under certain coronal conditions. In this work, we investigate a moving source of low-frequency radio emission as a CME and an associated EUV wave front move along a channel of a lower density, magnetic field, and Alfvén speed in the solar corona. Observations from the Atmospheric Imaging Assembly on board the Solar Dynamics Observatory, the Nançay Radio Heliograph (NRH), and the Irish Low Frequency Array(I-LOFAR) were analysed. Differential emission measure maps were generated to determine densities and Alfvén maps, and the kinematics of the EUV wave front was tracked using CorPITA. The radio sources' positions and velocity were calculated from NRH images and I-LOFAR dynamic spectra. The EUV wave expanded radially with a uniform velocity of $\sim$ 500 km s$^{-1}$. However, the radio source was observed to be deflected and appeared to move along a channel of a lower Alfvén speed, abruptly slowing from 1700 km s$^{-1}$ to 250 km s$^{-1}$ as it entered a quiet-Sun region. A shock wave with an apparent radial velocity of > 420 km s$^{-1}$ was determined from the drift rate of the associated Type II radio burst. The apparent motion of the radio source may have resulted from a wave front moving along a coronal wave guide or by different points along the wave front emitting at locations with favourable conditions for shock formation.

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GRB 201015A and the nature of low-luminosity soft gamma-ray bursts

GRB 201015A is a peculiarly low luminosity, spectrally soft gamma-ray burst (GRB), with $T_{\rm 90} = 9.8 \pm 3.5$ s (time interval of detection of 90\% of photons from the GRB), and an associated supernova (likely to be type Ic or Ic-BL). GRB 201015A has an isotropic energy $E_{γ,\rm iso} = 1.75 ^{+0.60} _{-0.53} \times 10^{50}$ erg, and photon index $Γ= 3.00 ^{+0.50} _{-0.42}$ (15-150 keV). It follows the Amati relation, a correlation between $E_{γ,\rm iso}$ and spectral peak energy $E_{\rm p}$ followed by long GRBs. It appears exceptionally soft based on $Γ$, the hardness ratio of HR = $0.47 \pm 0.24$, and low-$E_{\rm p}$, so we have compared it to other GRBs sharing these properties. These events can be explained by shock breakout, poorly collimated jets, and off-axis viewing. Follow-up observations of the afterglow taken in the X-ray, optical, and radio, reveal a surprisingly late flattening in the X-ray from $t = (2.61 \pm 1.27)\times 10^4$ s to $t = 1.67 ^{+1.14} _{-0.65} \times 10^6$ s. We fit the data to closure relations describing the synchrotron emission, finding the electron spectral index to be $p = 2.42 ^{+0.44} _{-0.30}$, and evidence of late-time energy injection with coefficient $q = 0.24 ^{+0.24} _{-0.18}$. The jet half opening angle lower limit ($θ_{j} \ge 16^{\circ}$) is inferred from the non-detection of a jet break. The launch of SVOM and Einstein Probe in 2023, should enable detection of more low luminosity events like this, providing a fuller picture of the variety of GRBs.

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Self-Supervised Clustering on Image-Subtracted Data with Deep-Embedded Self-Organizing Map

Developing an effective automatic classifier to separate genuine sources from artifacts is essential for transient follow-ups in wide-field optical surveys. The identification of transient detections from the subtraction artifacts after the image differencing process is a key step in such classifiers, known as real-bogus classification problem. We apply a self-supervised machine learning model, the deep-embedded self-organizing map (DESOM) to this "real-bogus" classification problem. DESOM combines an autoencoder and a self-organizing map to perform clustering in order to distinguish between real and bogus detections, based on their dimensionality-reduced representations. We use 32x32 normalized detection thumbnails as the input of DESOM. We demonstrate different model training approaches, and find that our best DESOM classifier shows a missed detection rate of 6.6% with a false positive rate of 1.5%. DESOM offers a more nuanced way to fine-tune the decision boundary identifying likely real detections when used in combination with other types of classifiers, for example built on neural networks or decision trees. We also discuss other potential usages of DESOM and its limitations.

cs.CV↗

Short Duration Accretion States of Polars as seen in TESS and ZTF data

Polars are highly magnetic cataclysmic variables which have been long observed to have both high and low brightness states. The duration of these states has been previously seen to vary from a number of days up to years. Despite this; these states and their physical origin has not been explained in a consistent manner. We present observations of the shortest duration states of a number of Polars observed by ZTF and TESS. This has allowed us to determine that short duration states are a relatively common feature across the population of Polars. Furthermore we have been able to generalise the model of star spot migration to explain both short lived high and low states in Polars by incorporating the interaction between the magnetic field of the white dwarf and that of the star spots.

