ADASSX
More details about physical posters and eposter submissions will be provided soon. The organizers encourage poster submissions to be provided in both physical and electronic formats to reach the widest audience. Posters will be displayed in the Kuiper Atrium where coffee breaks will be held. Physical posters should fit in a one-meter square space (the boards are 4'x8' to hold two posters side-by-side). It is the responsibility of the authors to arrange for posters to be put up. Push pins will be supplied.
Posters must be removed by the end of the final day. Any remaining will be discarded.
Eposter submissions (PDFs) are open now through the same Pretalx account where you submitted your abstract. We heartily encourage discussions about individual posters on the #poster channel on Slack.
The ADASS Program Organizing Committee has partnered with the Catalina Sky Survey, NOIRLab, the Lunar and Planetary Laboratory, Steward Observatory, and Rubin Observatory to organize a “mini-ADASS” workshop (https://catalina.lpl.arizona.edu/adassx-2025) to be held in Tucson, AZ. The meeting will follow the annual Rubin Community Workshop (https://project.lsst.org/meetings/rubin2025), and will feature special-topic workshops, tours, and splinter sessions in addition to the plenary meeting.
As the only ADASS event scheduled to be in the US for the next several years, we will provide a forum for participants who cannot easily attend the main ADASS meeting in the Fall. The ADASSx 2025 program will include plenary talks, posters, software demonstrations, and opportunities for attendees to organize topical interest sessions. These activities aim to stimulate further development of software and systems to meet the data science challenges of astronomy. Remote attendance will be supported. Themes include:
Time-domain Astronomy
Planetary Defense
Community Infrastructure
Pipelines and Archives
Other software topics of broad interest
On June 23rd, 2025, the Rubin Observatory made public its first science-usable dataset, in the form of a submission to the Minor Planet Center of around 340,000 measurements of about 2100 newly designated asteroids. This batch, identified and measured over a 12-night period in later April/early May 2025 and totaling about 10 hours of observing time, was enabled by Rubin's large mirror and wide FoV camera, but also a complex software stack capable of detecting, measuring, linking, QA-ing and submitting these objects a short time after observation.
This is not an aberration. Modern survey telescopes, including small body surveys, increasingly depend on advanced algorithms to take maximum advantage of hardware available to them. And with the escalating cost and difficulty of building ever larger telescopes, it is a trend that is sure to intensify. In this presentation, I will overview what we've learned from Rubin (so far) about developing and deploying asteroid detection and linking algorithms. I will discuss how our experiences connect to broader (and rich) developments in the community, and argue that upcoming improvements in Solar System mapping will be driven by software as much as hardware advances.
The Transients and Variable Stars Science Collaboration (TVS) for the Vera C. Rubin
Legacy Survey of Space and Time (LSST) includes a strikingly wide-range of researchers
and interest topics, from exoplanets to stellar variability of all kinds through to
extra-galactic transients such as Tidal Disruption Events. As a result, the community
also presents a wide range of requirements and challenges for the software tools needed
to accomplish these science goals. TVS has an active Software Group that aims to
facilitate the development of the necessary tools and to provide training opportunities
in key technologies.
TVS's groups for transient phenomena, ranging from microlensing to flares to
supernovae, are working with the developers of several brokers to produce filtered
alert streams to identify their targets of interest. Meanwhile, groups interested in
large-scale population analyses, or the detection of new stellar associations are
investigating optimal ways to perform very large catalog queries and/or bulk image
analysis. TVS' group searching for optical counterparts of gravitational wave or
neutrino detections are collaborating closely with Rubin project to establish protocols
for Rubin to perform a limited number of Target of Opportunity overrides.
The Science Collaboration is also the recipient of a number of directable software
contributions under Rubin's International In Kind program. These projects are
undertaken collaboratively to provide tools and services of general interest to TVS
members.
I will review ongoing software development efforts relating to TVS and In Kind
contributions.
The Stars, Milky Way, and Local Volume (SMWLV) LSST science collaboration covers a wide range of astronomical topics in the (at least relatively!) nearby universe, in both the static and variable skies, all of which the Vera C. Rubin Observatory is set to provide vast amounts of information for. However, as with most science cases in the "era of LSST", we will be challenged more by our own algorithms, both to reduce the data the telescope will provide and to interpret the resultant analyses, than with the data themselves. After a brief overview of the science collaboration, its members and its scientific interests, I will highlight a few of the areas at the forefront of software development within the science collaboration, such as the challenges around analysis in crowded Rubin stellar fields, large-scale classification problems, and in the treatment of systematics in analysis of the inter-stellar medium and galactic structure.
The start of operations at the Vera C. Rubin Observatory will herald a new era in astrophysics and cosmology, and with it, a new set of challenges. How can science collaborations deal with searching and sorting the large volume of data from Rubin, particularly as the project’s pipeline (and therefore the data’s provenance) evolves continuously over the course of the 10-year survey? How do we handle faulty data, or quantities for which multiple estimates exist from different sources? How do we provide meaningful value-added data products to the community, if these also require dedicated HPC resources to use to replicate analyses? How do we meaningfully interpret what our pipelines are even doing when an increasing number of their components are complex AI algorithms that may be soon be developed by other complex AI algorithms. DESC is beginning to grapple with these questions, and I will present some of the approaches we are taking in various DESC projects, and solicit your input for what we can do better.
The ADASSx Opening Reception will be held at Gentle Ben's (https://www.gentlebens.com) from 6:00 to 9:00 pm, Friday, August 1. Located adjacent to the Tyndall Avenue stop of the free air-conditioned Tucson Streetcar (https://www.suntran.com/routes-services/sunlink/), it will be easy to get to after the final workshop session of the day. Gentle Ben's is part of the Main Gate shopping and dining district of the University of Arizona (https://www.maingatesquare.com).
The Catalina Sky Survey holds its annual Monsoon Workshop during the Summer telescope shutdown for southern Arizona observatories. We have benefited from the participation of Spacewatch and other near-Earth asteroids (NEA) projects at the Lunar and Planetary Laboratory for many years. More recently, we have invited projects from the larger Planetary Defense community to participate both in-person and online.
From MPEC N12: "A new NEOCP candidate A11pl3Z was discovered by ATLAS Chile (W68) in four 30-second survey images taken on July 1 UT. Immediate follow-up and precovery observations by Q.-Z. Ye (I41) and S. Deen (W68, M22), including data from June, revealed a highly eccentric, hyperbolic orbit (e ~ 6). There are tentative reports of cometary activity from X09 (S. Deen), G37 (Q.-Z. Ye) and T14 (R. Weryk) with a marginal coma and a short 3" tail at a position angle 280 deg. Additional observations are strongly encouraged to better constrain the object's orbit and nature."
