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Julien Peloton

Publications and source records attributed to Julien Peloton.

At least 19 recordsLinked to original sources

A new era for multi-wavelength studies of blazars with Rubin and the CTAO

Both the Rubin Observatory and the first telescopes of the CTAO will be collecting data by 2026, marking a new era in optical and gamma-ray astronomy. Compared to predecessors, their enhanced sensitivity will extend extragalactic observations to a redshift of at least 2.5. This advancement offers insights into non-thermal astrophysical sources, particularly blazars. The 3-night cadence monitoring with Rubin, in one of its six filters, will produce blazar light curves that, when combined with targeted in-depth observations from the CTAO, could help distinguish acceleration and radiative models. Existing data from the ZTF and Fermi-LAT, though less sensitive, offer insights into what Rubin and the CTAO may achieve. However, the real-time processing of the immense data stream coming from Rubin/LSST presents a major challenge. Addressing this challenge is the work of brokers such as Fink, which we develop for multi-messenger astrophysics. Fink processes data in real-time before sending relevant information to other observatories like the CTAO. In this contribution, we present how we characterize the optical variability of blazars that emit in the gamma-ray range using the ZTF, with timescales spanning from the intra-night to multi-years. We identify properties in the resulting parameter space that could not only enable the identification of blazar-like sources, but also the characterization of the continuum of states. We describe our fast identification of transitions from one state to another, enabling the trigger of observations in the gamma-ray band and follow-up spectroscopic observations. Finally, we review the communication channel we set from the ZTF to the CTAO via Fink for blazars and discuss its outlook in light of the Rubin Observatory. This method is also applicable to other astrophysical sources and helps lay the groundwork for a fruitful era for time-domain astronomy.

astro-ph.HE

Early Identification of Optical Tidal Disruption Events: A science module for the Fink broker

The detection of tidal disruption events (TDEs) is one of the key science goals of large optical time-domain surveys such as the Zwicky Transient Facility (ZTF) and the upcoming Vera C. Rubin Observatory Legacy Survey of Space and Time. However, identifying TDEs in the vast alert streams produced by these surveys requires automated and reliable classification pipelines that can select promising candidates in real time. We developed a module within the Fink alert broker to identify TDEs during their rising phase. It was built to autonomously operate within the ZTF alert stream, producing a list of candidates every night and enabling spectral and multi-wavelength follow-up near peak brightness. All rising alerts are submitted to selection cuts and feature extraction using the Rainbow multi-band lightcurve fit. Best-fit values were used as input to train an XGBoost classifier with the goal of identifying TDEs. The training set was constructed using ZTF observations for objects with available classification in the Transient Name Server. Finally, candidates with high enough probability were visually inspected. The classifier achieves 76% recall, indicating strong performance in early-phase identification, despite the limited available information before peak. We show that, out of the known TDEs that pass the selection cuts, half of them are flagged as TDE before halfway in their rise, proving the feasibility of early classification. Additionally, new candidates were identified by applying the classifier on archival data, including a likely repeated TDE and some potential TDEs occurring in active galaxies. The module is implemented into the Fink alert processing framework, reporting each night a small number of candidates to dedicated communication channels through a user-friendly interface, for manual vetting and potential follow-up.

astro-ph.HE

The rate of satellite glints in ZTF and LSST sky surveys

We assess the impact of satellite glints -- rapid flashes produced by reflections of a sunlight from flat surfaces of rotating satellites -- on current and future deep sky surveys such as the ones conducted by the Zwicky Transient Facility (ZTF) and the Vera C. Rubin Observatory upcoming Legacy Survey of Space and Time (LSST). In addition to producing a large number of streaks polluting the images, artificial satellites and space debris also generate great amount of false point-source alerts hindering the search for new rapid astrophysical transients. To investigate the extent of this problem, we perform an analysis of isolated single frame events detected by ZTF in more than three years of its operation, and, using three different methods, assess the fraction of them related to artificial satellites to be at least 20\%. The satellites causing them occupy all kinds of orbits around the Earth, and the duration of flashes produced by their rotation is from a fraction of a second down to milliseconds, with mean all-sky rate of up to 80,000 per hour.

