We report on the observation and measurement of astrometry, photometry, morphology, and activity of the interstellar object 3I/ATLAS, also designated C/2025 N1 (ATLAS), with the NSF-DOE Vera C. Rubin Observatory. The third interstellar object, comet 3I/ATLAS, was first discovered on UT 2025 July 1. Serendipitously, the Rubin Observatory collected imaging in the area of the sky inhabited by the object during regular commissioning activities. We successfully recovered object detections from Rubin visits spanning UT 2025 June 21 (10 days before discovery) to UT 2025 July 7. Facilitated by Rubin's high resolution and large aperture, we report on the detection of cometary activity as early as June 21st, and observe it throughout. We measure the location and magnitude of the object on 37 Rubin images in r, i, and z bands, with typical precision of about 20 mas (100 mas, systematic) and about 10 mmag, respectively. We use these to derive improved orbit solutions, and to show there is no detectable photometric variability on hourly timescales. We derive a V-band absolute magnitude of H_V = (13.7 +/- 0.2) mag, and an equivalent effective nucleus radius of around (5.6 +/- 0.7) km. These data represent the earliest observations of this object by a large (8-meter class) telescope reported to date, and illustrate the type of measurements (and discoveries) Rubin's Legacy Survey of Space and Time (LSST) will begin to provide once operational later this year.

The first detection of an optical counterpart to a gravitational wave signal revealed that collaborative efforts between instruments with different specializations provide a unique opportunity to acquire impactful multi-messenger data. We present results of such a joint search with the Dark Energy Camera (DECam) and Prime Focus Spectrograph (PFS) for the optical counterpart of the LIGO-Virgo-KAGRA event S250328ae, a binary black hole merger candidate of high significance detected at a distance of 511$\pm$82 Mpc and localized within an area of 3 (15) square degrees at 50% (90%) confidence. We observed the 90% confidence area with DECam and identified 36 high-confidence transient candidates after image processing, candidate selection, and candidate vetting. We observed with PFS to obtain optical spectra of DECam candidates, Swift-XRT candidates, and potential host galaxies of S250328ae. In total, 3897 targets were observed by seven pointings covering ~50% of the 90% confidence area. After template fitting and visual inspection, we identified 12 SNe, 159 QSOs, 2975 galaxies, and 131 stars. With the joint observations of DECam and PFS, we found variability in 12 SNe, 139 QSOs, 37 galaxies, and 2 stars. We do not identify any confident optical counterparts, though the association is not ruled out for three variable candidates that are not observed by PFS and 6 QSO candidates without clear variability if the optical counterpart of S250328ae is faint. Despite the lack of confident optical counterparts, this paper serves as a framework for future collaborations between wide-field imagers and multi-object spectrographs to maximize multi-messenger analyses.

Enhanced modeling of microlensing variations in light curves of strongly lensed quasars improves measurements of cosmological time delays, the Hubble Constant, and quasar structure. Traditional methods for modeling extra-galactic microlensing rely on computationally expensive magnification map generation. With large datasets expected from wide-field surveys like the Vera C. Rubin Legacy Survey of Space and Time, including thousands of lensed quasars and hundreds of multiply imaged supernovae, faster approaches become essential. We introduce a deep-learning model that is trained on pre-computed magnification maps covering the parameter space on a grid of k, g, and s. Our autoencoder creates a low-dimensional latent space representation of these maps, enabling efficient map generation. Quantifying the performance of magnification map generation from a low dimensional space is an essential step in the roadmap to develop neural network-based models that can replace traditional feed-forward simulation at much lower computational costs. We develop metrics to study various aspects of the autoencoder generated maps and show that the reconstruction is reliable. Even though we observe a mild loss of resolution in the generated maps, we find this effect to be smaller than the smoothing effect of convolving the original map with a source of a plausible size for its accretion disk in the red end of the optical spectrum and larger wavelengths and particularly one suitable for studying the Broad-Line Region of quasars. Used to generate large samples of on-demand magnification maps, our model can enable fast modeling of microlensing variability in lensed quasars and supernovae.

