Fink-Brazil Workshop

May 6, 2024, 8:45 AM → May 10, 2024, 5:00 PM America/Sao_Paulo

Centro Brasileiro de Pesquisas Fisicas

Anais Moller (Swinburne University), Bernardo Fraga (CBPF), Clecio R. Bom (CBPF), Emille Ishida (CNRS/LPC-Clermont), Gustavo Cabral (CBPF), Julien Peloton (CNRS-IJCLab), Luidhy Santana-Silva (CBPF)

Enabling Astronomical Transient discoveries in the Rubin era: the Fink-Brazil Workshop

 

This event aims to bring together researchers interested in exploring the potential applications of Fink data, both in the context of the Zwicky Transient Facility (ZTF), as well as the upcoming Vera Rubin Observatory Large Survey of Space and Time (LSST). 

We aim to create connections between domain experts, machine learning specialists, observational facilities and the Fink community. 

The program will include keynote speakers and contributed talks, posters and tutorials that will help prepare the local community for the arrival of LSST data, maximizing the scientific output of the alert stream. 

Researchers from academia as well as industry are welcome to register and submit contributions.

It will be possible to follow the activities remotely. All contributed talks will be delivered in person.

This event is supported by FINEP,  FAPERJ and CNRS-IN2P3.

 

FinkBrazil_poster.pdf

Monday, May 6

CBPF: Welcome

1 Introduction to CBPF

Speaker: Marcio Albuquerque

Keynote speakers

2 Introduction to Fink

In this talk I will present Fink, an astronomy broker specifically designed for LSST. Fink is based on high-end technology and designed for fast and efficient analysis of big data streams. It has been chosen as one of the official LSST brokers who will receive the full data stream. I will highlight the state-of-the-art machine learning techniques used to generate early classification scores for a variety of time-domain phenomena, including supernovae, kilonovae, AGNs, young stellar objects, among many others. I will also describe the current efforts being developed in Brazil that will enable easy access to LSST the data stream through Fink, and discuss the possibility to develop tailored filters and science modules for other applications. In combination with other efforts already developed within the Fink community, this collaboration has the potential to boost scientific outcomes as soon as LSST comes online.

Speaker: Dr Emille Ishida (CNRS/LPC-Clermont)

fink_brazil_introduction.pdf

3 Gravitational Waves and Time Domain Astrophysics in Brazilian Center for Physics Research

Speaker: Clecio de Bom (CBPF)

ClecioBom_FINK2024.pptx

Contributed

4 Transient classifiers in Fink: Lessons learned from the ELAsTiCC dataset in preparation for LSST

The upcoming Vera Rubin observatory and its Legacy Survey of Space and Time (LSST) are expected to detect of the order of ten million alerts per night, which are then distributed to community brokers whose task is to filter, classify and redistribute this data to selected scientific communities. Due to the sheer amount of data, machine learning (ML) algorithms are expected to play a key role in the classification of photometric light curves. We present the classifiers developed within Fink in preparation for LSST data, using the simulations from the Extended LSST Astronomical Time-series Classification Challenge (ELAsTiCC). They include tree-based models with tailored feature extraction and deep learning models, both for binary and multi-class classification. In Particular, the CBPF Alert Transient Search (CATS), based on state-of-the-art deep learning models combined with hyperparameter tuning, will be presented. Furthermore, we discuss how the different algorithms can be combined to form hierarchical classifiers, or to improve the general results.

Speaker: Bernardo Fraga (CBPF)

fink_brazil_Fraga.pdf

CBPF

5 Logistics

Speaker: Dr Emille Ishida (CNRS/LPC-Clermont)

Logistics

10:30 AM Coffee Break

Contributed

6 Automatic detection of hostless transients in FINK

Almost all astronomical transients are hosted by a galaxy. Hostless transients are rare and have been associated with events that probe the extremes of physical mechanisms. Early identification based on their hostless characteristics would allow rapid follow-up and consequently better datasets for modelling and interpretation. Most apparently hostless events are not in fact hostless but their hosts are fainter than the limiting magnitude of the discovery survey. Thus, detecting hostless events could also be used as an indirect mechanism to discover low surface brightness galaxies. Only a few true hostless events have been found to date resulting in an illustrative application for automatic anomaly detection techniques. We developed a pipeline that implements fairly simple anomaly detection methods built upon the experience of domain knowledge experts. We aim to detect possible hostless transients using large surveys' alerts. We plan on implementing this pipeline into the FINK broker to provide hostless candidates in real-time. In this talk, I will describe the science case and highlight features that should be considered when dealing with real astronomical alerts. I will also show examples of a few hostless candidates and discuss how they can be used as templates to search for similar sources.