The era of Vera C. Rubin and NEO Surveyor and V=24 near-Earth object discoveries is nearly upon us. Observatories and archives are scrambling to find ways to squeeze even more performance out of their systems to meet the challenges of Rubin and Surveyor. Yet there is still a great deal of action brighter than V=24, as Rubin may not detect many small near-Earth asteroids when they are very close to the Earth. This regime is where the ATLAS survey lives.
Now with five observatories surveying 24/7 distributed globally, ATLAS has accelerated efforts to improve multi-observatory survey efficiencies and object detection, in some cases via real-time collaborations with other projects. In this talk, we present three such efforts with in ATLAS: a) real-time telescope scheduler optimized for NEO discovery; b) cross-telescope and cross-organizational detection linking of discovery tracklets, and c) participation in public archives for object recovery. We close with some guidance regarding the difficult problem of object precovery and the importance of deep knowledge regarding survey data in this process.
The Catalina Sky Survey (CSS) is a NASA-funded, ground-based observatory program dedicated to discovering and tracking Near-Earth Objects (NEOs) that may pose a threat to Earth. Operating since 1998, CSS employs a network of telescopes and a robust software pipeline to conduct both survey and targeted follow-up observations. Key facilities include the Mount Lemmon 1.5-m and 1.0-m telescopes (G96 and I52), the Mount Bigelow 0.7-m Schmidt (703) and Kuiper 1.55-m (V06), and the Bok 2.3-m telescope at Kitt Peak (V00), with the telescopes varying in field-of-view from 1 to 19 square degrees.
The CSS pipeline manages image calibration, astrometric and photometric reductions, and moving object detection. Calibration includes bias subtraction, flat-fielding, and sky background correction, while astrometric solutions rely on SCAMP and the Gaia DR2 catalog. Photometric calibration begins with SEXTRACTOR’s MAG_ISOCOR magnitude, adjusted by matches to catalog stars and finalized in Gaia G-band. The detection of moving objects is achieved through catalog comparisons and image subtraction techniques, with candidates scored via the DIGEST2 tool to prioritize likely NEOs.
A key operational feature is CSS’s real-time human validation system: each night, observers visually inspect thousands of blinked image sequences to confirm moving object detections. Validated detections are reported to the Minor Planet Center (MPC) in the ADES format, and the archival data is distributed via the Planetary Data System (PDS). CSS’s configuration-controlled pipeline ensures consistency across processing steps, and software development is maintained under Subversion (SVN).
Beyond discovery, CSS operations include confirmation of new discoveries, orbital arc extensions, and archival searches (“precoveries”) for earlier detections of known NEOs. The survey routinely performs follow-up on its own discoveries and contributes incidental astrometry for thousands of known objects each night.
CSS is also expanding its reach through crowd-sourced initiatives like its citizen science project on Zooniverse, The Daily Minor Planet and by developing NEOfixer, a broker that ranks follow-up priorities using input from MPC, Scout, and other data sources. These innovations aim to enhance community coordination and ensure that high-priority NEOs receive timely and sufficient follow-up. Ultimately, CSS's operations model emphasizes automation, real-time human validation, and archival accessibility—positioning it as a vital component of planetary defense.
Pan-STARRS (PS) continues to be one of the most productive near-Earth object (NEO) survey programs to date. While some researchers have suggested that NEO discoveries are beginning to plateau or decline year-on-year, the community continues to set new records. Most notably, both PS and ATLAS surpassed their personal best for discoveries in 2024. For PS, this sustained productivity is largely due to improvements in our follow-up strategies and ongoing improvements to our Moving Object Processing System (MOPS) that leverage new technologies.
In this talk, I will provide a brief overview of recent changes to PS's NEO follow-up approach, including updates on the status of our observatories. I will then delve into recent upgrades to MOPS, with a focus on our transition to adopting micro-services and containerization to enhance scalability and control. I will conclude with a discussion of what our plans are for the upcoming LSST era.
The Zwicky Transient Facility (ZTF) is a wide-field optical time-domain survey that has been in operation since 2018. Routine near-Earth object (NEO) searches have been conducted on the dataset, resulting in the discovery of about 300 NEOs. We will present an update of the NEOZTF program featuring three recent upgrades: a revised Twilight Survey which operates to 10 degrees twilight, the AutoStreak pipeline for linking multiple trailed objects, and a routine search for (p)recovery detections of newly discovered NEOs.
On 27 December 2024 the newly-discovered near-Earth asteroid 2024 YR4 was identified as a virtual impactor whose impact probability later pushed it to Torino Scale 3—the first object ever to trigger an International Asteroid Warning Network notification. We performed a multi-facility observing campaign combining time-series photometry (LDT, VLT, CSS), broadband visible–NIR colours (LDT, TNG, VLT/HAWK-I) and low-resolution spectroscopy (GTC/OSIRIS).
A Fourier-series analysis of four independent light-curves yields a synodic rotation period of 19.4633 ± 0.0002 min. The composite reflectance spectrum is best matched by Sq/K-type taxonomy. Phase-curve photometry in the R band gives H_R = 23.8 and an unusually shallow slope (G = 0.50). Combining the recent results from the JWST (D=60 +- 7m) and our determination of the H magnitude, we found that YR4 should display a moderately low albedo that would be more consistent with a K-type classification rather than an S-type.
These results, obtained within weeks of discovery, illustrate the effectiveness of coordinated rapid-response strategies for planetary defence. The precise spin state, taxonomic class and photometric properties derived here would have been critical inputs for impact-probability refinement and mitigation-option studies had the threatening trajectory persisted. 2024 YR4 therefore serves as a touchstone case for future fast-paced characterization of hazardous asteroids.
Over the last twenty years, the discovery of Near-Earth Objects (NEOs) has relied heavily on dedicated survey programs and the subsequent vetting of candidates via the Minor Planet Center’s Near-Earth Object Confirmation Page (NEOCP). This platform plays a central role in the rapid identification of NEOs by publishing short-arc tracklets for immediate follow-up by the astronomical community. Thanks to the rapid follow-up, over 38,000 NEOs have been cataloged to date, with discovery rates exceeding 3,000 per year since 2020.
Candidate selection for NEOCP posting is primarily based on the NEO digest2 score — a probability metric estimating whether a given object is an NEO. Tracklets with a digest2 score above 65 are qualified for posting, as NEOs typically score close to 100, while other populations, such as main-belt asteroids, tend to score much lower. Nevertheless, roughly 6,000 candidates appear on the NEOCP annually, of which approximately 11% remain unconfirmed due to insufficient follow-up. Among those confirmed, only about two-thirds are ultimately classified as NEOs, with the rest largely consisting of main-belt objects.
In this study, we perform a systematic evaluation of 13 digest2-derived orbital classification categories, analyzing them in both their "raw" and "noid" configurations. Our objective is to improve the efficiency of the NEOCP by reducing the prevalence of non-NEOs among posted candidates. We show that by incorporating the full set of digest2-derived parameters—rather than relying solely on the NEO digest2 score—it is possible to filter out up to 20% of non-NEO submissions without significantly impacting true NEO recoverability.