We present analyses of the early data from Rubin Observatory's Data Preview 1 (DP1) for the globular cluster 47 Tuc field. The DP1 dataset for 47 Tuc includes four nights of observations from the Rubin Commissioning Camera (LSSTComCam), covering multiple bands ($ugriy$). We address challenges of crowding in the inner region of the cluster and toward the SMC in DP1, and demonstrate improved star-galaxy separation by fitting fifth-degree polynomials to the stellar loci in color-color diagrams and applying multi-dimensional sigma clipping. We compile a catalog of 3,576 probable 47 Tuc member stars selected via a combination of isochrone, Gaia proper-motion, and color-color space matched filtering. We explore the sources of photometric scatter in the 47 Tuc color-color sequence, evaluating contributions from various potential sources, including differential extinction within the cluster. Finally, of the 72 well-characterized variables in the field, we recover five known variable stars, including three RR Lyrae and two eclipsing binaries, in the coadd-based object catalog, and identify 62 in the difference image-based object catalog. Although the DP1 lightcurves have sparse temporal sampling, they appear to follow the patterns of densely-sampled literature lightcurves well. Despite some data limitations for crowded-field stellar analysis, DP1 demonstrates the promising scientific potential for future LSST data releases.

The Euclid, Rubin/LSST and Roman (WFIRST) projects will undertake flagship optical/near-infrared surveys in the next decade. By mapping thousands of square degrees of sky and covering the electromagnetic spectrum between 0.3 and 2 microns with sub-arcsec resolution, these projects will detect several tens of billions of sources, enable a wide range of astrophysical investigations by the astronomical community and provide unprecedented constraints on the nature of dark energy and dark matter. The ultimate cosmological, astrophysical and time-domain science yield from these missions will require joint survey processing (JSP) functionality at the pixel level that is outside the scope of the individual survey projects. The JSP effort scoped here serves two high-level objectives: 1) provide precise concordance multi-wavelength images and catalogs over the entire sky area where these surveys overlap, which accounts for source confusion and mismatched isophotes, and 2) provide a science platform to analyze concordance images and catalogs to enable a wide range of astrophysical science goals to be formulated and addressed by the research community. For the cost of about 200WY, JSP will allow the U.S. (and international) astronomical community to manipulate the flagship data sets and undertake innovative science investigations ranging from solar system object characterization, exoplanet detections, nearby galaxy rotation rates and dark matter properties, to epoch of reionization studies. It will also allow for the ultimate constraints on cosmological parameters and the nature of dark energy, with far smaller uncertainties and a better handle on systematics than by any one survey alone.

We report our near-infrared (NIR) follow-up observations of the gravitational wave (GW) event S240422ed using the Subaru Telescope/MOIRCS. S240422ed was initially classified as a black hole-neutron star merger with $>$ 99% probability of electromagnetic wave emission. We started follow-up observations 7.8 hours after the event. Over two nights, we observed 206 nearby galaxies in $Y$ and $K_{\rm s}$ bands down to about 21.4 and 21.1 AB mag (3$\sigma$), respectively. The total completeness of our survey based on galaxy $B$-band luminosity is 22%. As a result of our observations, five candidate counterparts were identified. We show that properties of these five objects are not consistent with kilonova such as AT2017gfo. Four objects are consistent with known classes of transients such as supernovae or dwarf nova outbursts. On the other hand, the nature of the remaining one object, which shows a red color and rapid decline, remains unclear. Although later analyses of GW signal reclassified S240422ed as likely terrestrial noise, our NIR observations provide valuable lessons for future NIR surveys for GW sources. We demonstrate that deep NIR follow-up observations as presented in this work would effectively constrain the presence of red kilonova even at 200 Mpc distance. We also discuss the importance of deep and wide NIR reference images and of understanding the properties and frequency of Galactic transients.

