Transient Classifiers for Fink: Benchmarks for LSST

International audienceThe upcoming Legacy Survey of Space and Time (LSST) at the Vera Rubin Observatory is expected to detect a few million transients per night, which will generate a live alert stream during the entire 10 years of the survey. This will be distributed via community brokers whose task is to select subsets of the stream and direct them to scientific communities. Given the volume and complexity of data, machine learning (ML) algorithms will be paramount for this task. We present the infrastructure tests and classification methods developed within the {\sc Fink} broker in preparation for LSST. This work aims to provide detailed information regarding the underlying assumptions, and methods, behind each classifier, enabling users to make informed follow-up decisions from {\sc Fink} photometric classifications. Using simulated data from the Extended LSST Astronomical Time-series Classification Challenge (ELAsTiCC), we showcase the performance of binary and multi-class ML classifiers available in {\sc Fink}. These include tree-based classifiers coupled with tailored feature extraction strategies, as well as deep learning algorithms. We introduce the CBPF Alert Transient Search (CATS), a deep learning architecture specifically designed for this task. Results show that {\sc Fink} classifiers are able to handle the extra complexity which is expected from LSST data. CATS achieved $97\%$ accuracy on a multi-class classification while our best performing binary classifier achieve $99\%$ when classifying the Periodic class. ELAsTiCC was an important milestone in preparing {\sc Fink} infrastructure to deal with LSST-like data. Our results demonstrate that {\sc Fink} classifiers are well prepared for the arrival of the new stream; this experience also highlights that transitioning from current infrastructures to Rubin will require significant adaptation of currently available tools

Transient Classifiers for Fink: Benchmarks for LSST

International audienceThe upcoming Legacy Survey of Space and Time (LSST) at the Vera Rubin Observatory is expected to detect a few million transients per night, which will generate a live alert stream during the entire 10 years of the survey. This will be distributed via community brokers whose task is to select subsets of the stream and direct them to scientific communities. Given the volume and complexity of data, machine learning (ML) algorithms will be paramount for this task. We present the infrastructure tests and classification methods developed within the {\sc Fink} broker in preparation for LSST. This work aims to provide detailed information regarding the underlying assumptions, and methods, behind each classifier, enabling users to make informed follow-up decisions from {\sc Fink} photometric classifications. Using simulated data from the Extended LSST Astronomical Time-series Classification Challenge (ELAsTiCC), we showcase the performance of binary and multi-class ML classifiers available in {\sc Fink}. These include tree-based classifiers coupled with tailored feature extraction strategies, as well as deep learning algorithms. We introduce the CBPF Alert Transient Search (CATS), a deep learning architecture specifically designed for this task. Results show that {\sc Fink} classifiers are able to handle the extra complexity which is expected from LSST data. CATS achieved $97\%$ accuracy on a multi-class classification while our best performing binary classifier achieve $99\%$ when classifying the Periodic class. ELAsTiCC was an important milestone in preparing {\sc Fink} infrastructure to deal with LSST-like data. Our results demonstrate that {\sc Fink} classifiers are well prepared for the arrival of the new stream; this experience also highlights that transitioning from current infrastructures to Rubin will require significant adaptation of currently available tools

Transient Classifiers for Fink: Benchmarks for LSST

International audienceThe upcoming Legacy Survey of Space and Time (LSST) at the Vera Rubin Observatory is expected to detect a few million transients per night, which will generate a live alert stream during the entire 10 years of the survey. This will be distributed via community brokers whose task is to select subsets of the stream and direct them to scientific communities. Given the volume and complexity of data, machine learning (ML) algorithms will be paramount for this task. We present the infrastructure tests and classification methods developed within the {\sc Fink} broker in preparation for LSST. This work aims to provide detailed information regarding the underlying assumptions, and methods, behind each classifier, enabling users to make informed follow-up decisions from {\sc Fink} photometric classifications. Using simulated data from the Extended LSST Astronomical Time-series Classification Challenge (ELAsTiCC), we showcase the performance of binary and multi-class ML classifiers available in {\sc Fink}. These include tree-based classifiers coupled with tailored feature extraction strategies, as well as deep learning algorithms. We introduce the CBPF Alert Transient Search (CATS), a deep learning architecture specifically designed for this task. Results show that {\sc Fink} classifiers are able to handle the extra complexity which is expected from LSST data. CATS achieved $97\%$ accuracy on a multi-class classification while our best performing binary classifier achieve $99\%$ when classifying the Periodic class. ELAsTiCC was an important milestone in preparing {\sc Fink} infrastructure to deal with LSST-like data. Our results demonstrate that {\sc Fink} classifiers are well prepared for the arrival of the new stream; this experience also highlights that transitioning from current infrastructures to Rubin will require significant adaptation of currently available tools

Designing an Optimal Kilonova Search using DECam for Gravitational Wave Events

International audienceWe address the problem of optimally identifying all kilonovae detected via gravitational wave emission in the upcoming LIGO/Virgo/KAGRA Collaboration observing run, O4, which is expected to be sensitive to a factor of $\sim 7$ more Binary Neutron Stars alerts than previously. Electromagnetic follow-up of all but the brightest of these new events will require $>1$ meter telescopes, for which limited time is available. We present an optimized observing strategy for the Dark Energy Camera during O4. We base our study on simulations of gravitational wave events expected for O4 and wide-prior kilonova simulations. We derive the detectabilities of events for realistic observing conditions. We optimize our strategy for confirming a kilonova while minimizing telescope time. For a wide range of kilonova parameters, corresponding to a fainter kilonova compared to GW170817/AT2017gfo we find that, with this optimal strategy, the discovery probability for electromagnetic counterparts with the Dark Energy Camera is $\sim 80\%$ at the nominal binary neutron star gravitational wave detection limit for the next LVK observing run (190 Mpc), which corresponds to a $\sim 30\%$ improvement compared to the strategy adopted during the previous observing run. For more distant events ($\sim 330$ Mpc), we reach a $\sim 60\%$ probability of detection, a factor of $\sim 2$ increase. For a brighter kilonova model dominated by the blue component that reproduces the observations of GW170817/AT2017gfo, we find that we can reach $\sim 90\%$ probability of detection out to 330 Mpc, representing an increase of $\sim 20 \%$, while also reducing the total telescope time required to follow-up events by $\sim 20\%$