Speaker: Mrs Lilianne Nakazono (IAG-USP)

NakazonoLilianne_06May24.pdf

7 Lomikel - Graph on the Sky

This presentation introduces an innovative approach for the storage, organization, access, analysis, and visualization of Fink data. Rather than treating Fink data as isolated entities, we propose representing it as a graph of interconnected objects, comprising sources and alerts. This novel organization enables the capture of important collective features, providing a simple and intuitive framework for classification and analysis of otherwise concealed relationships. Graphs serve as a powerful tool for implementing sophisticated algorithms and facilitating the identification of clustered or isolated (unique) alert sources. Moreover, interconnected alerts create a natural environment for the application of machine learning algorithms. Concrete, reusable examples will be demonstrated to show the practical application of these possibilities. The presentation will also highlight the seamless integration of Lomikel within the Fink ecosystem, showing its functionality both through the command-line interface and standard web services. Additionally, insights into the relationship between integrated data storage, utilizing graph databases alongside traditional technologies, and common graph analysis frameworks will be discussed. The presentation will conclude by exploring possibilities for extending this integration to other potentially useful tools, providing a comprehensive overview of the Lomikel framework's capabilities within the Fink data ecosystem.

Speaker: Julius Hrivnac (IJCLab)

Graphs in the sky

8 Exploring the potential for Active Galactic Nuclei (AGN) emission line delays using LSST

Active Galactic Nuclei (AGN) are variable sources, and analyzing the time delay of the strong broad optical emission lines relative to the underlying continuum serves as a crucial tool to investigate this important trait. This method aids in measuring black hole masses, and deciphering the structure of the Broad Line Region (BLR) responsible for these emission lines, which can be extended to test existing cosmological models. The relationship between an AGN's absolute luminosity and the measured time delay contributes to these investigations. To facilitate time delay measurements, we have developed a pipeline that simulates the efficiency of such measurements using the survey strategies for the Vera C. Rubin Observatory's Legacy Survey of Space and Time. This code incorporates simulated light curves, incorporating strong emission lines (e.g., Hbeta, MgII, CIV), iron pseudo-continuum in the optical and UV bands, and contamination from starlight. By identifying optimal bands representing the continuum and dominant lines for a given redshift, the code enables the recovery of time delay under any available LSST cadences. Simulations are conducted for both the main survey and the Deep Drilling Fields (DDFs) using representative cadence strategies. The pipeline is modular, and multi-faceted, and can be used to determine time delays from actual data (e.g., ZTF), and can be easily extended to forthcoming surveys. I will highlight the salient features of the pipeline and discuss relevant applications and plans in anticipation of the first light of the Observatory.

Speaker: Dr Swayamtrupta Panda (Laboratorio Nacional de Astrofisica, Itajuba)

SPanda_Day1_LSST-Fink_Workshop.pdf

12:05 PM Lunch

Keynote speakers

9 Gravitational lensing in the era of brokers

The new era of wide-field time-domain surveys will open a new window to gravitational lensing phenomena, leading to the discovery of strongly lensed transients and enabling the discovery of microlensing events across the celestial sphere. For example, the Vera Rubin Legacy Survey of Space and Time (LSST) is expected to find on the order of a hundred strongly lensed supernovae, which will enable precise and independent measurements of the expansion rate of the Universe, shedding light on the so-called "Hubble tension". On the other hand, the detection of microlensing along new directions in the sky will help to build a census of the distribution of compact objects across the galaxy, including planets, black-holes and dark matter in the form of condensed structures. To fully exploit the applications of the strongly lensed transients as well as the transients from lensing (i.e. microlensing) requires the real time identification of these phenomena. In the case of LSST, this means spotting the few relevant events among the expected ten million alerts per day, which is typically a task delegated to the brokers. In this talk we discuss some of the expectations for lensing in LSST, the needs for real time follow-up observations, and ongoing work to help identify the strongly lensed transients and the microlensing events in LSST data.