astro-ph.IM

A data science platform to enable time-domain astronomy

SkyPortal is an open-source software package designed to efficiently discover interesting transients, manage follow-up, perform characterization, and visualize the results. By enabling fast access to archival and catalog data, cross-matching heterogeneous data streams, and the triggering and monitoring of on-demand observations for further characterization, a SkyPortal-based platform has been operating at scale for 2 yr for the Zwicky Transient Facility Phase II community, with hundreds of users, containing tens of millions of time-domain sources, interacting with dozens of telescopes, and enabling community reporting. While SkyPortal emphasizes rich user experiences (UX) across common frontend workflows, recognizing that scientific inquiry is increasingly performed programmatically, SkyPortal also surfaces an extensive and well-documented API system. From backend and frontend software to data science analysis tools and visualization frameworks, the SkyPortal design emphasizes the re-use and leveraging of best-in-class approaches, with a strong extensibility ethos. For instance, SkyPortal now leverages ChatGPT large-language models (LLMs) to automatically generate and surface source-level human-readable summaries. With the imminent re-start of the next-generation of gravitational wave detectors, SkyPortal now also includes dedicated multi-messenger features addressing the requirements of rapid multi-messenger follow-up: multi-telescope management, team/group organizing interfaces, and cross-matching of multi-messenger data streams with time-domain optical surveys, with interfaces sufficiently intuitive for the newcomers to the field. (abridged)

astro-ph.IM

Repeating Outbursts from the Young Stellar Object Gaia23bab (= SPICY 97589)

The light curve of Gaia23bab (= SPICY 97589) shows two significant ($ΔG>2$ mag) brightening events, one in 2017 and an ongoing event starting in 2022. The source's quiescent spectral energy distribution indicates an embedded ($A_V>5$ mag) pre-main-sequence star, with optical accretion emission and mid-infrared disk emission. This characterization is supported by the source's membership in an embedded cluster in the star-forming cloud DOBASHI 1604 at a distance of $900\pm45$~pc. Thus, the brightening events are probable accretion outbursts, likely of EX Lup-type.

astro-ph.SR

The Tiny Time-series Transformer: Low-latency High-throughput Classification of Astronomical Transients using Deep Model Compression

A new golden age in astronomy is upon us, dominated by data. Large astronomical surveys are broadcasting unprecedented rates of information, demanding machine learning as a critical component in modern scientific pipelines to handle the deluge of data. The upcoming Legacy Survey of Space and Time (LSST) of the Vera C. Rubin Observatory will raise the big-data bar for time-domain astronomy, with an expected 10 million alerts per-night, and generating many petabytes of data over the lifetime of the survey. Fast and efficient classification algorithms that can operate in real-time, yet robustly and accurately, are needed for time-critical events where additional resources can be sought for follow-up analyses. In order to handle such data, state-of-the-art deep learning architectures coupled with tools that leverage modern hardware accelerators are essential. We showcase how the use of modern deep compression methods can achieve a $18\times$ reduction in model size, whilst preserving classification performance. We also show that in addition to the deep compression techniques, careful choice of file formats can improve inference latency, and thereby throughput of alerts, on the order of $8\times$ for local processing, and $5\times$ in a live production setting. To test this in a live setting, we deploy this optimised version of the original time-series transformer, t2, into the community alert broking system of FINK on real Zwicky Transient Facility (ZTF) alert data, and compare throughput performance with other science modules that exist in FINK. The results shown herein emphasise the time-series transformer's suitability for real-time classification at LSST scale, and beyond, and introduce deep model compression as a fundamental tool for improving deploy-ability and scalable inference of deep learning models for transient classification.

astro-ph.IM

Finding active galactic nuclei through Fink

We present the Active Galactic Nuclei (AGN) classifier as currently implemented within the Fink broker. Features were built upon summary statistics of available photometric points, as well as color estimation enabled by symbolic regression. The learning stage includes an active learning loop, used to build an optimized training sample from labels reported in astronomical catalogs. Using this method to classify real alerts from the Zwicky Transient Facility (ZTF), we achieved 98.0% accuracy, 93.8% precision and 88.5% recall. We also describe the modifications necessary to enable processing data from the upcoming Vera C. Rubin Observatory Large Survey of Space and Time (LSST), and apply them to the training sample of the Extended LSST Astronomical Time-series Classification Challenge (ELAsTiCC). Results show that our designed feature space enables high performances of traditional machine learning algorithms in this binary classification task.