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The OmegaWhite Survey for Short-Period Variable Stars VII: High amplitude, short period blue variables

Blue Large Amplitude Pulsators (BLAPs) are a relatively new class of blue variable stars showing periodic variations in their light curves with periods shorter than a few tens of mins and amplitudes of more than ten percent. We report nine blue variable stars identified in the OmegaWhite survey conducted using ESO's VST, which show a periodic modulation in the range 7-37 min and an amplitude in the range 0.11-0.28 mag. We have obtained a series of followup photometric and spectroscopic observations made primarily using SALT and telescopes at SAAO. We find four stars which we identify as BLAPs, one of which was previously known. One star, OW J0820--3301, appears to be a member of the V361 Hya class of pulsating stars and is spatially close to an extended nebula. One further star, OW J1819--2729, has characteristics similar to the sdAV pulsators. In contrast, OW J0815--3421 is a binary star containing an sdB and a white dwarf with an orbital period of 73.7 min, making it only one of six white dwarf-sdB binaries with an orbital period shorter than 80 min. Finally, high cadence photometry of four of the candidate BLAPs show features which we compare with notch-like features seen in the much longer period Cepheid pulsators.

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The OmegaWhite Survey for Short Period Variable Stars VI. Open Clusters

Using light curves with $\sim$3 min cadence and a duration of 2 hrs made using the OmegaWhite survey, we present the results of a search for short-period variable stars in the field of 20 open clusters. We identified 92 variable stars in these fields. Using a range of cluster member catalogues and Gaia EDR3 data, we have determined that 10 are cluster members and 2 more are probable members. Based on their position on the Gaia HRD and their photometric periods, we find that most of these are $δ$ Sct stars. We obtained low-resolution optical spectroscopy of some of these cluster members and field stars. We discuss the cluster variable stars in the context of $δ$ Sct stars in other open clusters.

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The PLATO field selection process I. Identification and content of the long-pointing fields

PLATO (PLAnetary Transits and Oscillations of stars) is an ESA M-class satellite planned for launch by end 2026 and dedicated to the wide-field search of transiting planets around bright and nearby stars, with a strong focus on discovering habitable rocky planets hosted by solar-like stars. The choice of the fields to be pointed at is a crucial task since it has a direct impact on the scientific return of the mission. In this paper we describe and discuss the formal requirements and the key scientific prioritization criteria that have to be taken into account in the Long-duration Observation Phase (LOP) field selection, and apply a quantitative metric to guide us in this complex optimization process. We identify two provisional LOP fields, one for each hemisphere (LOPS1, LOPN1), and discuss their properties and stellar content. While additional fine-tuning shall be applied to LOP selection before the definitive choice (to be made two years before launch), we expect their position will not move by more than a few degrees with respect to what is proposed in this paper.

astro-ph.EP↗

The return of the spin period in DW Cnc and evidence of new high state outbursts

DW Cnc is an intermediate polar which has previously been observed in both high and low states. Observations of the high state of DW Cnc have previously revealed a spin period at ~ 38.6 min, however observations from the 2018/19 low state showed no evidence of the spin period. We present results from our analysis of 12 s cadence photometric data collected by NGTS of DW Cnc during the high state which began in 2019. Following the previously reported suppression of the spin period signal we identify the return of this signal during the high state, consistent with previous observations of it. We identify this as the restarting of accretion during the high state. We further identified three short outbursts lasting ~ 1 d in DW Cnc with a mean recurrence time of ~ 60 d and an amplitude of ~ 1 mag. These are the first outbursts identified in DW Cnc since 2008. Due to the short nature of these events we identify them not as a result of accretion instabilities but instead either from instabilities originating from the interaction of the magnetorotational instability in the accretion disc and the magnetic field generated by the white dwarf or the result of magnetic gating.

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The Gravitational-wave Optical Transient Observer (GOTO): prototype performance and prospects for transient science

The Gravitational-wave Optical Transient Observer (GOTO) is an array of wide-field optical telescopes, designed to exploit new discoveries from the next generation of gravitational wave detectors (LIGO, Virgo, KAGRA), study rapidly evolving transients, and exploit multi-messenger opportunities arising from neutrino and very high energy gamma-ray triggers. In addition to a rapid response mode, the array will also perform a sensitive, all-sky transient survey with few day cadence. The facility features a novel, modular design with multiple 40-cm wide-field reflectors on a single mount. In June 2017 the GOTO collaboration deployed the initial project prototype, with 4 telescope units, at the Roque de los Muchachos Observatory (ORM), La Palma, Canary Islands. Here we describe the deployment, commissioning, and performance of the prototype hardware, and discuss the impact of these findings on the final GOTO design. We also offer an initial assessment of the science prospects for the full GOTO facility that employs 32 telescope units across two sites.

