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 $E_{\gamma,\rm iso} = 1.75 ^{+0.60} _{-0.53} \times 10^{50}$ erg, and photon index $\Gamma = 3.00 ^{+0.50} _{-0.42}$ (15-150 keV). It follows the Amati relation, a correlation between $E_{\gamma,\rm iso}$ and spectral peak energy $E_{\rm p}$ followed by long GRBs. It appears exceptionally soft based on $\Gamma$, 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 ($\theta_{j} \ge 16^{\circ}$) 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.Comment: 15 pages, 4 figure

The Gravitational-wave Optical Transient Observer (GOTO)

The Gravitational-wave Optical Transient Observer (GOTO) is a wide-field telescope project focused on detecting optical counterparts to gravitational-wave sources. GOTO uses arrays of 40 cm unit telescopes (UTs) on a shared robotic mount, which scales to provide large fields of view in a cost-effective manner. A complete GOTO mount uses 8 unit telescopes to give an overall field of view of 40 square degrees, and can reach a depth of 20th magnitude in three minutes. The GOTO-4 prototype was inaugurated with 4 unit telescopes in 2017 on La Palma, and was upgraded to a full 8-telescope array in 2020. A second 8-UT mount will be installed on La Palma in early 2021, and another GOTO node with two more mount systems is planned for a southern site in Australia. When complete, each mount will be networked to form a robotic, dual-hemisphere observatory, which will survey the entire visible sky every few nights and enable rapid follow-up detections of transient sources

The Gravitational-wave Optical Transient Observer (GOTO)

The Gravitational-wave Optical Transient Observer (GOTO) is a wide-field telescope project focused on detecting optical counterparts to gravitational-wave sources. Each GOTO robotic mount holds eight 40 cm telescopes, giving an overall field of view of 40 square degrees. As of 2022 the first two GOTO mounts have been commissioned at the Roque de los Muchachos Observatory on La Palma, Canary Islands, and construction of the second node with two additional 8-telescope mounts has begin at Siding Spring Observatory in New South Wales, Australia. Once fully operational each GOTO mount will be networked to form a robotic, multi-site observatory, which will survey the entire visible sky every two nights and enable rapid follow-up detections of transient sources

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

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 T90 = 9.8 ± 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_{\gamma , \rm iso}$$= 1.75 ^{+0.60} _{-0.53} \times 10^{50}$ erg, and photon index $\Gamma = 3.00 ^{+0.50} _{-0.42}$ (15–150 keV). It follows the Amati relation, a correlation between $E_{\gamma , \rm iso}$ and spectral peak energy Ep followed by long GRBs. It appears exceptionally soft based on Γ, the hardness ratio of HR = 0.47 ± 0.24, and low-Ep, 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 ± 1.27) × 104 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 (θj ≥ 16○) 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

The GRANDMA network in preparation for the fourth gravitational-wave observing run

GRANDMA is a world-wide collaboration with the primary scientific goal ofstudying gravitational-wave sources, discovering their electromagneticcounterparts and characterizing their emission. GRANDMA involves astronomers,astrophysicists, gravitational-wave physicists, and theorists. GRANDMA is now atruly global network of telescopes, with (so far) 30 telescopes in bothhemispheres. It incorporates a citizen science programme (Kilonova-Catcher)which constitutes an opportunity to spread the interest in time-domainastronomy. The telescope network is an heterogeneous set of already-existingobserving facilities that operate coordinated as a single observatory. Withinthe network there are wide-field imagers that can observe large areas of thesky to search for optical counterparts, narrow-field instruments that dotargeted searches within a predefined list of host-galaxy candidates, andlarger telescopes that are devoted to characterization and follow-up of theidentified counterparts. Here we present an overview of GRANDMA after the thirdobserving run of the LIGO/VIRGO gravitational-wave observatories in $2019-2020$and its ongoing preparation for the forthcoming fourth observational campaign(O4). Additionally, we review the potential of GRANDMA for the discovery andfollow-up of other types of astronomical transients.<br

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

Ready for O4 II: GRANDMA Observations of Swift GRBs during eight-weeks of Spring 2022

We present a campaign designed to train the GRANDMA network and its infrastructure to follow up on transient alerts and detect their early afterglows. In preparation for O4 II campaign, we focused on GRB alerts as they are expected to be an electromagnetic counterpart of gravitational-wave events. Our goal was to improve our response to the alerts and start prompt observations as soon as possible to better prepare the GRANDMA network for the fourth observational run of LIGO-Virgo-Kagra (which started at the end of May 2023), and future missions such as SM. To receive, manage and send out observational plans to our partner telescopes we set up dedicated infrastructure and a rota of follow-up adcates were organized to guarantee round-the-clock assistance to our telescope teams. To ensure a great number of observations, we focused on Swift GRBs whose localization errors were generally smaller than the GRANDMA telescopes' field of view. This allowed us to bypass the transient identification process and focus on the reaction time and efficiency of the network. During 'Ready for O4 II', 11 Swift/INTEGRAL GRB triggers were selected, nine fields had been observed, and three afterglows were detected (GRB 220403B, GRB 220427A, GRB 220514A), with 17 GRANDMA telescopes and 17 amateur astronomers from the citizen science project Kilonova-Catcher. Here we highlight the GRB 220427A analysis where our long-term follow-up of the host galaxy allowed us to obtain a photometric redshift of $z=0.82\pm0.09$, its lightcurve elution, fit the decay slope of the afterglows, and study the properties of the host galaxy

GRANDMA and HXMT Observations of GRB 221009A -- the Standard-Luminosity Afterglow of a Hyper-Luminous Gamma-Ray Burst

GRB 221009A is the brightest Gamma-Ray Burst (GRB) detected in more than 50 years of study. In this paper, we present observations in the X-ray and optical domains after the GRB obtained by the GRANDMA Collaboration (which includes observations from more than 30 professional and amateur telescopes) and the Insight-HXMT Collaboration. We study the optical afterglow with empirical fitting from GRANDMA+HXMT data, augmented with data from the literature up to 60 days. We then model numerically, using a Bayesian approach, the GRANDMA and HXMT-LE afterglow observations, that we augment with Swift-XRT and additional optical/NIR observations reported in the literature. We find that the GRB afterglow, extinguished by a large dust column, is most likely behind a combination of a large Milky-Way dust column combined with moderate low-metallicity dust in the host galaxy. Using the GRANDMA+HXMT-LE+XRT dataset, we find that the simplest model, where the observed afterglow is produced by synchrotron radiation at the forward external shock during the deceleration of a top-hat relativistic jet by a uniform medium, fits the multi-wavelength observations only moderately well, with a tension between the observed temporal and spectral evolution. This tension is confirmed when using the extended dataset. We find that the consideration of a jet structure (Gaussian or power-law), the inclusion of synchrotron self-Compton emission, or the presence of an underlying supernova do not improve the predictions, showing that the modelling of GRB22109A will require going beyond the most standard GRB afterglow model. Placed in the global context of GRB optical afterglows, we find the afterglow of GRB 221009A is luminous but not extraordinarily so, highlighting that some aspects of this GRB do not deviate from the global known sample despite its extreme energetics and the peculiar afterglow evolution.Comment: Accepted to ApJL for the special issue, 37 pages, 23 pages main text, 6 tables, 13 figure

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 overrepresented 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, 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