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We present overall specifications and science goals for a new optical and near-infrared (350 - 1650 nm) instru- ment designed to greatly enlarge the current Search for Extraterrestrial Intelligence (SETI) phase space. The Pulsed All-sky Near-infrared Optical SETI (PANOSETI) observatory will be a dedicated SETI facility that aims to increase sky area searched, wavelengths covered, number of stellar systems observed, and duration of time monitored. This observatory will offer an “all-observable-sky” optical and wide-field near-infrared pulsed tech- nosignature and astrophysical transient search that is capable of surveying the entire northern hemisphere. The final implemented experiment will search for transient pulsed signals occurring between nanosecond to second time scales. The optical component will cover a solid angle 2.5 million times larger than current SETI targeted searches, while also increasing dwell time per source by a factor of 10,000. The PANOSETI instrument will be the first near-infrared wide-field SETI program ever conducted. The rapid technological advance of fast-response optical and near-infrared detector arrays (i.e., Multi-Pixel Photon Counting; MPPC) make this program now feasible. The PANOSETI instrument design uses innovative domes that house 100 Fresnel lenses, which will search concurrently over 8,000 square degrees for transient signals (see Maire et al. and Cosens et al., this conference). In this paper, we describe the overall instrumental specifications and science objectives for PANOSETI. 

Machine learning is becoming a popular tool to quantify galaxy morphologies and identify mergers. However, this technique relies on using an appropriate set of training data to be successful. By combining hydrodynamical simulations, synthetic observations, and convolutional neural networks (CNNs), we quantitatively assess how realistic simulated galaxy images must be in order to reliably classify mergers. Specifically, we compare the performance of CNNs trained with two types of galaxy images, stellar maps and dust-inclusive radiatively transferred images, each with three levels of observational realism: (1) no observational effects (idealized images), (2) realistic sky and point spread function (semirealistic images), and (3) insertion into a real sky image (fully realistic images). We find that networks trained on either idealized or semireal images have poor performance when applied to survey-realistic images. In contrast, networks trained on fully realistic images achieve 87.1 per cent classification performance. Importantly, the level of realism in the training images is much more important than whether the images included radiative transfer, or simply used the stellar maps ($87.1{{\ \rm per\ cent}}$ compared to $79.6{{\ \rm per\ cent}}$ accuracy, respectively). Therefore, one can avoid the large computational and storage cost of running radiative transfer with a relatively modest compromise in classification performance. Making photometry-based networks insensitive to colour incurs a very mild penalty to performance with survey-realistic data ($86.0{{\ \rm per\ cent}}$ with r-only compared to $87.1{{\ \rm per\ cent}}$ with gri). This result demonstrates that while colour can be exploited by colour-sensitive networks, it is not necessary to achieve high accuracy and so can be avoided if desired. We provide the public release of our statistical observational realism suite, RealSim, as a companion to this paper.