Speaker: Dr Martin Makler (UNSAM/CBPF)

FINK_Makler.pdf

Contributed

10 Building a lookup table for strongly lensed transients and prospects for FINK

Strong lensing time-delays provide an independent method for measuring $H_0$ and probing cosmology. Although multiply imaged transients are very rare, ongoing and upcoming projects in wide-field surveys are expected to discover thousands of such systems, especially in the Vera Rubin Observatory Legacy Survey of Space and Time (LSST) survey. As a preparation for this unprecedented amount of data, our group has carried out an extensive compilation of thousands of strong lensing candidates in the footprint of current ground-based wide-field surveys with sub-arcsecond resolution. We refer to this comprehensive compilation of gravitational lenses as “the Last Stand Before Rubin” (LaStBeRu). This compilation readily provides a lookup table to identify potential strongly lensed transiens. In addition, we are carrying out an extensive modeling of the best systems in the sample using a custom made pipeline based on the Source Lens and Mass (SLaM) pipelines which makes use of features of the publicly available open-source \texttt{PyAutoLens} software. We have derived modeling results for around 150 strong lensing systems which can be used as a rich reference database for future strongly lensed transients. To the best of our knowledge, this is one of the largest uniformly modeled strong lensing samples in current ground-based imaging data. The LaStBeRu sample has been matched with FINK ZTF identified supernovae, yielding dozens transients that might be strongly lensed, some of which could potentially be used to derive time-delay measurements with our modeling pipeline. In this contribution, we will present preliminary results on the modeling of cross-matched systems, including time-delay predictions, for some of them.

Speaker: Mr Joao Paulo Franca (CBPF)

joaofrancaFinkRio2024.pdf

11 Rapid automated detection and modeling of strong lenses for time delay cosmography and supernova studies with LSST tansients

Thanks to the large sky coverage and the high cadence, the Rubin Observatory Legacy Survey of Space and Time (LSST) will reveal on the order of 100,000 strongly lensed galaxy-scale systems and revolutionize transient studies. With dedicated neural network classifiers trained on realistic mock images, we will be able to analyze all LSST images, and detect these lens candidates. By cross-matching them with all transients detected by LSST on a daily basis, we expect to detect a significant number of strongly lensed supernovae (slSNe). These very rare systems offer promising avenues in cosmology such as the direct measurement of the Hubble constant H0 completely independently from other probes. This allows us to assess the current tension on the H0 value, and the possible need for new physics. Furthermore, these slSNe help to constrain the supernova progenitor scenarios by facilitating follow-up observations of the counter images in the first hours after explosion. In my talk, I will present new achievements of the HOLISMOKES collaboration developing and testing required tools for this procedure. Specifically, I will highlight our various recent developments in lens classification, as well as the automation of strong-lens modeling with a residual neural network that brings the runtime from weeks down to fractions of a second, making the crucial follow-up scheduling immediately after the transient detection possible. The neural networks are trained on realistic images and tested on real systems from the Hyper Suprime Cam, which images are expected to match those from LSST very well. With these networks, retrained on LSST images as soon as these become available, we will be able to efficiently process the huge amount of images detected by LSST and plan the follow-up of promising transients in due time.

Speaker: Dr Stefan Schuldt (University of Milan)

Stefan_Schuldt.pdf

3:15 PM Coffee break

Tutorial

12 Introduction to Fink tools

Speaker: Dr Julien Peloton (CNRS-IJCLab)

fink_tools.pdf

Break: Welcome cocktail

Tuesday, May 7

Keynote speakers

13 On the need of a plan for obtaining fast follow up of transients in the Rubin era with Brazilian facilities

The Vera Rubin Observatory will enable a wealth of discoveries when mapping the sky from Cerro Pachon, Chile, every few nights, for the next decade. Brazil is in a privileged situation to do follow-up work and characterize the newly discovered sources, having in particular a 4m and an 8m telescope next doors to the Vera Rubin telescope and a number of other telescopes (available or planned) on mountains close-by. There are also a number of smaller telescopes available in Brazil that could work towards the same goal. These Brazilian facilities have great potential to make breakthrough discoveries if they work within an organized plan and explore synergies. This talk will be a brief description of what facilities may be available and will present a few works that are already ongoing using T80-South, the Brazilian robotic telescope, which may be relevant for such a common effort.