Additionally, we explore the predictive capabilities of several machine learning (ML) classifiers—Gradient Boosting Machines (GBM), Random Forests (RF), Stochastic Gradient Descent (SGD), and Neural Networks (NN)—applied to NEOCP candidate data collected between 2019 and 2024. Using observations from 2019–2023 for training and 2024 for validation, we achieve consistent NEO classification accuracies of 91%–92%, with negligible variation across models.
We advocate for integrating digest2-based feature sets with ML methodologies to improve candidate selection on the NEOCP. This approach promises not only to reduce the burden of false positives on follow-up networks but also to enhance the overall efficiency and completeness of NEO detection efforts.
The Vera C. Rubin Observatory is a new NSF/DOE-funded facility on Cerro Pachón, Chile. It houses the 8.4m Simonyi Survey Telescope and the 3.2 Gigapixel LSSTCam camera. The Observatory is in the final stages of commissioning, expected to enter operations by the end of 2025. Once operational, Rubin will execute the Legacy Survey of Space and Time (LSST). Enabled by its 9.6 square degree field of view and a cadence covering the sky every 3-4 days to ~24.5 mag, the LSST dataset can dramatically advance the understanding of the Solar System and planetary defense.
This talk will present the first public Solar System-related results from Rubin's early commissioning efforts, their implications to Planetary Defense, and discuss the plans to submit Rubin single-night high-confidence tracklets for inclusion on the NEOCP. Because publication could initially increase NEOCP traffic to >100 new objects per night, at very low purity (<10%; Wagg et al. 2025), it will be important to organize community follow-up around the highest confidence and follow-up value candidates. I will present some options, and hope to initiate a discussion on what the community would like to see for successful follow-up of Rubin NEOCP submissions.
Space missions provide an opportunity to rapidly increase knowledge in a field by acquiring data that responds to a specific mission goal. Understanding the intersection of space-based and ground-based observation is critical to maximizing the scientific return of any mission. For example, the impact of the Gaia spacecraft extends to every corner of astronomy. This mission had a simple goal: produce a high-precision astrometric and photometric catalog of the sky, and its measurements have fed into many follow up observational studies. For asteroids, the ten-fold astrometric precision improvement enabled by Gaia’s data has profoundly affected the quality of asteroid orbits, in addition to yielding advantages in all other astronomy fields.
The NEO Surveyor 0.5m telescope at Sun-Earth L1 will observe simultaneously in two bands, NC1 (4-5.2µm) and NC2 (6-10µm). These two infrared observations will provide an astrometric position that is improved by the simultaneous two-band measurement of the asteroid and stars, and the object's diameter through modeling of the thermal flux. The mission design will take advantage of the fact that the near-Earth object population has an average albedo of much less than 50%, which means most of the Sun’s energy is absorbed, making these asteroids thermally bright.
NEO Surveyor is designed to discover and measure diameters of the near-Earth asteroid and comet population. It will not collect other physical characterization observations such as spectral type or geometric albedo, and is not optimized to obtain rotation rates for all objects detected. Therefore, this provides an opportunity for ground-based telescopes to expand the mission's scientific return by carrying out ground-based population studies.
We will present three possible ground-based studies that can be undertaken. First, since NEO Surveyor will detect asteroids via their thermal emission, the actual albedo of the asteroid will remain unknown without optical observations. As described in Masiero et al. (2024), the best estimate NEO Surveyor will be able to provide of a possible V-band magnitude is plus or minus two magnitudes. Therefore, the first opportunity for ground-based observers is to carry out a large ground-based campaign to collect optical observations of NEO Surveyor’s discoveries. Without an optical tracklet, additional characterization observations will be unlikely to succeed due to the large magnitude uncertainty.
The second possible ground-based observation campaign will be a geometric albedo study. Combining geometric albedos with NEO Surveyor diameters would uncover the real NEO albedo distribution. The third useful campaign will be a spectral type versus diameter study to determine the spectral type distribution of the NEO population as a function of size.
As the list of NEOs and recent discoveries rapidly grows, NEO follow-up observers need an automated way to determine the best targets they can observe while also preventing duplication of effort. This is exactly why NEOfixer was created. It already does this, and will continue to do so, as Vera Rubin and NEO Surveyor make discoveries. We expect to make some changes as the situation evolves and are already working with the NEO Surveyor team to make one such adjustment.
SPACEWATCH®, which pioneered using CCDs to survey the sky for NEOs, currently conducts Near Earth Object (NEO) follow-up observations. To improve planetary defense capabilities by reducing the uncertainty in NEO orbital elements, we conduct full-time rapid astrometric follow-up observations of high priority NEOs as the sole users of the Lunar and Planetary Laboratory’s Spacewatch 1.8-m observatory and the Steward Observatory’s 0.9-m telescope on Kitt Peak. Additionally, we conduct astrometric follow-up with Steward Observatory’s Bok 2.3-m telescope during bright time with the Spacewatch Cassegrain Camera (SCC).
Our highest priority targets for NEO astrometric follow-up are virtual impactors (VIs) and Potentially Hazardous Asteroids (PHAs). PHAs are ≳140 meters in diameter with Earth Minimum Orbit Intersection Distances (EMOIDs) ≲ 0.05 au. VIs have sufficiently uncertain heliocentric orbital parameters such that at least one orbit solution predicts an Earth impact within 100 years. PHAs pose a greater hazard due to their size, but the majority do not have orbits in which the asteroid could impact Earth itself. VIs pose a greater impact risk due to their real (but low) probability of impact. Currently, only ~1% of NEAs on the JPL Sentry risk list of VIs are “large” (>140 m). It is particularly important to minimize the orbit uncertainties for VI PHAs to rule out (or in) possible impacts.
Spacewatch has observed a majority of the newly discovered NEOs that are or were on JPL’s VI impact risk list since October 16, 2019. According to the PDS SBN, from Sept. 1993 through March 2025, the 1.8-m is third in making the first observation for follow-up MPECs, sixth in follow-up MPECS, and fifth over all types of MPECs. It is fifth in MPECs for making the first follow-up observation over the past year. The 0.9-m is sixth in discovery MPECs and eighth in precovery MPECs from September 1993 through March 2025.
The Save Earth Joint Observations for the Next Generation (SEJONG) telescope represents South Korea’s first dedicated facility for asteroid surveillance and is scheduled for installation at Cerro Tololo Inter-American Observatory (CTIO) in Chile by early 2027. Equipped with a 1.55-meter aperture and a wide 2.24° × 2.24° field of view, SEJONG is designed to proactively detect near-Earth asteroids (NEAs) and potentially hazardous asteroids (PHAs), thereby strengthening global planetary defense capabilities.