astro-ph.IM↗

The all-sky PLATO input catalogue

Context. The ESA PLAnetary Transits and Oscillations of stars (PLATO) mission will search for terrestrial planets in the habitable zone of solar-type stars. Because of telemetry limitations, PLATO targets need to be pre-selected. Aims. In this paper, we present an all sky catalogue that will be fundamental to selecting the best PLATO fields and the most promising target stars, deriving their basic parameters, analysing the instrumental performances, and then planing and optimising follow-up observations. This catalogue also represents a valuable resource for the general definition of stellar samples optimised for the search of transiting planets. Methods. We used Gaia Data Release 2 (DR2) astrometry and photometry and 3D maps of the local interstellar medium to isolate FGK (V$\leq$13) and M (V$\leq$16) dwarfs and subgiant stars. Results. We present the first public release of the all-sky PLATO Input Catalogue (asPIC1.1) containing a total of 2 675 539 stars including 2 378 177 FGK dwarfs and subgiants and 297 362 M dwarfs. The median distance in our sample is 428 pc for FGK stars and 146 pc for M dwarfs, respectively. We derived the reddening of our targets and developed an algorithm to estimate stellar fundamental parameters (Teff, radius, mass) from astrometric and photometric measurements. Conclusions. We show that the overall (internal+external) uncertainties on the stellar parameter determined in the present study are $\sim$230 K (4%) for the effective temperatures, $\sim$0.1 R$_{\odot}$ (9%) for the stellar radii, and $\sim$0.1 M$_{\odot}$ (11%) for the stellar mass. We release a special target list containing all known planet hosts cross-matched with our catalogue.

astro-ph.EP↗

Searching For Fermi GRB Optical Counterparts With The Prototype Gravitational-Wave Optical Transient Observer (GOTO)

The typical detection rate of $\sim1$ gamma-ray burst (GRB) per day by the \emph{Fermi} Gamma-ray Burst Monitor (GBM) provides a valuable opportunity to further our understanding of GRB physics. However, the large uncertainty of the \emph{Fermi} localization typically prevents rapid identification of multi-wavelength counterparts. We report the follow-up of 93 \emph{Fermi} GRBs with the Gravitational-wave Optical Transient Observer (GOTO) prototype on La Palma. We selected 53 events (based on favourable observing conditions) for detailed analysis, and to demonstrate our strategy of searching for optical counterparts. We apply a filtering process consisting of both automated and manual steps to 60\,085 candidates initially, rejecting all but 29, arising from 15 events. With $\approx3$ GRB afterglows expected to be detectable with GOTO from our sample, most of the candidates are unlikely to be related to the GRBs. Since we did not have multiple observations for those candidates, we cannot confidently confirm the association between the transients and the GRBs. Our results show that GOTO can effectively search for GRB optical counterparts thanks to its large field of view of $\approx40$ square degrees and its depth of $\approx20$ mag. We also detail several methods to improve our overall performance for future follow-up programs of \emph{Fermi} GRBs.

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Light curve classification with recurrent neural networks for GOTO: dealing with imbalanced data

The advent of wide-field sky surveys has led to the growth of transient and variable source discoveries. The data deluge produced by these surveys has necessitated the use of machine learning (ML) and deep learning (DL) algorithms to sift through the vast incoming data stream. A problem that arises in real-world applications of learning algorithms for classification is imbalanced data, where a class of objects within the data is underrepresented, leading to a bias for over-represented classes in the ML and DL classifiers. We present a recurrent neural network (RNN) classifier that takes in photometric time-series data and additional contextual information (such as distance to nearby galaxies and on-sky position) to produce real-time classification of objects observed by the Gravitational-wave Optical Transient Observer (GOTO), and use an algorithm-level approach for handling imbalance with a focal loss function. The classifier is able to achieve an Area Under the Curve (AUC) score of 0.972 when using all available photometric observations to classify variable stars, supernovae, and active galactic nuclei. The RNN architecture allows us to classify incomplete light curves, and measure how performance improves as more observations are included. We also investigate the role that contextual information plays in producing reliable object classification.

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Processing GOTO data with the Rubin Observatory LSST Science Pipelines II: Forced Photometry and light curves