Speaker: Prof. Claudia Mendes de Oliveira (IAG-USP)

SPLUS_FINK_rev.pptx

Contributed

14 S-PLUS Transient Extension Program (STEP)

Astronomical transients refer to objects in the sky not present in previously acquired data and have a finite amount of time visible in the sky. Time domain astronomy studies the dynamic sky, focusing on discovering and characterizing those transients, helping understand the physics and evolution of the sources and their surrounding environment. The S-PLUS Transient Extension Program (STEP) is a supernova and fast transient survey conducted in the Southern Hemisphere using data from the Southern Photometric Local Universe Survey (S-PLUS) Main Survey and T80-South telescope. We present an overview of the project, showing the infrastructure and operations, SN follow-up data obtained, data reduction, analysis of new transients and deep learning algorithms to optimize transient candidate selection. Additionally, we present prospects and some findings during the first part of O4 as part of a GW follow-up program to find the EM counterpart of GW events. The Vera Rubin Observatory’s first light is scheduled to happen in 2025 and will be the largest Time-domain survey of its time. We remark on how our project can help fill the gaps from Rubin’s cadence and contribute with light curve data of interesting candidates and field selection.

Speaker: Ms Andre Santos (CBPF)

STEP.pdf

15 The ALeRCE Broker

ALeRCE (http://alerce.science/) is an alert annotation and classification system led by an interdisciplinary and interinstitutional group of scientists based in Chile. ALeRCE focuses primarily on transients, variable stars, and stochastic sources. Thanks to its state-of-the-art machine learning models, ALeRCE has become the third-leading contributor in reporting candidates to the Transient Name Server, while enabling new avenues for scientific exploration across diverse astrophysical phenomena. This talk will provide an overview of the current status of ALeRCE, highlighting its achievements and contributions to date. Additionally, it will delve into the ongoing challenges that the project is actively addressing, while outlining plans for the future, particularly in anticipation of the Vera C. Rubin Observatory.

Speaker: Dr Guillermo Cabrera (Universidad de Concepción)

FINK - ALeRCE.pptx

10:15 AM Coffee break

Contributed

16 A wide view of the multi-messenger astronomy with Fink, from the detection to the characterisation

With the advent of optical large-scale astronomical surveys such as the Zwicky Transient Facility (ZTF) or the upcoming Vera C. Rubin Observatory, the number of alerts generated by transient, variable, and moving objects is skyrocketing to millions per night. To handle this influx, the processing of alerts has been delegated to the alert broker such as Fink, which identifies and classifies them for distribution to the scientific community. In multi-messenger astronomy, combining data from various sources like gravitational waves, neutrinos, and the other electromagnetic wavelengths with optical alerts provides a more comprehensive view of astrophysical objects. Fink-MM is the most recent development within the Fink broker that enables real-time multi-messenger transient detection. It merges the Fink alerts stream and General Coordinates Network (GCN) alerts stream via an automated pipeline to detect optical alerts that match in space and time with a GCN alert. Fink leverages cutting-edge computer science to quickly and efficiently filter the large number of alerts within the error box of multi-messenger events, redistributing them publicly to the scientific community. However, Fink-MM only detects multi-messenger candidates and does not help characterize them. Follow-up facilities must be involved for a better sampling of light curves. Additionally, a target and observation manager (TOM) has been developed between Fink and the ground-based network of telescopes used with the SVOM mission. The Fink-TOM manages the GVOM network, comprising Fink and the SVOM ground telescope, to automatically recover alerts from Fink suited for follow-up and manage photometry and spectroscopy follow-up campaigns involving multiple telescopes. The talk will presents Fink-MM and the GVOM network as well as the TOM.

