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Compact binary mergers detectable in gravitational waves can be accompanied by a kilonova, an electromagnetic transient powered by radioactive decay of newly synthesised r-process elements. A few kilonova candidates have been observed during short gamma-ray burst follow-up, and one found associated with a gravitational wave detection, GW170817. However, robust kilonova candidates are yet to be found in un-triggered, wide-field optical surveys, that is, a search not requiring an initial gravitational wave or gamma-ray burst trigger. Here we present the first observing run for the Kilonova and Transients Program (KNTraP) using the Dark Energy Camera. The first KNTraP run ran for 11 nights, covering 31 fields at a nightly cadence in two filters. The program can detect transients beyond the LIGO/Virgo/KAGRA horizon, be agnostic to the merger orientation, avoid the Sun and/or Galactic plane, and produces high cadence multi-wavelength light curves. The data were processed nightly in real-time for rapid identification of transient candidates, allowing for follow-up of interesting candidates before they faded away. Three fast-rising candidates were identified in real-time, however none had the characteristics of the kilonova AT2017gfo associated with GW170817 or with the expected evolution for kilonovae from our fade-rate models. After the run, the data were reprocessed, then subjected to stringent filtering and model fitting to search for kilonovae offline. Multiple KNTraP runs (3+) are expected to detect kilonovae via this optical-only search method. No kilonovae were detected in this first KNTraP run using our selection criteria, constraining the KN rate to $R < 1.8\times10^{5}$ Gpc$^{-3}$ yr$^{-1}$., Comment: 17 pages, 6 figures, 3 tables, submitted to MNRAS

Current and future gravitational-wave observatories rely on large-scale, precision interferometers to detect the gravitational-wave signals. However, microscopic imperfections on the test masses, known as point absorbers, cause problematic heating of the optic via absorption of the high-power laser beam, which results in diminished sensitivity, lock loss, or even permanent damage. Consistent monitoring of the test masses is crucial for detecting, characterizing, and ultimately removing point absorbers. We present a machine-learning algorithm for detecting point absorbers based on the object-detection algorithm You Only Look Once (YOLO). The algorithm can perform this task in situ while the detector is in operation. We validate our algorithm by comparing it with past reports of point absorbers identified by humans at LIGO. The algorithm confidently identifies the same point absorbers as humans with minimal false positives. It also identifies some point absorbers previously not identified by humans, which we confirm with human follow-up. We highlight the potential of machine learning in commissioning efforts.

GRB$\,$220831A is a gamma-ray burst (GRB) with a duration and spectral peak energy that places it at the interface between the distribution of long-soft and short-hard GRBs. In this paper, we present the multi-wavelength follow-up campaign to GRB$\,$220831A and its optical, near-infrared, X-ray and radio counterparts. Our deep optical and near-infrared observations do not reveal an underlying host galaxy, and establish that GRB$\,$220831A is observationally hostless to depth, $m_i\gtrsim26.6$ AB mag. Based on the Amati relation and the non-detection of an accompanying supernova, we find that this GRB is most likely to have originated from a collapsar at $z>2$, but it could also possibly be a compact object merger at $z<0.4$ with a large separation distance from its host galaxy. Regardless of its origin, we show that its optical and near-infrared counterpart departs from the evolution expected from a forward shock dominated synchrotron afterglow, exhibiting a steep post-break temporal powerlaw index of $-3.83^{+0.62}_{-0.79}$, too steep to be the jet-break. By analysing a range of models, we find that the observed steep departure from forward shock closure relations is likely due to an internal process producing either a flare or a plateau., Comment: 17 pages, 14 figures, 7 tables, submitted to MNRAS