To ensure effective operation, we aim to develop optimized observation strategies tailored to early threat detection. Using Granvik’s near-Earth asteroid population model, we analyze the sky-plane distribution and orbital characteristics of yet-undiscovered NEAs. This analysis helps identify favorable regions and times for detection.
Based on these results, we propose survey strategies for SEJONG that maximize its capability to discover hazardous objects. This work lays a foundation for SEJONG’s operations and contributes to international efforts to mitigate asteroid impact risks.
The Alice P. Lennon Telescope and the Thomas J. Bannan Astrophysics Facility, informally the Vatican Advanced Technology Telescope (VATT), dedicated in 1993, is located on Mount Graham in Arizona. It is an aplanatic Gregorian telescope with an f/1 primary and an f/9 full system optics. In 2024, the mount control system was overhauled and a complete facility control system was implemented by ProjectSoft HK (Czech Republic). The new system, named ‘Don’, in honor of Donald M. Alstadt (1921-2007), uses PLCs. Together with new absolute encoders and drives, the telescope provides 3 arcsec rms pointing, and unguided tracking >1 hr. ‘Don’ will allow for remote and/or scripted operation, with no need for on-site operators. We are currently working on automated collimation and focusing. Procurement of a new imaging camera is also in process.
The oldest telescope making regular contributions to Planetary Defense is likely the Spacewatch 0.9-meter telescope, MPC code 691. When the 0.9-meter was originally commissioned in 1923 for other duties at the University of Arizona, only three Near-Earth Asteroids (NEAs) had been discovered: Eros (433), Albert (719), and Alinda (887). The Minor Planet Center catalog now contains more than 38,000 NEAs, 908 of which were discovered by 691 between its first NEO discovery in 1989 and its most recent in 2018.
A large fraction of all telescopes used for Planetary Defense were constructed for other purposes. The community can feel justifiably proud about the several million astrometric and photometric observations of NEOs that have been made with these telescope assets. But the central conundrum of Planetary Defense funding, at least in the United States, has been that while NASA supports Planetary Defense, they do not build new telescopes, and while NSF can fund telescope construction, they don't do Planetary Defense. Thus, identifying larger telescopes, so-called "midscale" telescopes between about 2-4 meters in diameter, requires creative negotiation over telescope allocation and wide-field imagers.
With the advent of Rubin observatory in the southern hemisphere, and the anticipated launch of the Infrared NEO Surveyor spacecraft to the L1 Lagrange Point, ground-based telescopes for follow-up and characterization of targets from the future surveys will become critical. These can also help fill the survey gap in space and time left by Rubin and NEOS in the northern hemisphere. This session will bring together representatives of diverse telescope facilities in the 1.5 to 2.5 meter range to discuss possible strategies for allocating more time and improved instrumentation to Planetary Defense activities, while identifying ways in which this segment of the community might benefit each other through operational best practices and united action.
I provide an update on the many changes to the MPC's processing pipeline that have been implemented in preparation for the influx of data expected from the Rubin LSST survey.
I will describe the changes that have been made to our hardware, software and overall system architecture with the goal of improving the efficiency and resilience of the processing system.
Finally I will go on to describe the ongoing and future work that we will be performing to further improve the processing of all data submitted to the MPC.
CATCH is a search tool designed to identify observations of comets and asteroids in wide-field time-domain sky survey data. A driving use case for CATCH is to find observations or significant non-detections of potentially hazardous asteroids in archival data sets. Hosted and maintained by the Planetary Data System's Small Bodies Node, it currently contains 38 million observational data products from twelve different observatories, including surveys such as Spacewatch, Catalina Sky Survey, and ATLAS (Asteroid Terrestrial-impact Last Alert Survey). Together there are 12,000 observatory-nights spanning the years 1996 to 2025. We briefly review the methodology of CATCH and its features, then present our recent updates and future plans. For example, in 2025 we updated CATCH to support fixed-target searches, provide a 3D view of the Solar System at the time of the observation, and added the ability to limit searches by date range. Development on CATCH continues, and we will share our progress re-writing the CATCH backend, aimed at reducing typical full-database moving target search times from minutes to tens of seconds, and adding orbital-element-based searches.
While the majority of solar system objects discovered by wide field surveys are ordinary, they also provide the opportunity to discover hidden gems such as interstellar objects, near-Sun asteroids, and bright comets. These provide opportunities to characterize extrasolar planetesimals, search for new sources of asteroids in the inner solar system, and study the composition of the protoplanetary disk. I will describe survey techniques used to discover these hidden gems in observations of the near-Sun sky during twilight in current and next-generation surveys, such as the Zwicky Transient Facility and the Rubin Observatory. I will describe three examples of twilight solar system results: 1.) the discovery and follow-up observations of (594913) 'Ayló'chaxnim, the first known asteroid possessing an aphelion entirely within the orbit of Venus, 2.) the recovery of interstellar comet 2I/Borisov, and 3.) the discovery of naked-eye comet C/2022 E3 (ZTF). I will discuss the behind-the-scenes work of using machine learning in these results and their implications for the formation of the solar system and the composition of extrasolar and solar system planetesimals.
The ability to efficiently detect faint fast moving asteroids requires a knowledge of the brightness of the night sky. We are using Sky Quality Meters (SQMs) located on Mt. Lemmon, Mt. Bigelow, Kitt Peak, the Cosmic Campground IDSS, and other locations to measure night sky brightness. We have developed techniques to detect clouds and thus select the best astronomically dark nights. These data are used to measure sky brightness changes due to increasing solar activity, periodic changes as the Earth orbits the Sun, as well as dynamic events caused by coronal mass ejections or other changes in the solar wind. An interesting new result is that there appears to be a sky brightness minimum a few weeks after the vernal equinox and a maximum a few weeks after the autumnal equinox.
Planetary defense surveys routinely find artificial objects in their data. Some move slowly enough to mimic natural objects. It is desirable to keep track of them, so that they won't be erroneously reported as minor planets, and so that follow-time isn't wasted on them. The methods and problems involved in tracking them will be discussed.
The Minor Planet Center (MPC) is the single worldwide location for receipt and distribution of positional measurements of minor planets, comets and outer irregular natural satellites of the major planets. Crucially, this includes Near Earth Objects (NEOs) and Potentially Hazardous Asteroids (PHAs). Thusly, real-time insight into the MPC’s operations is necessary. We present “dashboarding” tools that provide assurances of the MPC’s processing functionality, data on its performance, and statistics on its archive.
New asteroids and comets are continuously being discovered in the night sky. Such objects need to be dynamically characterised quickly, to determine the possibility of Earth impact and schedule follow-up observations before the object is lost. Here we present the Meerkat Asteroid Guard, an automated imminent impact warning service developed and operated at the ESA NEO Coordination Centre.