Speaker: Roman Le Montagner

Fink_MM_Fink_Brazil@CBPF 07_05_2024.pdf

17 Simulation Based Inference of BNS Kilonova Properties: A Case Study with AT2017gfo

Kilonovae are a class of astronomical transients observed as counterparts to mergers of compact binary systems, such as a binary neutron star (BNS) or black hole-neutron star (BHNS) inspirals. They serve as probes for heavy-element nucleosynthesis in astrophysical environments, while together with gravitational wave emission constraining the distance to the merger itself, they can place constraints on the Hubble constant. Obtaining the physical parameters (e.g. ejecta mass, velocity, composition) of a kilonova from observations is a complex inverse problem, usually tackled by sampling-based inference methods such as Markov-chain Monte Carlo (MCMC) or nested sampling techniques. These methods often rely on computing approximate likelihoods, since a full simulation of compact object mergers involve expensive computations such as integrals, the calculation of likelihood of the observed data given parameters can become intractable, rendering the likelihood-based inference approaches inapplicable. We propose here to use Simulation-based Inference (SBI) techniques to infer the physical parameters of BNS kilonovae from their spectra, using simulations produced with KilonovaNet. Our model uses Amortized Neural Posterior Estimation (ANPE) together with an embedding neural network to accurately predict posterior distributions from simulated spectra. We further test our model with real observations from AT2017gfo, the only kilonova with multi-messenger data, and show that our estimates agree with previous likelihood-based approaches.

Speaker: Mr Phelipe Antonie Darc de Matos (CBPF)

phelipe_darc_sbi_fink.pdf

Contributed

18 Light-curve models of radiation counterparts from the merger of compact objects in AGNs

Time-domain surveys, such as DECam, ZTF, and the forthcoming Vera Rubin Telescope, open new possibilities for discovering explosive transients in our Universe. Optical flares, in particular, have been widely considered as potential counterparts to gravitational waves measured by the LIGO-Virgo-KAGRA Collaboration (LVK). While binary black hole (BBH) mergers dominate the population of gravitational wave (GW) events measured by the LVK, the origin of their potential multi-messenger signals is currently not well understood. In this presentation, we introduce new theoretical models predicting radiation signatures associated with BBH mergers in active galactic nuclei (AGNs), which are promising locations for a significant fraction of the GWs measured by the LVK experiment. The radiation feedback predicted from our analysis is comparable to or exceeds the emission of the hosting AGN. Specifically, we derive light curve models at optical wavelengths as a function of the parameters of the hosting AGNs and the merger event. Such theoretical models are applicable for localising radiation signals measured by time-domain surveys that can be associated with GW events.

Speaker: Dr Juan Carlos Rodriguez Ramirez (CBPF)

Juan_BBHs_Fink2024.pdf

Break: Group photo

12:05 PM Lunch

Round Table

19 Facilities for follow-up

Speakers: Charlie Kilpatrick (Northwestern University), Maria Pruzhinskaya (LPCA), Ted Leandro de Almeida (Laboratório Nacional de Astrofísica)

3:00 PM Coffee break

Tutorial

20 Offline analysis: REST API

Speaker: Dr Julien Peloton (CNRS-IJCLab)

Wednesday, May 8

Keynote speakers

21 Unveiling Rare Astrophysical Events in the Fink Broker

The detection of new astronomical events is one of the most anticipated outcomes of the next generation of large-scale sky surveys. Experiments such as the Vera Rubin Observatory Legacy Survey of Space and Time are expected to continuously monitor large areas of the sky with remarkable deliberation, which will undoubtedly lead to the detection of unforeseen astrophysical phenomena. At the same time, the volume of data gathered every night will also increase to unprecedented levels, rendering serendipitous discoveries unlikely. In the era of big data, most detected sources will never be visually inspected, and the use of automated algorithms is unavoidable. I would like to present the anomaly detection module developed for the Fink community broker – one of the official LSST brokers – to search for unusual astrophysical events in the Zwicky Transient Facility alert stream and LSST in future. I will present the first discoveries made with the module including AT2023awt – rare subtype of AM CVn variables, SN 2023mtp – supernova with a precursor. The spectral and photometric follow-up observations of AT2023awt and SN 2023mtp will be discussed. Other discoveries like fast transients, supernova candidates and cataclysmic variables will be presented. I will also introduce the Fink anomaly detection bot for Slack and Telegram, as well as the active anomaly detection that has been recently implemented to make the search for anomalies more efficient.