Upgrades to existing gravitational-wave observatories have the potential to simultaneously constrain the nuclear equation of state and Hubble's constant $H_0$ to percent level with merging neutron star binaries. In practice, performing simultaneous inference of $H_0$ and the equation of state is limited computationally by the requirement to solve the equations of general-relativistic hydrostatic equilibrium millions of times. We develop a machine-learning model to solve the Tolman-Oppenheimer-Volkoff equations in less than a millisecond, and demonstrate its utility by performing direct inference of both equation of state and Hubble's constant for synthetic neutron star merger signals with LIGO-Virgo-KAGRA operating at A+ sensitivities. We show that a population of fifteen mergers observed with A+ allows for the radius of a $1.4\,M_{\odot}$ neutron star and $H_0$ to be constrained to $R_{1.4} = 11.74^{+0.35}_{-0.28}$ km and $H_0 = 68^{+17}_{-13} \rm \ km \ s^{-1} \ Mpc^{-1}$, at 90% credible interval and 68% credible interval respectively. These constraints utilise only the gravitational-wave information to infer cosmological parameters; such numbers will be further improved with the addition of electromagnetic counterparts and/or galaxy catalogues., Comment: 14 pages, 9 figures, submitted to PRD

The relativistic outflows that produce Long GRBs (LGRBs) can be described by a structured jet model where prompt $\gamma$-ray emission is restricted to a narrow region in the jet's core. Viewing the jet off-axis from the core, a population of afterglows without an associated GRB detection can be predicted. In this work, we conduct an archival search for these `orphan' afterglows (OAs) with minute-cadence, deep ($g\sim23$) data from the Dark Energy Camera (DECam) taken as part of the Deeper, Wider, Faster programme (DWF). We introduce a method to select fast-evolving OA candidates within DWF data that comprises a machine learning model, based on a realistic synthetic population of OAs. Using this classifier, we recover 51 OA candidates. Of these candidates, 42 are likely flare events from M-class stars. The remaining nine possess quiescent, coincident sources in archival data with angular profiles consistent with a star and are inconsistent with the expected population of LGRB host galaxies. We therefore conclude that these are likely Galactic events. We calculate an upper limit on the rate of OAs down to $g<22$ AB mag of 7.46\,deg$^{-2}$yr$^{-1}$ using our criteria and constrain possible jet structures. We also place an upper limit of the characteristic angle between the $\gamma$-ray emitting region and the jet's half opening angle. For a smooth power-law and a power-law with core jet model respectively, these values are $58.3^{\circ}$ and $56.6^{\circ}$, for a power-law index of 0.8 and $75.3^{\circ}$ and $76.8^{\circ}$ for a power-law index of 1.2., Comment: 17 pages, 13 figures, accepted for publication in MNRAS

With the volume and availability of astronomical data growing rapidly, astronomers will soon rely on the use of machine learning algorithms in their daily work. This proceeding aims to give an overview of what machine learning is and delve into the many different types of learning algorithms and examine two astronomical use cases. Machine learning has opened a world of possibilities for us astronomers working with large amounts of data, however if not careful, users can trip into common pitfalls. Here we'll focus on solving problems related to time-series light curve data and optical imaging data mainly from the Deeper, Wider, Faster Program (DWF). Alongside the written examples, online notebooks will be provided to demonstrate these different techniques. This guide aims to help you build a small toolkit of knowledge and tools to take back with you for use on your own future machine learning projects., Comment: 12 pages, 5 Figures, International Astronomical Union Proceedings Series 368

Tidal disruption events (TDEs) are bursts of electromagnetic energy released when supermassive black holes (SMBHs) at the centers of galaxies violently disrupt a star that passes too close. TDEs provide a new window to study accretion onto SMBHs; in some rare cases, this accretion leads to launching of a relativistic jet, but the necessary conditions are not fully understood. The best studied jetted TDE to date is Swift J1644+57, which was discovered in gamma-rays, but was too obscured by dust to be seen at optical wavelengths. Here we report the optical discovery of AT2022cmc, a rapidly fading source at cosmological distance (redshift z=1.19325) whose unique lightcurve transitioned into a luminous plateau within days. Observations of a bright counterpart at other wavelengths, including X-rays, sub-millimeter, and radio, supports the interpretation of AT2022cmc as a jetted TDE containing a synchrotron "afterglow", likely launched by a SMBH with spin $a \gtrsim 0.3$. Using 4 years of Zwicky Transient Facility (ZTF) survey data, we calculate a rate of $0.02 ^{+ 0.04 }_{- 0.01 }$ Gpc$^{-3}$ yr$^{-1}$ for on-axis jetted TDEs based on the luminous, fast-fading red component, thus providing a measurement complementary to the rates derived from X-ray and radio observations. Correcting for the beaming angle effects, this rate confirms that about 1% of TDEs have relativistic jets. Optical surveys can use AT2022cmc as a prototype to unveil a population of jetted TDEs., Comment: Accepted for publication in Nature