We have adapted the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) Science Pipelines to process data from the Gravitational-Wave Optical Transient Observer (GOTO) prototype. In this paper, we describe how we used the Rubin Observatory LSST Science Pipelines to conduct forced photometry measurements on nightly GOTO data. By comparing the photometry measurements of sources taken on multiple nights, we find that the precision of our photometry is typically better than 20~mmag for sources brighter than 16 mag. We also compare our photometry measurements against colour-corrected PanSTARRS photometry, and find that the two agree to within 10~mmag (1$σ$) for bright (i.e., $\sim14^{\rm th}$~mag) sources to 200~mmag for faint (i.e., $\sim18^{\rm th}$~mag) sources. Additionally, we compare our results to those obtained by GOTO's own in-house pipeline, {\sc gotophoto}, and obtain similar results. Based on repeatability measurements, we measure a $5σ$ L-band survey depth of between 19 and 20 magnitudes, depending on observing conditions. We assess, using repeated observations of non-varying standard SDSS stars, the accuracy of our uncertainties, which we find are typically overestimated by roughly a factor of two for bright sources (i.e., $<15^{\rm th}$~mag), but slightly underestimated (by roughly a factor of 1.25) for fainter sources ($>17^{\rm th}$~mag). Finally, we present lightcurves for a selection of variable sources, and compare them to those obtained with the Zwicky Transient Factory and GAIA. Despite the Rubin Observatory LSST Science Pipelines still undergoing active development, our results show that they are already delivering robust forced photometry measurements from GOTO data.

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Transient-optimised real-bogus classification with Bayesian Convolutional Neural Networks -- sifting the GOTO candidate stream

Large-scale sky surveys have played a transformative role in our understanding of astrophysical transients, only made possible by increasingly powerful machine learning-based filtering to accurately sift through the vast quantities of incoming data generated. In this paper, we present a new real-bogus classifier based on a Bayesian convolutional neural network that provides nuanced, uncertainty-aware classification of transient candidates in difference imaging, and demonstrate its application to the datastream from the GOTO wide-field optical survey. Not only are candidates assigned a well-calibrated probability of being real, but also an associated confidence that can be used to prioritise human vetting efforts and inform future model optimisation via active learning. To fully realise the potential of this architecture, we present a fully-automated training set generation method which requires no human labelling, incorporating a novel data-driven augmentation method to significantly improve the recovery of faint and nuclear transient sources. We achieve competitive classification accuracy (FPR and FNR both below 1%) compared against classifiers trained with fully human-labelled datasets, whilst being significantly quicker and less labour-intensive to build. This data-driven approach is uniquely scalable to the upcoming challenges and data needs of next-generation transient surveys. We make our data generation and model training codes available to the community.

astro-ph.IM↗

Evidence that short period AM CVn systems are diverse in outburst behaviour

We present results of our analysis of up to 15 years of photometric data from eight AM CVn systems with orbital periods between 22.5 and 26.8 min. Our data has been collected from the GOTO, ZTF, Pan-STARRS, ASAS-SN and Catalina all-sky surveys and amateur observations collated by the AAVSO. We find evidence that these interacting ultra-compact binaries show a similar diversity of long term optical properties as the hydrogen accreting dwarf novae. We found that AM CVn systems in the previously identified accretion disc instability region are not a homogenous group. Various members of the analysed sample exhibit behaviour reminiscent of Z Cam systems with long super outbursts and standstills, SU UMa systems with regular, shorter super outbursts, and nova-like systems which appear only in a high state. The addition of TESS full frame images of one of these systems, KL Dra, reveals the first evidence for normal outbursts appearing as a precursor to super outbursts in an AM CVn system. Our results will inform theoretical modelling of the outbursts of hydrogen deficient systems.

astro-ph.SR↗

Processing GOTO data with the Rubin Observatory LSST Science Pipelines I : Production of coadded frames

The past few decades have seen the burgeoning of wide field, high cadence surveys, the most formidable of which will be the Legacy Survey of Space and Time (LSST) to be conducted by the Vera C. Rubin Observatory. So new is the field of systematic time-domain survey astronomy, however, that major scientific insights will continue to be obtained using smaller, more flexible systems than the LSST. One such example is the Gravitational-wave Optical Transient Observer (GOTO), whose primary science objective is the optical follow-up of Gravitational Wave events. The amount and rate of data production by GOTO and other wide-area, high-cadence surveys presents a significant challenge to data processing pipelines which need to operate in near real-time to fully exploit the time-domain. In this study, we adapt the Rubin Observatory LSST Science Pipelines to process GOTO data, thereby exploring the feasibility of using this "off-the-shelf" pipeline to process data from other wide-area, high-cadence surveys. In this paper, we describe how we use the LSST Science Pipelines to process raw GOTO frames to ultimately produce calibrated coadded images and photometric source catalogues. After comparing the measured astrometry and photometry to those of matched sources from PanSTARRS DR1, we find that measured source positions are typically accurate to sub-pixel levels, and that measured L-band photometries are accurate to $\sim50$ mmag at $m_L\sim16$ and $\sim200$ mmag at $m_L\sim18$. These values compare favourably to those obtained using GOTO's primary, in-house pipeline, GOTOPHOTO, in spite of both pipelines having undergone further development and improvement beyond the implementations used in this study. Finally, we release a generic "obs package" that others can build-upon should they wish to use the LSST Science Pipelines to process data from other facilities.

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