Speaker: Dr Maria Pruzhinskaya (LPCA)

pruzhinskaya_fink_brazil.pdf

Contributed

22 Deep Learning Simulation-Based Inference for Strong Lensing Inverse Modeling in Wide-Field Surveys

Strong Lensing is a phenomenon predicted by Einstein’s General Relativity in which the light emitted by a distant source is deflected by a massive object in its path, causing the image of the distant object to appear magnified and distorted. This process carries information about the dark matter distribution and the underlying cosmology in galaxies and galaxy clusters, making Strong Lensing a valuable probe of a few different astrophysical phenomena. Currently, the small number of known lenses, in the range of hundreds confirmed and modeled, prevents robust statistical analyses of these objects. However, future surveys such as the Vera Rubin Observatory and the ESA Euclid are expected to greatly increase the number of known strong lenses. Current modeling methods are relatively slow and require supervision, making them unsuitable for the volume of data expected in the next few years. As a result, methods based on Deep Learning have been proposed as alternatives for parameter inference in a likelihood-free fashion. In this work, we leverage Simulation-Based Inference methods to obtain posterior distributions for a few strong lensing parameters. We make use of Deep Learning techniques and, in particular, normalizing flows as density estimators to approach this problem under a Bayesian framework. We trained our models to infer Einstein Radius, lens galaxy velocity dispersion, and redshift of both lens and source objects on realistic wide field DECam-based simulated griz-band images, therefore DES-like, including realistic effects such as PSF and noise accordingly. Our top-performing models are able to generate well-calibrated posterior distributions with up to 83% median precision in inferred values, and show median fractional deviations of 5.5% for measurements of Einstein radius on simulated images. Our current focus is on achieving comparable results in real data.

Speaker: Mr Vitor Ramos (CBPF)

VitorRamos-FinkBrazil_talk.pdf

23 Kilonova Spectroscopic Identification with Deep Learning

In astronomy, transients are a source of new discoveries about the universe. Kilonovae are a type of transient resulting from the collision between neutron stars or between a neutron star and a black hole in compact binary systems, and currently, it is the only object with electromagnetic and gravitational wave counterparts (GW170817). As we navigate in the Big Data era, and expect the intensification of it with observatories such as LSST generating several alerts of transients per night, the use of Machine Learning algorithms enables an effective response for their analysis. This study seeks to understand how the use of Deep Learning can help in the identification of Kilonovae's spectral energy distribution (SED), testing different networks and studying which model achieves the best results when trying to differ between Supernovae e Kilonovae by its spectrum. The initial results indicate the accuracy of the recurrent neural network (RNN) architecture designed to classify the spectra, and with that in mind we can consider the possibility of future application of this methodology as a follow-up for the O4 LIGO-Virgo-KAGRA observing run, using data from observatories such as LSST to the classification of these transients searching for multi-messenger data.

Speaker: Ms Mariana Bittencourt (UFF / CBPF)

MarianaBittencourt-FINK_talk.pdf

24 Enabling precision photometric SN Ia cosmology with machine learning

The discovery of the accelerating expansion of the universe has led to increasing interest in probing the nature of dark energy. As very bright standardizable candles, type Ia supernovae (SNe Ia) are used to measure precise distances on cosmological scales and thus have been instrumental to this effort. Building a robust dataset of SNe Ia across a wide range of redshifts will allow for the construction of an accurate Hubble diagram, enrich our understanding of the expansion history of the universe, as well as place constraints on the dark energy equation of state. However, much of our analysis pipeline will be overwhelmed by the data deluge of the LSST era. In this talk, I will present recent improvements on two key pieces of SN Ia cosmology analysis: the purity of the photometric SNe Ia sample and the redshift identification accuracy for these SNe. To address the SNe Ia purity problem, I will present SCONE (Supernova Classification with a Convolutional Neural Network), a deep learning-based approach to early and full lightcurve photometric SN classification. On the redshift estimation front, I will present work on characterizing inaccurate redshifts due to SN host galaxy mismatch and its effect on cosmology, as well as Photo-zSNthesis, a machine learning algorithm that uses SN photometry to directly estimate redshift. As long as logistical challenges prevent the spectroscopic follow-up of most detected SNe, a reliable photometric SN classification algorithm and redshift estimation strategy will allow us to tap into the vast potential of the photometric dataset.