The Vera C. Rubin Observatory will, over a period of 10 years, repeatedly survey the southern sky. To ensure that images generated by Rubin meet the quality requirements for precision science, the observatory will use an Active Optics System (AOS) to correct for alignment and mirror surface perturbations introduced by gravity and temperature gradients in the optical system. To accomplish this Rubin will use out-of-focus images from sensors located at the edge of the focal plane to learn and correct for perturbations to the wavefront. We have designed and integrated a deep learning model for wavefront estimation into the AOS pipeline. In this paper, we compare the performance of this deep learning approach to Rubin's baseline algorithm when applied to images from two different simulations of the Rubin optical system. We show the deep learning approach is faster and more accurate, achieving the atmospheric error floor both for high-quality images, and low-quality images with heavy blending and vignetting. Compared to the baseline algorithm, the deep learning model is 40x faster, the median error 2x better under ideal conditions, 5x better in the presence of vignetting by the Rubin camera, and 14x better in the presence of blending in crowded fields. In addition, the deep learning model surpasses the required optical quality in simulations of the AOS closed loop. This system promises to increase the survey area useful for precision science by up to 8%. We discuss how this system might be deployed when commissioning and operating Rubin.

We present the most extensive set to date of high-quality RR Lyrae light curve templates in the griz bands, based on time-series observations of the Dark Energy Camera Plane Survey (DECaPS) East field, located in the Galactic bulge at coordinates (RA, DEC)(J2000) = (18:03:34, -29:32:02), obtained with the Dark Energy Camera (DECam) on the 4-m Blanco telescope at the Cerro Tololo Inter-American Observatory (CTIO). Our templates, which cover both fundamental-mode (RRab) and first-overtone (RRc) pulsators, can be especially useful when there is insufficient data for accurately calculating the average magnitudes and colors, hence distances, as well as to inform multi-band light curve classifiers, as will be required in the case of the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST). In this paper, we describe in detail the procedures that were adopted in producing these templates, including a novel approach to account for the presence of outliers in photometry. Our final sample comprises 136 RRab and 144 RRc templates, all of which are publicly available. Lastly, in this paper we study the inferred Fourier parameters and other light curve descriptors, including rise time,skewness, and kurtosis, as well as their correlations with the pulsation mode, period, and effective wavelength.

Determining habitable zones in binary star systems can be a challenging task due to the combination of perturbed planetary orbits and varying stellar irradiation conditions. The concept of "dynamically informed habitable zones" allows us, nevertheless, to make predictions on where to look for habitable worlds in such complex environments. Dynamically informed habitable zones have been used in the past to investigate the habitability of circumstellar planets in binary systems and Earth-like analogs in systems with giant planets. Here, we extend the concept to potentially habitable worlds on circumbinary orbits. We show that habitable zone borders can be found analytically even when another giant planet is present in the system. By applying this methodology to Kepler-16, Kepler-34, Kepler-35, Kepler-38, Kepler-64, Kepler-413, Kepler-453, Kepler-1647 and Kepler-1661 we demonstrate that the presence of the known giant planets in the majority of those systems does not preclude the existence of potentially habitable worlds. Among the investigated systems Kepler-35, Kepler-38 and Kepler-64 currently seem to offer the most benign environment. In contrast, Kepler-16 and Kepler-1647 are unlikely to host habitable worlds.

Determining habitable zones in binary star systems can be a challenging task due to the combination of perturbed planetary orbits and varying stellar irradiation conditions. The concept of "dynamically informed habitable zones" allows us, nevertheless, to make predictions on where to look for habitable worlds in such complex environments. Dynamically informed habitable zones have been used in the past to investigate the habitability of circumstellar planets in binary systems and Earth-like analogs in systems with giant planets. Here, we extend the concept to potentially habitable worlds on circumbinary orbits. We show that habitable zone borders can be found analytically even when another giant planet is present in the system. By applying this methodology to Kepler-16, Kepler-34, Kepler-35, Kepler-38, Kepler-64, Kepler-413, Kepler-453, Kepler-1647 and Kepler-1661 we demonstrate that the presence of the known giant planets in the majority of those systems does not preclude the existence of potentially habitable worlds. Among the investigated systems Kepler-35, Kepler-38 and Kepler-64 currently seem to offer the most benign environment. In contrast, Kepler-16 and Kepler-1647 are unlikely to host habitable worlds.