Meerkat continually downloads tracklets for newly discovered objects from the Minor Planet Center. For many of these new objects, the observation arc length is short. While the object's plane of sky position and motion may be well known, the remaining two parameters required to describe the orbit, the topocentric range and range rate, are not. Such short arcs lead to severe errors and degeneracies in traditional orbit determination methods. To overcome this, we employ the method of systematic ranging, whereby a grid of topocentric range and range rates have their orbits fitted with associated weighted root mean square error. From this error we derive a posterior probability distribution. By scanning a suitably dense grid, we can produce a statistical description of the most likely orbital solutions, and derive important information such as estimated size and impact probability. Meerkat operates 24/7, delivering email and phone alerts to subscribed users for imminent impactors and close approaches.
Over its five-year operational lifetime, Meerkat has successfully issued alerts for the past seven imminent impactors, from 2022 EB5 to most recently 2024 XA1. These alerts were vital for coordinating follow-up observations and preparing local authorities for fireball events.
After undergoing major software development and testing, v2.0 is ready to be released. Much of the functionality has been redesigned with ESA's new flight dynamics library, GODOT. This library is comprehensive and versatile, enabling the optimisation of the orbit determination, impact monitoring and ephemeris routines. New features such as the grid memory and propagation error handling have reduced the error rate of the systematic ranging algorithm. Under the hood, the software exploits a fully dockerised architecture for more efficient service and deployment. The software input is upgraded to meet modern standards, able to process ADES 2022 files and retrieve observations from the Minor Planet Center SBN Postgres database. In the output, new metrics flag potential false positive alerts to assist astronomers in time-critical follow-up observations.
The importance of an imminent impactor warning system cannot be overstated. With the advent of new surveys from ESA Flyeye, the Vera Rubin Observatory and NEO Surveyor, the number of new detections is predicted to increase dramatically. To this end, it is vital we ensure our planetary defence systems are as accurate and reliable as possible, ready to inform decision-makers when hazardous objects are found.
Citizen science is a form of participation and collaboration that actively involves non-scientists in scientific research. It is used in a wide range of areas, including ecology, medicine and astronomy. In recent years, citizen science has emerged as a powerful tool in astronomical research, enabling people from all over the world, regardless of their age, nationality or gender, to contribute to real scientific discoveries. In the field of asteroid search and observation, citizen scientists can currently help professional astronomers in three projects: International Astronomical Search Collaboration (IASC), The Daily Minor Planet (TDMP), and Come on! Impacting ASteroids (COIAS). This is particularly evident in the context of Planetary Defense, where detection and tracking of Near-Earth Objects (NEOs) are critical for assessing potential impact threats. The talk is an attempt to analyze these projects and presents a first-hand experience from an active participant involved in each of them. This work shows my observations, research and discoveries of minor planets as a citizen scientist. The presentation will cover different types of asteroids, including Main-Belt Asteroids, Near-Earth Objects, Jupiter Trojans and Trans-Neptunian objects, illustrating how citizen science contributes to our broader understanding of the Solar System dynamics.
MPEC Watch (https://sbnmpc.astro.umd.edu/mpecwatch/) is a utility that digests the Minor Planet Center’s publications to present statistical summaries of the reported observations of small bodies that are of high interest to the community. MPECs, or “Minor Planet Electronic Circulars” (https://minorplanetcenter.net/mpec/RecentMPECs.html), are issued in the form of emails and corresponding website postings, with their own DOIs by the Minor Planet Center for announcing discoveries of objects of interest (like Near-Earth Objects - NEOs, irregular satellites, or comets) and updates to the MPC’s database. MPEC Watch provides a summary of the discovery, follow-up, and first-follow-up statistics by worldwide observatories and surveys, which can be used to demonstrate the effectiveness of NEO observing programs to the community. We will discuss recent and planned upgrades of MPEC Watch, including searchable summary tables, data export options, survey statistics, and object-specific breakdowns.
This work is supported by the NASA Planetary Data System’s Small Bodies Node (NASA 80NSSC22M0024).
Satellite interference is a shared challenge across both optical and radio astronomy. Open-source tools play a key role in mitigation efforts, but they’re not always easy to find, adapt, or build on.
This session brings together the optical and radio communities via SatHub at the IAU CPS (https://cps.iau.org/) to highlight practical, open-source approaches to detecting and modeling satellite signals, plus mitigating their impact on observations when possible. We'll begin with two brief talks—one from each domain—to frame the problem space and highlight some current projects, followed by a set of short invited presentations. The second part will focus on discussion and questions, aimed at identifying common needs, surfacing existing tools, and exploring opportunities for collaboration.
Links to current projects:
SatChecker (https://satchecker.readthedocs.io/en/latest/, https://github.com/iausathub/satchecker)
SCORE (https://score.cps.iau.org/, https://github.com/iausathub/score)
SOPP - Satellite Orbit Prediction Processor (https://pypi.org/project/sopp/, https://github.com/NSF-Swift/satellite-overhead)
The Keck Observatory Archive (KOA) curates records of over 100 million observations acquired by the 13 instruments (11 active, 2 retired) operating at the W. M. Keck Observatory, and the archive is expected to grow rapidly as complex new instruments will soon be commissioned and as the expectations of archive users have expanded. In response, KOA has been deploying new Python based infrastructure. We have deployed real time ingestion of newly acquired data, and a dedicated interface for observers to manage these newly acquired data. We have been developing a new fast python-based VO-compliant query infrastructure. Our poster at ADASS 2024 identified the technologies chosen: Plotly-Dash, a low-code framework that exploits event-driven callbacks to simplify the handling of user interactions; R-tree spatial indexing to speedup spatial searches by x20; a VO-compliant TAP middleware, used already at the NASA Exoplanet Archive and the NEID archive; and mViewer, a visualization engine in the Montage Image Mosaic toolkit that is optimized for astronomy images.
These technologies will underpin new services that can be hosted on web pages or in Jupyter notebooks, and when completed, will replace the current query infrastructure. We have completed two new, fully functional interfaces that are in beta release. One is the Data Discovery Service, a web-based dashboard which queries the entire archive in seconds, and supports filtering observations by keywords, previewing results in a interactive data grid, visualizing images, and downloading raw, quicklook reduced and pipeline reduced data. The second is a Jupyter notebook that demonstrates how use CDS Vizier to create a list of protostars in the Rho Oph dark cloud, discover which Keck instruments have observations of them, and overlay the positions of the observations on an image downloaded from IRSA of Rho Oph, measured with the Infrared Array Camera.
This presentation describes the design of these services, and gives demonstrations of them in operation.
We will demonstrate how to use NEOfixer for a variety of specific purposes and go over features you might not be aware of. We will cover manual use of the web page and automated use of the API. This tutorial will show how to find the best targets for you, update your targets, report your observing status, and view the status of other observers. Examples will range from finding a single target to do now, to finding targets for a night, to finding targets that meet specific requirements. Questions will be encouraged.