Speaker: Helen Qu (University of Pennsylvania)

helen qu fink brazil .pdf

10:30 AM Coffee break

Contributed

25 Circular polarimetry of V1082 Sgr: an extraordinary long-period magnetic cataclysmic variable

Pre-polars are detached binary systems composed by a magnetic white dwarf (WD) accreting matter by the capture of the stellar wind of a cool donor, usually a late-type main sequence star. These systems can be the progenitors of polars, which is a subclass of magnetic cataclysmic variable consisting of a Roche Lobe filling low-mass companion star and a strongly magnetized WD. Nowadays the evolution of magnetic cataclysmic variables is undergoing intense study, and pre-polars can play an important role for its comprehension. The present list of pre-polars has 22 objects, among confirmed and probable systems. Recently, 5 systems were detected with variable circular polarization. V1082 Sgr is a pre-polar candidate with long orbital period of 20.8-h. In this contribution, we present polarimetric observations performed using the 0.6-m Perkin-Elmer telescope of the Observatório Pico dos Dias (OPD/LNA- Brazil) coupled with the IAGPOL polarimeter. We detected circular polarization with an amplitude of 1% modulated with a period of 1.9-h interpreted as WD spin period. This result confirms the presence of magnetic accretion in V1082 Sgr and increases the number of pre-polars with positive detection of circular polarization.

Speaker: Dr Isabel de Jesus Lima (Unesp)

Isabel_talk.pdf

Keynote speakers

26 Active learning for improved classifications

Speaker: Dr Emille Ishida (CNRS/LPC-Clermont)

AL in Fink

AL_loop_tutorial_video

Round Table

27 Machine Learning

Speakers: Bernardo Fraga (CBPF), Guillermo Cabrera (Universidad de Concepción), Helen Qu (University of Pennsylvania)

ML

12:30 PM Lunch

Sightseeing: Visit to Morro da Urca + Pao de Acucar

Thursday, May 9

Keynote speakers

28 Resolved stellar populations in the Rubin Era: revealing extreme mass loss and stellar explosions before they occur

The Vera C. Rubin Legacy Survey of Space and Time will probe the optical time-domain with unprecedented depth, wide area, and cadence, yielding millions of transients and new scientific insight into stellar explosions, compact object physics, and cosmology. Much of the preparation for Rubin science focuses around readying existing transient surveys for the multiple order of magnitude increase in the discovery rate of optical transients. However, new challenges will arise as Rubin enables wholly new areas of discovery, including studies of the variability from the millions of resolved massive stars that will be present in Rubin survey fields, including their modes of mass loss, binary evolution, and signposts of their impending core collapse, both into black holes and luminous supernovae. I will discuss efforts to prepare for resolved massive star science in the Rubin era, including the identification of rare and extreme variables similar to the variable, heavily-extinguished red supergiant progenitor of SN2023ixf in M101, archival programs to establish catalogs of massive star populations in Hubble Space Telescope and James Webb Space Telescope imaging, and the potential to identify supernova progenitor stars through their variability on timescales of years to months before the explode.

Speaker: Dr Charlie Kilpatrick (Northwestern University)

kilpatrick_fink_2024-05-09.pdf

Contributed

29 SN 2022ACKO and SN progenitor Star properties: Detection, Light Curves and late phases analysis

Red supergiants (RSGs) stars are a stage of the life cycle of a massive star, with mass greater than 8 times the mass of the Sun. At the end of their lives, these stars either explode as supernovae or collapse directly into black holes. Having an efficient alert system allows us to infer the fundamental properties of the supernova progenitor star, that are contained mostly in the early phases of the explosion In this contribution, we present an analysis of infrared, optical, and ultraviolet data from the hydrogen-rich supernova 2022acko. We will discuss possible disagreements between the shock cooling modeling and the observed data for earlier phases. From the late phases data, we will discuss the radius and temperature evolution of the shock, and also an analysis on the nickel mass of the SN progenitor star. The follow-up data and pre-explosion imaging support that 2022acko was the explosion of a low-mass red supergiant star. Combining these data, we constrain the formation of a neutron star in the explosion of this event.