The current data acquisition rate of astronomical transient surveys and the promise for significantly higher rates during in the next decade necessitate the development of novel approaches to analyze astronomical data sets and promptly detect objects of interest. The Deeper, Wider, Faster (DWF) program is a survey focused on the identification of fast evolving transients, such as fast radio bursts, gamma-ray bursts, and supernova shock breakouts. It employs a multi-frequency simultaneous coverage of the same part of the sky over several orders of magnitude. Using the Dark Energy Camera mounted on the 4-meter Blanco telescope, DWF captures a 20 second g-band exposure every minute, at a typical seeing of ~ 1" and an airmass of ~ 1.5. These optical data are collected simultaneously with observations conducted over the entire electromagnetic spectrum - from radio to gamma-rays - as well as cosmic ray observations. In this paper, we present a novel real-time light curve analysis algorithm, designed to detect transients in the DWF optical data; this algorithm functions independently from, or in conjunction with, image subtraction. We present a sample of fast transients detected by our algorithm, as well as a false-positive analysis. Our algorithm is customizable and can be tuned to be sensitive to transients evolving over different timescales and flux ranges., Comment: AJ accepted, 19 pages, 11 figures

We present our 500 pc distance-limited study of stellar fares using the Dark Energy Camera as part of the Deeper, Wider, Faster Program. The data was collected via continuous 20-second cadence g band imaging and we identify 19,914 sources with precise distances from Gaia DR2 within twelve, ~3 square-degree, fields over a range of Galactic latitudes. An average of ~74 minutes is spent on each field per visit. All light curves were accessed through a novel unsupervised machine learning technique designed for anomaly detection. We identify 96 flare events occurring across 80 stars, the majority of which are M dwarfs. Integrated are energies range from $\sim 10^{31}-10^{37}$ erg, with a proportional relationship existing between increased are energy with increased distance from the Galactic plane, representative of stellar age leading to declining yet more energetic are events. In agreement with previous studies we observe an increase in flaring fraction from M0 -> M6 spectral types. Furthermore, we find a decrease in the flaring fraction of stars as vertical distance from the galactic plane is increased, with a steep decline present around ~100 pc. We find that ~70% of identified flares occur on short timescales of ~8 minutes. Finally we present our associated are rates, finding a volumetric rate of $2.9 \pm 0.3 \times 10^{-6}$ flares pc$^{-3}$ hr$^{-1}$., Comment: 20 pages, 14 figures, 6 tables

Identification of anomalous light curves within time-domain surveys is often challenging. In addition, with the growing number of wide-field surveys and the volume of data produced exceeding astronomers ability for manual evaluation, outlier and anomaly detection is becoming vital for transient science. We present an unsupervised method for transient discovery using a clustering technique and the Astronomaly package. As proof of concept, we evaluate 85553 minute-cadenced light curves collected over two 1.5 hour periods as part of the Deeper, Wider, Faster program, using two different telescope dithering strategies. By combining the clustering technique HDBSCAN with the isolation forest anomaly detection algorithm via the visual interface of Astronomaly, we are able to rapidly isolate anomalous sources for further analysis. We successfully recover the known variable sources, across a range of catalogues from within the fields, and find a further 7 uncatalogued variables and two stellar flare events, including a rarely observed ultra fast flare (5 minute) from a likely M-dwarf., Comment: Accepted 7 Aug 2020, 19 pages, 8 figures