The Tycho Tracker software has evolved since its development began in 2018. The latest version supports measuring asteroids, comets, variable stars, and artificial satellites. In this session, we will examine several examples of how to use the Tycho Tracker software for the detection and measurement of asteroids, comets, and artificial satellites.
(1) Discovery: How to use synthetic tracking for blind search.
(2) Recovery: How to use synthetic tracking to recover objects.
(3) Asteroid Photometry: Using the light curve module to generate time-series photometry of asteroids, and determine rotation period.
(4) Comet Photometry: Using the new comet photometry module to compute Afrho value for comets and generate measurements in ICQ format.
(5) Artificial satellites: Detection, identification, and measurement using the new FAST Tracker module.
The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will catalog over 100,000 NEOs along with around 5 million Main Belt asteroids, almost 300,000 Jupiter Trojans, and over 40,000 KBOs. The LSST Solar System Science Collaboration (SSSC) has been preparing tools and methods to analyze these data, organize and conduct follow-up and carry out the wealth of science that will be possible with Rubin Observatory's LSST.
I will provide an update on some of the projects underway within the SSSC such as the Sorcha survey simulator, forced photometry for fainter objects, activity and tail detection, the RAFT (Research Announcements for the Solar System) system for rapid communication of important results and the Follow-up Of Moving Objects (FOMO) Target and Observation Manager system for coordinating characterization follow-up observations.
The CATCH service at PDS-SBN allows users to find targets of interest within archived survey datasets. This service computes target locations and searches specified data sets for the frames where the target may have appeared. CATCH provides an API interface and a graphical display of cutouts to provide quick evaluation of data. We will discuss initial steps to implement online tools to aid in target identification and initial analysis. These tools include astrometric corrections and photometric calibration of subframes relative to standard catalogs to precisely find targets regardless of the original data's pointing accuracy. Image stacking will enable searches for fainter objects not detectable in individual frames. Photometric tools are also under development, accommodating flexible aperture and background annulus parameters to allow studies of both asteroids and comets.The CATCH Analysis Tools will include streamlined steps for generating target locations and brightnesses in formats suitable to report to the MPC.
The James Webb Space Telescope was called the "First Light Machine" when it was being studied as a potential NASA mission. It quickly became obvious that it would be impossible to prove that the "first" galaxy had been seen, but much more promising was looking for the most distant galaxies possible. This goal led astronomers to design a suite of instruments for the telescope that are optimized for looking for very faint galaxies at infrared wavelengths -- infrared is required because the expansion of the Universe moves the ultraviolet-visible output of galaxies to longer wavelengths. We have succeeded at finding galaxies seen at an age of less than 300 million years after the Big Bang. Such distant galaxies are proving to have unexpected properties which challenge our ideas of how stars formed in these first galaxies.
The ADASSx Banquet will be held at the Flandrau Planetarium from 6:00 to 9:00 pm, Monday, August 4, 2025. Flandrau (https://flandrau.org) is located immediately adjacent to the Kuiper Space Sciences building where all ADASSx sessions will take place. In addition to a buffet dinner and planetarium show, attendees will have free access to Flandrau's science exhibits. USA Today ranks Flandrau as one of the top ten planetariums in the country.
Light curve simulations are essential for the time-domain astronomy community to prepare for and to conduct at-scale analyses of LSST data, enabling exploration of the detectability of novel transient and variable classes, validation of data processing pipelines, and both implementing and executing advanced analysis techniques such as likelihood-free inference. While a common light curve simulation software framework could enable more sophisticated and efficient modeling across all science foci, development of modeling tools has historically been performed piecemeal on a per-research-group basis, with attempts to establish common tooling limited to specific analysis goals, posing a challenge to for both developers and users when it comes to adapting such codes to applications beyond their original scope. The LSST Interdisciplinary Network for Collaboration and Computing (LINCC) Frameworks team is developing a new software package, TDAstro, to address the need for an accessible, scalable, and extensible light curve simulation infrastructure to meet the needs of the time-domain astronomy community in fully exploiting LSST data for discovery and physical insight. This presentation showcases the capabilities of the TDAstro codebase and invites community contributions.
Surveys are a crucial tool for the discovery of electromagnetic counterparts to gravitational wave sources, such as kilonovae. Machine learning tools can play an important role in aiding search efforts. We have developed a public tool to predict kilonova light curves using low-latency alert data from the International Gravitational Wave Network during observing runs 4 (O4) and 5 (O5). This method uses a bidirectional long-short-term memory (LSTM) model to forecast kilonova light curves from binary neutron star and neutron star-black hole mergers in the Zwicky Transient Facility (ZTF) and Rubin Observatory's Legacy Survey of Space and Time filters. The model achieves a test mean squared error (MSE) of 0.19 on average across all ZTF and Rubin filters. We verify the performance of the model against merger events followed-up by the ZTF partnership during O4a and O4b. We also analyze the effect of incorporating skymaps and constraints on physical features such as ejecta mass through a hybrid convolutional neural network and LSTM. Using mass ejecta, the performance of the model improves to an MSE of 0.1. However, using full skymap information results in slightly lower model performance. Our models are publicly available and can help to add important information to help plan follow-up of candidate events discovered by current and next- generation public surveys.
We present an initial release of the Open mulTiwavelength Transient Event Repository (OTTER), a publicly available catalog of published transient event metadata and photometry. For this initial release, we focus on ingesting data related to tidal disruption events, a transient event that occurs when a star is torn apart by a massive black hole. Unlike previous efforts, our data schema is optimized for the storage of multiwavelength photometric datasets spanning the entire electromagnetic spectrum, from radio to X-rays. The dataset is stored using a document database structure to maximize flexibility while maintaining a level of data standardization. A web application provides an easy way to view the existing data and upload new datasets. A RESTful API, and related Python package, are available for bulk programmatic access of the data. We built this infrastructure with the goal of adding other transient data (SNe, SLSN, GRBs, etc.) and transient spectroscopy in the future. We plan to use this database infrastructure for a number of future projects including population studies of various transient events, machine learning with Rubin datasets, and light curve modeling. A beta release of the dataset, web application, and API was recently made publicly available (available upon request) and a publication is in preparation.
Current generation surveys will introduce unprecedented challenges in the joint analysis of astronomical datasets. We present Hierarchical Adaptive Tiling Scheme (HATS, formerly referred to as hipscat), an advanced spatial partitioning of large datasets using Parquet storage. LSST Interdisciplinary Network for Collaboration and Computing (LINCC) Frameworks has built the LSDB library on top of the HATS format for efficient and scalable cross-matching and analysis of big datasets, to enable catalog builders to provide archival and future catalogs in HATS format. We have created a global partnership to provide survey data in HATS (including STScl, IPAC, CDS, and S-PLUS).