While significant advances have been made in photometric classification ahead of the millions of transient events and hundreds of supernovae (SNe) each night that the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will discover, classifying SNe spectroscopically remains the best way to determine most subtypes of SNe. Traditional spectrum classification tools use template matching techniques (Blondin & Tonry 2007) and require significant human supervision. Two deep learning spectral classifiers, DASH (Muthukrishna et al. 2019) and SNIascore (Fremling et al. 2021) define the state of the art, but SNIascore is a binary classifier devoted to maximizing the purity of the SN Ia-norm sample, while DASH is no longer maintained and the original work suffers from contamination of multi-epoch spectra in the training and test sets. We have explored several neural network architectures in order to create a new automated method for classifying SN subtypes, settling on an attention-based model we call ABC-SN. We benchmark our results against an updated version of DASH, thus providing the community with an up-to-date general purpose SN classifier. Our dataset includes ten different SN subtypes including subtypes of SN Ia, core collapse and interacting SNe. We find that ABC-SN outperforms DASH, and we discuss the possibility that modern SN spectra datasets contain label noise which limit the performance of all classifiers.

We examine the simple model put forth in a recent note by Loeb regarding the brightness of space debris in the size range of 1-10 cm and their impact on the Rubin Observatory Legacy Survey of Space and Time (LSST) transient object searches. Their main conclusion was that "image contamination by untracked space debris might pose a bigger challenge [than large commercial satellite constellations in LEO]". Following corrections and improvements to this model, we calculate the apparent brightness of tumbling low-Earth orbit (LEO) debris of various sizes, and we briefly discuss the likely impact and potential mitigations of glints from space debris in LSST. We find the majority of the difference in predicted signal-to-noise ratio (S/N), about a factor of 6, arises from the defocus of LEO objects due to the large Simonyi Survey Telescope primary mirror and finite range of the debris. The largest change from the Loeb estimates is that 1-10 cm debris in LEO pose no threat to LSST transient object alert generation because their S/N for detection will be much lower than estimated by Loeb due to defocus. We find that only tumbling LEO debris larger than 10 cm or with significantly greater reflectivity, which give 1 ms glints, might be detected with high confidence (S/N > 5). We estimate that only one in five LSST exposures low on the sky during twilight might be affected. More slowly tumbling objects of larger size can give flares in brightness that are easily detected; however, these will not be cataloged by the LSST Science Pipelines because of the resulting long streak.

The rise of synoptic sky surveys has ushered in an era of big data in time-domain astronomy, making data science and machine learning essential tools for studying celestial objects. While tree-based models (e.g. Random Forests) and deep learning models dominate the field, we explore the use of different distance metrics to aid in the classification of astrophysical objects. We developed DistClassiPy, a new distance metric based classifier. The direct use of distance metrics is unexplored in time-domain astronomy, but distance-based methods can help make classification more interpretable and decrease computational costs. In particular, we applied DistClassiPy to classify light curves of variable stars, comparing the distances between objects of different classes. Using 18 distance metrics on a catalog of 6,000 variable stars across 10 classes, we demonstrate classification and dimensionality reduction. Our classifier meets state-of-the-art performance but has lower computational requirements and improved interpretability. Additionally, DistClassiPy can be tailored to specific objects by identifying the most effective distance metric for that classification. To facilitate broader applications within and beyond astronomy, we have made DistClassiPy open-source and available at this https URL.