astro-ph.IM

Fink: early supernovae Ia classification using active learning

We describe how the Fink broker early supernova Ia classifier optimizes its ML classifications by employing an active learning (AL) strategy. We demonstrate the feasibility of implementation of such strategies in the current Zwicky Transient Facility (ZTF) public alert data stream. We compare the performance of two AL strategies: uncertainty sampling and random sampling. Our pipeline consists of 3 stages: feature extraction, classification and learning strategy. Starting from an initial sample of 10 alerts (5 SN Ia and 5 non-Ia), we let the algorithm identify which alert should be added to the training sample. The system is allowed to evolve through 300 iterations. Our data set consists of 23 840 alerts from the ZTF with confirmed classification via cross-match with SIMBAD database and the Transient name server (TNS), 1 600 of which were SNe Ia (1 021 unique objects). The data configuration, after the learning cycle was completed, consists of 310 alerts for training and 23 530 for testing. Averaging over 100 realizations, the classifier achieved 89% purity and 54% efficiency. From 01/November/2020 to 31/October/2021 Fink has applied its early supernova Ia module to the ZTF stream and communicated promising SN Ia candidates to the TNS. From the 535 spectroscopically classified Fink candidates, 459 (86%) were proven to be SNe Ia. Our results confirm the effectiveness of active learning strategies for guiding the construction of optimal training samples for astronomical classifiers. It demonstrates in real data that the performance of learning algorithms can be highly improved without the need of extra computational resources or overwhelmingly large training samples. This is, to our knowledge, the first application of AL to real alerts data.

astro-ph.IM

Impact of satellite glints on the transient science on ZTF scale

Thousands of active artificial objects are orbiting around Earth along with much more non-operational ones -- derelict satellites or rocket bodies, collision debris, or spacecraft payloads, significant part of them being uncatalogued. They all impact observations of the sky by ground-based telescopes by producing a large number of streaks polluting the images, as well as generating false alerts hindering the search for new astrophysical transients. While the former threat for astronomy is widely discussed nowadays in regard of rapidly growing satellite mega-constellations, the latter one -- false transients -- still lacks attention on the similar level. In this work we assess the impact of satellite glints -- rapid flashes produced by reflections of a sunlight from flat surfaces of rotating satellites -- on current and future deep sky surveys such as the ones conducted by the Zwicky Transient Facility (ZTF) and the Vera Rubin Observatory Legacy Survey of Space and Time (LSST). For that, we propose a simple routine that detects, in a single exposure, a series of repeated flashes along the trajectories of otherwise invisible satellites, and describe its implementation in FINK alert broker. Application of the routine to ZTF alert stream revealed about 73,000 individual events polluting 3.6\% of all ZTF science images between November 2019 and December 2021 and linked to more than 300 different glinting satellites on all kinds of orbits, from low-Earth up to geostationary ones. The timescales of individual flashes are as short as $0.1$--$10^{-3}$ seconds, with instant brightness of 4--14 magnitudes, peak amplitudes of at least 2--4 magnitudes, and generally complex temporal patterns of flashing activity. We expect LSST to see much more such satellite glints of even larger amplitudes due to its better sensitivity.

astro-ph.IM

Instrumental systematics biases in CMB lensing reconstruction: a simulation-based assessment

Weak gravitational lensing of the cosmic microwave background (CMB) is an important cosmological tool that allows us to learn about the structure, composition and evolution of the Universe. Upcoming CMB experiments, such as the Simons Observatory (SO), will provide high-resolution and low-noise CMB measurements. We consider the impact of instrumental systematics on the corresponding high-precision lensing reconstruction power spectrum measurements. We simulate CMB temperature and polarization maps for an SO-like instrument and potential scanning strategy, and explore systematics relating to beam asymmetries and offsets, boresight pointing, polarization angle, gain drifts, gain calibration and electric crosstalk. Our analysis shows that the majority of the biases induced by the systematics we modeled are below a detection level of $\sim 0.6σ$. We discuss potential mitigation techniques to further reduce the impact of the more significant systematics, and pave the way for future lensing-related systematics analyses.

astro-ph.CO

Simulating instrumental systematics of Cosmic Microwave Background experiments with s4cmb