In this talk, we will showcase: an overview of the tiling scheme and its power in driving massive cross-matches; an introduction to the LSDB API; a demonstration of its use on DP1 and crossmatches to publicly-available HATS catalogs like Gaia, ZTF, and PanStarrs.
The era of gravitational wave (GW) astronomy has yielded many ground-breaking discoveries in astrophysics, where some of the most exciting revelations are only possible when the electromagnetic counterpart or host galaxy is also identified. However, there has been only one such counterpart found to date, GW170817, leaving a large gap in our understanding of compact merger events and their population statistics. In this talk, I will describe how the NASA/IPAC Extragalactic Database (NED) gravitational wave follow-up (NED-GWF) service assists in the searches for counterparts to GW events. This service provides lists of host-galaxy candidates in event volumes and makes them publicly available on the website (and via an API) within minutes of an alert. We have also published an analysis of a subset of NED galaxies with distances out to 1000 Mpc, the local volume sample (NED-LVS), where we have performed additional vetting of objects, characterized the sample properties, derived prioritization metrics, and quantified the completeness. NED-LVS is updated regularly as new measurements are ingested into the NED database, and it is publicly available for download at the website. We are now exploring additional capabilities to support the searches for other multi-messenger events.
In the era of data-intensive research the astronomy community needs to acquire skills to handle increasingly larger and more complex datasets, and to gain access to high-performance computing and analysis tools. In this tutorial we will teach participants how to use data-proximate science platforms to conduct astronomy research. Using the Astro Data Lab science platform and the SPARCL (SPectra Analysis and Retrievable Catalog Lab) service for spectroscopy, participants will learn how to find documentation, information about all of Astro Data Lab's data holdings of over 100 TB of wide-field survey catalogs, 2.5 PB of imagery, and over 30 million spectra from the Dark Energy Spectroscopic Instrument (DESI) and the Sloan Digital Sky Survey (SDSS). The event will feature the record-breaking optical spectroscopy dataset DESI DR1 released in March 2025. We will teach the group in an interactive mode how to use various data services and analysis tools at Astro Data Lab, including how to crossmatch tables, build and submit catalog queries, obtain image cutouts, search for and download spectra, and how to use the Astro Data Lab Jupyter notebook server. The participants will execute and modify science-case example notebooks from various domains of astronomy focusing on data analysis, with assistance available from Astro Data Lab and SPARCL personnel.
This session is open to all interested ADASSx attendees. Participants wishing to follow along interactively are invited to bring a laptop and create an Astro Data Lab user account at https://datalab.noirlab.edu/account/register/
Primary learning objectives:
● Construct SQL queries to query large datasets through a dedicated Jupyter Notebook
server, web-interface, and command-line interface
● Use Astro Data Lab’s X-match web service to crossmatch datasets
● Discover and query for SDSS and DESI spectra with SPARCL
● Create plots with the data obtained to realize the graphical and visualization capabilities
within our notebooks
Coarse tutorial structure:
1) Brief introductory presentation on astronomy science platforms showcasing SPARCL
and the Astro Data Lab science platform and how they can be utilized to explore,
discover, and analyze data easily and efficiently
2) Hands-on tutorial of using SPARCL and Astro Data Lab
a) Using our X-match web interface service to crossmatch with the new DESI DR1
dataset (including a provided test user table, or optionally users can bring their
own data table)
b) Hands-on exercises using available Jupyter notebooks as a starting point,
including: (i) Using SPARCL to retrieve spectra and Data Lab to retrieve images; (ii) Both static and interactive visualization of spectra and images; (iii) Example analysis from catalog data (varied topics)
3) Remaining time for Q&A, troubleshooting, discussion and/or feature requests, etc
The software ecosystem for astronomical spectroscopy is in a transition period. Since 2023, an informal group of spectroscopy software developers and scientists ("SpectroscopyDev") have been meeting regularly to coordinate activities and discuss developments in the field. This group is an outcome of the meeting "Coordinating the Next Generation of Spectroscopic Processing and Analysis Tools", November 2023 (https://noirlab.edu/science/documents/scidoc3085), which itself was an outcome of the "Future of Astronomical Data Infrastructure", February 2023 (https://arxiv.org/abs/2311.04272), also known as the "Flatiron Meeting". We include developers of multi-instrument reduction packages such as PypeIt (https://pypeit.readthedocs.io/en/stable/), specutils (https://specutils.readthedocs.io/en/stable/) and specreduce (https://specreduce.readthedocs.io/en/stable/), as well as more instrument-specific packages such as DRAGONS for Gemini (https://dragons.readthedocs.io/en/stable/) and the desispec for DESI (https://desispec.readthedocs.io/en/latest/). In addition to coordinating software development, we are also documenting a common set of terms (https://specreduce.readthedocs.io/en/latest/terms.html), to assist with cross-project communication. We invite the entire community to participate in more detailed descriptions of our activities, provide feedback and assist in future planning.
ADASSx has been a special northern summer workshop (https://catalina.lpl.arizona.edu/adassx-2025) in a long series of Astronomical Data Analysis Software and Systems conferences, beginning in Tucson, November 6-8, 1991. The next full ADASS conference, the 35th annual, will be held in Görlitz, Germany, November 9-13, 2025 (https://indico.dzastro.de/event/4/). Following ADASS XXXV, there will be the Interoperability Meeting of the International Virtual Observatory Alliance (IVOA).
Astrocook is a Python package designed for the analysis of quasar spectra, with a focus on modelling absorption features from intervening systems. Since its initial release, it has provided a flexible framework where each processing step—continuum fitting, line detection, profile fitting, and more—is implemented as a modular recipe, allowing users to construct reproducible, customizable workflows.
The upcoming v2 release marks a significant evolution of the package. It introduces improved algorithms for continuum normalization and Voigt profile fitting, with enhanced accuracy and stability across a wide range of spectral qualities. In addition, v2 emphasizes interoperability and AI-assisted workflows. Each recipe exposes a clean, standardized API designed for seamless integration with external tools—including large language models and intelligent agents. This shift enables interactive, human-in-the-loop data analysis and paves the way for autonomous workflows driven by AI reasoning.
Astrocook v2 aims to bridge the gap between domain expertise and automation, empowering researchers to process large spectral datasets with greater control and insight. We will present the architecture of the new version, key algorithmic updates, and examples of AI-guided spectral analysis using LLMs.
Galaxy mergers are known to play a pivotal role in shaping the structure and evolution of galaxies across the universe. In this proposed study, I aim to investigate how spiral galaxies transform morphologically after undergoing merger events with companion galaxies. The primary focus will be on understanding the relationship between initial merger conditions and the resulting galaxy structures.
This research will integrate observational data from major surveys such as the Sloan Digital Sky Survey (SDSS) and Hubble Space Telescope (HST) archives. High-resolution astrophysical simulations including IllustrisTNG, EAGLE, GADGET, and RAMSES will be used to analyze the post-merger structural changes in spiral galaxies across different environments. To enhance conceptual understanding and illustrate merger dynamics, Universe Sandbox is employed as a visualization tool.