To prepare for the upcoming Legacy Survey of Space and Time, we develop methods for quantifying the selection function of a wide-field survey as a function of all six orbital parameters and absolute magnitude. We perform a HelioLinC3D search for Centaurs in the Pan-STARRS1 detection catalog and use a synthetic debiasing population to characterize our survey's selection function. We find nine new objects, including Centaur 2010 RJ$_{226}$, among 320 real objects, along with $\sim$70,000 debiasing objects. We use the debiasing population to fit a selection function and apply the selection function to a model Centaur population with literature orbital and size distributions. We confirm the model's marginal distributions but reject its joint distribution, and estimate an intrinsic population of 21,400$^{+3,400}_{-2,800}$ Centaurs with H_r < 13.7. The discovery of only nine new objects in archival data verifies that the Pan-STARRS discovery pipeline had high completeness, but also shows that new linking algorithms can contribute even to traditional single-tracklet surveys. As the first systematic application of HelioLinC3D to a survey with extensive sky coverage, this project proves the viability of HelioLinC3D as a discovery algorithm for big-data wide-field surveys.

Ram pressure stripped galaxies are rare cases of environmental evolution in action. However, our ability to understand these galaxies is limited by the small number of identified galaxies experiencing ram pressure stripping (RPS). Our aim is to explore the efficacy of citizen science classifications in identifying ram pressure stripped galaxies, and use this to aid in motivating new samples of ram pressure stripped candidates. We compile a sample of over 200 known ram pressure stripped galaxies from existing literature, with morphological classifications obtained from Galaxy Zoo. We compare these galaxies with magnitude and redshift-matched comparison cluster and field galaxies. Additionally, we create a sample of SDSS cluster galaxies, with morphological classifications similar to known ram pressure stripped galaxies, and compare the fraction of potential new RPS candidates against control samples. We find that ram pressure stripped galaxies exhibit a higher proportion of odd and irregular morphological classifications compared to field and cluster comparison samples. This trend is particularly pronounced in galaxies displaying strong optical ram pressure stripping features, but absent from galaxies with only radio tails. We find that SDSS galaxies with Galaxy Zoo classifications consistent with the known RPS galaxies have a higher fraction of visible ram pressure stripping features ($19\%$) compared with other cluster galaxies ($12\%$) when classified by experts. We identify 101 new ram pressure stripping candidate galaxies through these expert classifications. We demonstrate that indirect morphological classifications from citizen science projects can increase the efficiency in which new stripping candidates are found. Projects such as Galaxy Zoo can aid in the identification of ram pressure stripped galaxies that are key to understanding galaxy evolution in clusters.

The Legacy Survey of Space and Time (LSST) will revolutionize Time Domain Astronomy by detecting millions of transients. In particular, it is expected to increment the number of type Ia supernovae (SNIa) of a factor of 100 compared to existing samples up to z~1.2. Such a high number of events will dramatically reduce statistical uncertainties in the analysis of SNIa properties and rates. However, the impact of all other sources of uncertainty on the measurement must still be evaluated. The comprehension and reduction of such uncertainties will be fundamental both for cosmology and stellar evolution studies, as measuring the SNIa rate can put constraints on the evolutionary scenarios of different SNIa progenitors. We use simulated data from the DESC Data Challenge 2 (DC2) and LSST Data Preview 0 (DP0) to measure the SNIa rate on a 15 deg2 region of the Wide-Fast-Deep area. We select a sample of SN candidates detected on difference images, associate them to the host galaxy, and retrieve their photometric redshifts (z-phot). Then, we test different light curves classification methods, with and without redshift priors. We discuss how the distribution in redshift measured for the SN candidates changes according to the selected host galaxy and redshift estimate. We measure the SNIa rate analyzing the impact of uncertainties due to z-phot, host galaxy association and classification on the distribution in redshift of the starting sample. We found a 17% average lost fraction of SNIa with respect to the simulated sample. As 10% of the bias is due to the uncertainty on the z-phot alone (which also affects classification when used as a prior), it results to be the major source of uncertainty. We discuss possible reduction of the errors in the measurement of the SNIa rate, including synergies with other surveys, which may help using the rate to discriminate different progenitor models.