The observation of cosmic microwave background (CMB) anisotropies is one of the key probes of physical cosmology. The weak nature of this signal has driven the construction of increasingly complex and sensitive experiments observing the sky at multiple frequencies with thousands of polarization sensitive detectors. Given the high sensitivity of such experiments, instrumental systematic effects can become the limiting factor towards the full scientific exploitation of their data. In this paper we present s4cmb (Systematics for CMB), a Python package designed to simulate raw data streams in time domain of modern CMB experiments based on bolometric technology, and to inject in these realistic instrumental systematics effects. The aim of the package is to help assessing the contamination due to instrumental systematic effects on real data, to guide the design of future instruments, as well as to increase the realism of simulated data sets required in the development of accurate data analysis methods.

astro-ph.IM

The LSST DESC DC2 Simulated Sky Survey

We describe the simulated sky survey underlying the second data challenge (DC2) carried out in preparation for analysis of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) by the LSST Dark Energy Science Collaboration (LSST DESC). Significant connections across multiple science domains will be a hallmark of LSST; the DC2 program represents a unique modeling effort that stresses this interconnectivity in a way that has not been attempted before. This effort encompasses a full end-to-end approach: starting from a large N-body simulation, through setting up LSST-like observations including realistic cadences, through image simulations, and finally processing with Rubin's LSST Science Pipelines. This last step ensures that we generate data products resembling those to be delivered by the Rubin Observatory as closely as is currently possible. The simulated DC2 sky survey covers six optical bands in a wide-fast-deep (WFD) area of approximately 300 deg^2 as well as a deep drilling field (DDF) of approximately 1 deg^2. We simulate 5 years of the planned 10-year survey. The DC2 sky survey has multiple purposes. First, the LSST DESC working groups can use the dataset to develop a range of DESC analysis pipelines to prepare for the advent of actual data. Second, it serves as a realistic testbed for the image processing software under development for LSST by the Rubin Observatory. In particular, simulated data provide a controlled way to investigate certain image-level systematic effects. Finally, the DC2 sky survey enables the exploration of new scientific ideas in both static and time-domain cosmology.

astro-ph.IM

DESC DC2 Data Release Note

In preparation for cosmological analyses of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), the LSST Dark Energy Science Collaboration (LSST DESC) has created a 300 deg$^2$ simulated survey as part of an effort called Data Challenge 2 (DC2). The DC2 simulated sky survey, in six optical bands with observations following a reference LSST observing cadence, was processed with the LSST Science Pipelines (19.0.0). In this Note, we describe the public data release of the resulting object catalogs for the coadded images of five years of simulated observations along with associated truth catalogs. We include a brief description of the major features of the available data sets. To enable convenient access to the data products, we have developed a web portal connected to Globus data services. We describe how to access the data and provide example Jupyter Notebooks in Python to aid first interactions with the data. We welcome feedback and questions about the data release via a GitHub repository.

astro-ph.CO

Fink, a new generation of broker for the LSST community

Fink is a broker designed to enable science with large time-domain alert streams such as the one from the upcoming Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). It exhibits traditional astronomy broker features such as automatised ingestion, annotation, selection and redistribution of promising alerts for transient science. It is also designed to go beyond traditional broker features by providing real-time transient classification which is continuously improved by using state-of-the-art Deep Learning and Adaptive Learning techniques. These evolving added values will enable more accurate scientific output from LSST photometric data for diverse science cases while also leading to a higher incidence of new discoveries which shall accompany the evolution of the survey. In this paper we introduce Fink, its science motivation, architecture and current status including first science verification cases using the Zwicky Transient Facility alert stream.

astro-ph.IM

FITS Data Source for Apache Spark

We investigate the performance of Apache Spark, a cluster computing framework, for analyzing data from future LSST-like galaxy surveys. Apache Spark attempts to address big data problems have hitherto proved successful in the industry, but its use in the astronomical community still remains limited. We show how to manage complex binary data structures handled in astrophysics experiments such as binary tables stored in FITS files, within a distributed environment. To this purpose, we first designed and implemented a Spark connector to handle sets of arbitrarily large FITS files, called spark-fits. The user interface is such that a simple file "drag-and-drop" to a cluster gives full advantage of the framework. We demonstrate the very high scalability of spark-fits using the LSST fast simulation tool, CoLoRe, and present the methodologies for measuring and tuning the performance bottlenecks for the workloads, scaling up to terabytes of FITS data on the Cloud@VirtualData, located at Université Paris Sud. We also evaluate its performance on Cori, a High-Performance Computing system located at NERSC, and widely used in the scientific community.