Key factors such as mass ratios, angular momentum, gas content, and collision geometry will be explored and correlated with the resulting galaxy morphology: elliptical, lenticular, irregular, or transitional. Environmental influences like local galaxy density and cluster membership will also be examined for their effect on merger dynamics.
The study will also investigate the behavior of central black holes during galaxy mergers. As galaxies collide, their supermassive black holes are expected to interact and eventually merge. This process offers a valuable opportunity to study the comparative strength of each black hole including how much matter, gas, and nearby stellar material may be pulled into them. Such mergers allow us to determine which black hole is more massive, how their spins affect the remnant, and what physical effects emerge during their coalescence. These extreme interactions not only reshape galactic cores but also generate gravitational waves, serving as key targets for detectors like LISA.
To quantify morphological evolution, I will use Python-based tools like AstroPy, NumPy, Pandas, and Matplotlib, alongside structural indices such as Gini-M20 and the CAS (Concentration, Asymmetry, Smoothness) metrics.
This proposed research seeks to contribute to our understanding of how galaxy mergers influence not only visible matter but also the distribution of dark matter within clusters. Ultimately, the findings could offer valuable insights into the broader processes of galaxy formation and cosmic structure evolution.
Keywords: Galaxy Mergers, Spiral Galaxies, Galaxy Morphology, Astrophysical Simulations, Cosmic Structure, Dark Matter Dynamics, Supermassive Black Hole Mergers.
Recent surveys, such as the Sloan Digital Sky Survey (SDSS), have discovered thousands of white dwarfs, low-mass non-fusing stellar remnants. Because these white dwarfs (WDs) radiate away their energy as they cool, changing their spectral features, their spectral characterization and evolution serve as "cosmic clocks", used to date stellar populations and stellar formation history. Emerging studies have applied machine learning techniques to analyze this population; however, these methods are often hindered due to the large number of low-resolution WD spectra and the computational intensity of atmospherically modeled synthetic spectra. This work introduces WD-SYNSPEC, a parameterized framework to generate noisy synthetic WD spectra in the optical range based on spectral type, effective temperature, and surface gravity. Able to simulate multiple spectral types, WD-SYNSPEC helps solve both the forward and inverse modeling problems in WD spectroscopy by (1) creating a sample of unlabeled spectra and (2) creating a population of spectra on which existing spectral fitting methods can be applied. WD-SYNSPEC functions by generating a blackbody spectrum, introducing spectral lines as least-squares optimized Voigt profiles (via median temperature-binned spectra), adjusting for line broadening as a function of surface gravity and temperature, and introducing Gaussian noise and adjusting spectral resolution. Although WD-SYNSPEC does not consider factors such as convective features or non-equilibrium chemistries, it leverages these trade-offs in its extensibility and ability to generate populations of synthetic spectra rapidly due to its low time complexity. These methods demonstrate WD-SYNSPEC’s effectiveness and possible use within a future, more complex deep-learning-based modeling and classification pipeline.
The Astro Data Lab team at NOIRLab's Community Science and Data Center has recently deployed a new integrated Data Lab web portal (https://datalab.noirlab.edu). Its development was informed by several years of experience in operating a science platform with thousands of users, and feedback from volunteer testers. The portal provides capabilities to explore catalog and image holdings, hosts services for queries, remote file and database storage, supports SIA image search and cutout services, and fuses cleanly our data center holdings with the user's own datasets. Integration of a catalog cross-matching service, and of the AladinLite visualizer are the next development steps. We present the overall vision and execution of the new web portal, highlighting its capabilities, and the reasons behind some technical and design decisions.
In this study, we investigate how a machine learning algorithm, Uniform Manifold Approximation and Projection (UMAP), classifies and gives insight about active galactic nuclei (AGN) and quasar activity of over 200,000 low-redshift galaxies with spectra from the Sloan Digital Sky Survey. Using traditional broad-line and narrow-line diagnostics on the sample, we find that AGN (broad line and narrow line) and quasars occupy distinct branches on the UMAP space. We further investigate trends with dust reddening and AGN luminosity to understand whether the algorithm reveals paths within the broader galaxy evolution context. Lastly, we create a new UMAP projection for the AGN and quasars alone to search for trends more closely related to black hole growth. This work demonstrates the power of unsupervised ML tools to reveal physically meaningful patterns in large astronomical datasets, offering new ways to study black hole growth and AGN diversity.
By utilizing the public datasets archived at DARTS, which ISAS/JAXA operates, we investigate the systematic attitude difference between the International Space Station (ISS) and the Monitor of All-Sky X-ray Image (MAXI) installed on the exposed part of the Japanese Experiment Module on the ISS. We have found that the attitude difference is the smallest around the longest axis of the ISS and greater in the two other perpendicular axes. We applied the Lomb-Scargle periodicity detection algorithm and detected short-term (~92 minutes) and long-term (~60 days) periodic variations in the attitude differences. The former is likely related to the orbital period, and the latter is to the angle variation between the Sun and the ISS orbital plane. Current results are expected to provide insights into the structure and distortion of the ISS body.
Quasar accretion disks, predicted by the standard thin disk theory to emit optical continuum from regions spanning light‐hours, remain challenging to resolve via traditional reverberation mapping (RM) techniques limited by: 1. Long, daily to monthly observational cadence, and 2. Oversmoothing in damped random walk (DRW) time-series models. We present a 6-month, high-cadence (180s exposure, 3-5 hours per night), cost-effective photometric RM campaign using the 24-inch Keeler Telescope at the Allegheny Observatory, monitoring 3 circumpolar quasars in g′ and r′ bands. Preprocessing pipeline incorporated differential photometry and 10-min binned spline interpolation, yielding <0.01 mag precision. By developing an adaptive Bayesian RM model, we achieved hourly-scale lag sensitivity through a truncated DRW kernel, a mixture density network, and Markov chain Monte Carlo sampling. On 15,000 simulated optical light curves with known, short time lags, our model correctly predicted 71.17% of lags within 0.2 days, outperforming the JAVELIN RM model by 17.92%. For quasar PG0804+761, we revealed inter-band time lags of 0.61 ± 0.24 days, corresponding to disk radii scaling with the thin disk theory. Applied to 13 publicly available daily-cadence quasar light curves, our model recovered time lags from 0.02 to 1.23 days, consistent with thin disk estimations, and achieved reduced uncertainties compared to existing studies. In essence, we place promising constraints on light-hour-scale accretion disk radii predicted by the standard thin disk model. Our sub-meter telescope’s photometric quality is comparable to space-based surveys, and our model enables robust hour-scale reverberation mapping critical for next-generation active galactic nuclei research.