Stellar multiplicity: an interdisciplinary nexus

Our uncertainties about binary star systems (and triples and so on) limit our capabilities in literally every single one of the Thematic Areas identified for Astro2020. We need to understand the population statistics of stellar multiplicity and their variations with stellar type, chemistry, and dynamical environment: Correct interpretation of any exoplanet experiment depends on proper treatment of resolved and unresolved binaries; stellar multiplicity is a direct outcome of star and companion formation; the most precise constraints on stellar structure come from well-characterized binary systems; stellar populations heavily rely on stellar and binary evolution modeling; high-redshift galaxy radiation and reionization is controlled by binary-dependent stellar physics; compact objects are the outcomes of binary evolution; the interpretation of multi-messenger astronomy from gravitational waves, light, and neutrinos relies on understanding the products of binary star evolution; near-Universe constraints on the Hubble constant with Type Ia supernovae and gravitational-wave mergers are subject to systematics related to their binary star progenitors; local measures of dark-matter substructure masses are distorted by binary populations. In order to realize the scientific goals in each of these themes over the next decade, we therefore need to understand how binary stars and stellar multiplets are formed and distributed in the space of masses, composition, age, and orbital properties, and how the distribution evolves with time. This white paper emphasizes the interdisciplinary importance of binary-star science and advocates that coordinated investment from all astrophysical communities will benefit almost all branches of astrophysics.Comment: Submitted to the Astro2020 Decadal Survey White Paper cal

Chandra Observations of Candidate Subparsec Binary Supermassive Black Holes

We present analysis of Chandra X-ray observations of seven quasars that were identified as candidate subparsec binary supermassive black hole (SMBH) systems in the Catalina Real-Time Transient Survey based on the apparent periodicity in their optical light curves. Simulations predict that close-separation accreting SMBH binaries will have different X-ray spectra than single accreting SMBHs, including harder or softer X-ray spectra, ripple-like profiles in the Fe K-α line, and distinct peaks in the spectrum due to the separation of the accretion disk into a circumbinary disk and mini disks around each SMBH. We obtained Chandra observations to test these models and assess whether these quasars could contain binary SMBHs. We instead find that the quasar spectra are all well fit by simple absorbed power-law models, with the rest-frame 2–10 keV photon indices, Γ, and the X-ray-to-optical power slopes, α_(OX), indistinguishable from those of the larger quasar population. This may indicate that these seven quasars are not truly subparsec binary SMBH systems, or it may simply reflect that our sample size was too small to robustly detect any differences. Alternatively, the X-ray spectral changes might only be evident at energies higher than probed by Chandra. Given the available models and current data, no firm conclusions are drawn. These observations will help motivate and direct further work on theoretical models of binary SMBH systems, such as modeling systems with thinner accretion disks and larger binary separations

Deep-Manager: a versatile tool for optimal feature selection in live-cell imaging analysis

One of the major problems in bioimaging, often highly underestimated, is whether features extracted for a discrimination or regression task will remain valid for a broader set of similar experiments or in the presence of unpredictable perturbations during the image acquisition process. Such an issue is even more important when it is addressed in the context of deep learning features due to the lack of a priori known relationship between the black-box descriptors (deep features) and the phenotypic properties of the biological entities under study. In this regard, the widespread use of descriptors, such as those coming from pre-trained Convolutional Neural Networks (CNNs), is hindered by the fact that they are devoid of apparent physical meaning and strongly subjected to unspecific biases, i.e., features that do not depend on the cell phenotypes, but rather on acquisition artifacts, such as brightness or texture changes, focus shifts, autofluorescence or photobleaching. The proposed Deep-Manager software platform offers the possibility to efficiently select those features having lower sensitivity to unspecific disturbances and, at the same time, a high discriminating power. Deep-Manager can be used in the context of both handcrafted and deep features. The unprecedented performances of the method are proven using five different case studies, ranging from selecting handcrafted green fluorescence protein intensity features in chemotherapy-related breast cancer cell death investigation to addressing problems related to the context of Deep Transfer Learning. Deep-Manager, freely available at https://github.com/BEEuniroma2/Deep-Manager, is suitable for use in many fields of bioimaging and is conceived to be constantly upgraded with novel image acquisition perturbations and modalities

Chandra Observations of Candidate Subparsec Binary Supermassive Black Holes

We present analysis of Chandra X-ray observations of seven quasars that were identified as candidate subparsec binary supermassive black hole (SMBH) systems in the Catalina Real-Time Transient Survey based on the apparent periodicity in their optical light curves. Simulations predict that close-separation accreting SMBH binaries will have different X-ray spectra than single accreting SMBHs, including harder or softer X-ray spectra, ripple-like profiles in the Fe K-α line, and distinct peaks in the spectrum due to the separation of the accretion disk into a circumbinary disk and mini disks around each SMBH. We obtained Chandra observations to test these models and assess whether these quasars could contain binary SMBHs. We instead find that the quasar spectra are all well fit by simple absorbed power-law models, with the rest-frame 2–10 keV photon indices, Γ, and the X-ray-to-optical power slopes, α_(OX), indistinguishable from those of the larger quasar population. This may indicate that these seven quasars are not truly subparsec binary SMBH systems, or it may simply reflect that our sample size was too small to robustly detect any differences. Alternatively, the X-ray spectral changes might only be evident at energies higher than probed by Chandra. Given the available models and current data, no firm conclusions are drawn. These observations will help motivate and direct further work on theoretical models of binary SMBH systems, such as modeling systems with thinner accretion disks and larger binary separations

NuSTAR Observations of Candidate Subparsec Supermassive Black Holes

We present analysis of NuSTAR X-ray observations of three AGN that were identified as candidate subparsec binary supermassive black hole (SMBH) systems in the Catalina Real-Time Transient Survey based on apparent periodicity in their optical light curves. Simulations predict that close-separation accreting SMBH binaries will have different X-ray spectra than single accreting SMBHs. We previously observed these AGN with Chandra and found no differences between their low energy X-ray properties and the larger AGN population. However some models predict differences to be more prominent at energies higher than probed by Chandra. We find that even at the higher energies probed by NuSTAR, the spectra of these AGN are indistinguishable from the larger AGN population. This could rule out models predicting large differences in the X-ray spectra in the NuSTAR bands. Alternatively, it might mean that these three AGN are not binary SMBHs