astro-ph.IM

Performance of a continuously rotating half-wave plate on the POLARBEAR telescope

A continuously rotating half-wave plate (CRHWP) is a promising tool to improve the sensitivity to large angular scales in cosmic microwave background (CMB) polarization measurements. With a CRHWP, single detectors can measure three of the Stokes parameters, $I$, $Q$ and $U$, thereby avoiding the set of systematic errors that can be introduced by mismatches in the properties of orthogonal detector pairs. We focus on the implementation of CRHWPs in large aperture telescopes (i.e. the primary mirror is larger than the current maximum half-wave plate diameter of $\sim$0.5 m), where the CRHWP can be placed between the primary mirror and focal plane. In this configuration, one needs to address the intensity to polarization ($I{\rightarrow}P$) leakage of the optics, which becomes a source of 1/f noise and also causes differential gain systematics that arise from CMB temperature fluctuations. In this paper, we present the performance of a CRHWP installed in the POLARBEAR experiment, which employs a Gregorian telescope with a 2.5 m primary illumination pattern. The CRHWP is placed near the prime focus between the primary and secondary mirrors. We find that the $I{\rightarrow}P$ leakage is larger than the expectation from the physical properties of our primary mirror, resulting in a 1/f knee of 100 mHz. The excess leakage could be due to imperfections in the detector system, i.e. detector non-linearity in the responsivity and time-constant. We demonstrate, however, that by subtracting the leakage correlated with the intensity signal, the 1/f noise knee frequency is reduced to 32 mHz ($\ell \sim$39 for our scan strategy), which is very promising to probe the primordial B-mode signal. We also discuss methods for further noise subtraction in future projects where the precise temperature control of instrumental components and the leakage reduction will play a key role.

astro-ph.IM

Full covariance of CMB and lensing reconstruction power spectra

CMB and lensing reconstruction power spectra are powerful probes of cosmology. However they are correlated, since the CMB power spectra are lensed and the lensing reconstruction is constructed using CMB multipoles. We perform a full analysis of the auto- and cross-covariances, including polarization power spectra and minimum variance lensing estimators, and compare with simulations of idealized future CMB-S4 observations. Covariances sourced by fluctuations in the unlensed CMB and instrumental noise can largely be removed by using a realization-dependent subtraction of lensing reconstruction noise, leaving a relatively simple covariance model that is dominated by lensing-induced terms and well described by a small number of principal components. The correlations between the CMB and lensing power spectra will be detectable at the level of $\sim 5σ$ for a CMB-S4 mission, and neglecting those could underestimate some parameter error bars by several tens of percent. However we found that the inclusion of external priors or data sets to estimate parameter error bars can make the impact of the correlations almost negligible.

astro-ph.CO

Making maps of Cosmic Microwave Background polarization for B-mode studies: the POLARBEAR example

Analysis of cosmic microwave background (CMB) datasets typically requires some filtering of the raw time-ordered data. Filtering is frequently used to minimize the impact of low frequency noise, atmospheric contributions and/or scan synchronous signals on the resulting maps. In this work we explicitly construct a general filtering operator, which can unambiguously remove any set of unwanted modes in the data, and then amend the map-making procedure in order to incorporate and correct for it. We show that such an approach is mathematically equivalent to the solution of a problem in which the sky signal and unwanted modes are estimated simultaneously and the latter are marginalized over. We investigate the conditions under which this amended map-making procedure can render an unbiased estimate of the sky signal in realistic circumstances. We then study the effects of time-domain filtering on the noise correlation structure in the map domain, as well as impact it may have on the performance of the popular pseudo-spectrum estimators. We conclude that although maps produced by the proposed estimators arguably provide the most faithful representation of the sky possible given the data, they may not straightforwardly lead to the best constraints on the power spectra of the underlying sky signal and special care may need to be taken to ensure this is the case. By contrast, simplified map-makers which do not explicitly correct for time-domain filtering, but leave it to subsequent steps in the data analysis, may perform equally well and be easier and faster to implement. We focus on polarization-sensitive measurements targeting the B-mode component of the CMB signal and apply the proposed methods to realistic simulations based on characteristics of an actual CMB polarization experiment, POLARBEAR.

astro-ph.IM