The HR 2562 system is a rare case where a brown dwarf companion resides in a cleared inner hole of a debris disk, offering invaluable opportunities to study the dynamical interaction between a substellar companion and a dusty disk. We present the first ALMA observation of the system as well as the continued GPI monitoring of the companion's orbit with 6 new epochs from 2016 to 2018. We update the orbital fit and, in combination with absolute astrometry from GAIA, place a 3$\sigma$ upper limit of 18.5 $M_J$ on the companion's mass. To interpret the ALMA observations, we used radiative transfer modeling to determine the disk properties. We find that the disk is well resolved and nearly edge on. While the misalignment angle between the disk and the orbit is weakly constrained due to the short orbital arc available, the data strongly support a (near) coplanar geometry for the system. Furthermore, we find that the models that describe the ALMA data best have an inner radius that is close to the companion's semi-major axis. Including a posteriori knowledge of the system's SED further narrows the constraints on the disk's inner radius and place it at a location that is in reasonable agreement with, possibly interior to, predictions from existing dynamical models of disk truncation by an interior substellar companion. HR\,2562 has the potential over the next few years to become a new testbed for dynamical interaction between a debris disk and a substellar companion.

As the frontier of precision astronomical photometry continues to advance, correcting for time-variable atmospheric transmission becomes increasingly important. We describe an observational approach to monitoring optical attenuation due to atmospheric aerosols, using a multiband filter and disperser on the Auxiliary Telescope at the Vera C. Rubin Observatory. This configuration allows us to perform simple aperture photometry on four notched-out spectral regions, covering 347 to 618 nm. We see clear evidence of temporal variations in extinction across these bands, which we attribute to variation in the aerosol content of the atmosphere above the observatory. The observed differences in extinction between the reddest and bluest band can exceed 5 mmag/airmass, highlighting the importance of including variable aerosols in the transmission of the atmosphere. We aspire to using precise determinations of the optical transmission of the atmosphere to enable a forward-modeling approach to achieving mmag photometric precision with Rubin data.

The Legacy Survey of Space and Time, operated by the Vera C. Rubin Observatory, is a 10-year astronomical survey due to start operations in 2022 that will image half the sky every three nights. LSST will produce ~20TB of raw data per night which will be calibrated and analyzed in almost real time. Given the volume of LSST data, the traditional subset-download-process paradigm of data reprocessing faces significant challenges. We describe here, the first steps towards a gateway for astronomical science that would enable astronomers to analyze images and catalogs at scale. In this first step we focus on executing the Rubin LSST Science Pipelines, a collection of image and catalog processing algorithms, on Amazon Web Services (AWS). We describe our initial impressions on the performance, scalability and cost of deploying such a system in the cloud.

The chemical evolution history of the Small Magellanic Cloud (SMC) is complex and is best understood through a comprehensive analysis of star clusters across its various regions. The VISCACHA survey aims to fully explain the chemical evolution of SMC star clusters by analyzing different sub-regions adopted from an existing framework. The west halo (WH) region, which contains the oldest and most metal-poor stellar populations, exhibits a clear age-metallicity relation (AMR) with minimal dispersion. This region shows a significant dip of ~0.5 dex in metallicity approximately 6 Gyr ago. This was likely caused by a major merger event that subsequently accelerated the star formation rate. Clusters in the Southern Bridge (SB) and Northern Bridge regions of the SMC may have experienced distinct chemical enrichment histories, as suggested by our previous works but with limited data coverage. Furthermore, the AMR of wing/bridge (W/B) shows no sign of enrichment caused by the aforementioned merger event, but exhibits signatures of the recent collisions between the clouds contemporaneous with the epochs of the Magellanic Stream and Bridge formations. In this study, we present an updated AMR for the SB region based on a sample that includes approximately 67% of its known clusters. Contrary to the expectation of a very unique chemical evolution history, these SB clusters show a trend similar to the one of the WH clusters. The chemical evolution models that best fit the AMR trend of the SB clusters show excellent agreement with the major merger model proposed for the WH clusters. Building on this, we suggest a new unified chemical evolution model for both the WH and SB clusters, which can be explained by a major merger at ~6 Gyr followed by episodic chemical enrichment over time.