Speaker: Mr Gabriel Teixeira (CBPF)

FINK BR - SN 2022ACKO.pdf

30 PETS: A pure expansion model for type Ia supernovae standardization

We present a light curve fitter for type Ia Supernovae dubbed Pure Expansion Template for Supernovae (PETS). The model consists of an expansion of its rest-frame flux based on the well-known Spectral Adaptive Light Curve Template 2 (SALT2). We generate the expansion components by performing Principal Component Analysis (PCA) and Factor Analysis (FA) onto a representative training set. Then, we derive a Tripp-like expression for the distance modulus and fit the ΛCDM cosmological model on the Pantheon sample. Next, we evaluate the constraining power of the model in comparison with SALT2 and verify linear correlations with color and stretch parameters.

Speaker: Ms Cassia Nascimento (UFRJ)

PETS_Cassia.pdf

31 Astrometric Redshifts of Supernovae

Differential Chromatic Refraction (DCR) is caused by the wavelength dependence of our atmosphere's refractive index that shifts the apparent positions of stars and galaxies and distorts their shapes depending on their spectral energy distribution (SED). While this effect is typically mitigated and corrected for in imaging observations, we investigate how DCR can instead be used to our advantage to infer the redshifts of supernovae from multi-band, time-series imaging data. We simulate Type Ia supernova (SN Ia) in the proposed Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) Deep Drilling Field (DDF), and evaluate astrometric redshifts. We find that the redshift accuracy improves dramatically with the statistical quality of the astrometric measurements as well as with the accuracy of the astrometric solution. For a conservative choice of a 5-mas systematic uncertainty floor, we find that our redshift estimation is accurate at $z < 0.6$. We then combine our astrometric redshifts with both host galaxy photometric redshifts and supernovae photometric (light curve) redshifts and show that this considerably enhances the overall redshift accuracy. These astrometric redshifts will be valuable especially since Rubin will discover a vast number of supernovae for which we will not be able to obtain spectroscopic redshifts.

Speaker: Mr Jaemyoung (Jason) Lee (University of Pennsylvania)

Jason_Fink_Brazil_240509-1.pdf

10:30 AM Coffee break

Keynote speakers

32 The Legacy Survey of Space and Time (LSST) - an overview

The Legacy Survey of Space and Time (LSST) is a planned 10-year survey of the southern sky to be conducted at the Vera C. Rubin Observatory. The Rubin Observatory is expected to produce approximately 15 terabytes of LSST data per night, covering the entire sky every three nights. The LSST project involves eight separate science collaborations, including the Dark Energy Science Collaboration (DESC) and the LSST Transients and Variable Stars Science Collaboration (TVSSC). This presentation will provide a brief overview of the LSST project and highlight the involvement of the Brazilian Participation Group - LSST (BPG-LSST).

Speaker: Dr Mariana Penna-Lima (University of Brasilia)

PennaLima_FinkBrazil_May24.pdf

Contributed

33 Detection and characterization of exoplanets using Gravitational Microlensing: OPD detections with a worldwide effort

The identification and characterization of exoplanets have surpassed 5000 celestial bodies, by using various techniques, such as the transit method, which depends on the periodic transits caused by the planet passing in front of the star, and the radial velocity method which relies on the oscillations in absorption line positions in stellar spectra. Each technique has specific advantages; the transit method excels in detecting close giant planets, while radial velocity identifies high-mass planets effectively. In this talk, we will go into gravitational microlensing (GM), a highly sensitive method for detecting low-mass exoplanets at greater distances from their hosts. Attention is dedicated to statistically surveying our galaxy for exoplanets, emphasizing Gravitational Microlenses. The transient nature of these events, coupled with collaboration with observatories around the World, enhances the significance of the probability of detection. We will explore the data and results of the collaboration between the National Laboratory of Astrophysics (LNA) and Ohio University through the MicroFun network that started in 2021. The Pico dos Dias Observatory (OPD), managed by LNA, has observed hundreds of GM events since 2021, in particular, the KB20414Lb, which is a stellar system that hosts an Earth-mass planet.

Speaker: Dr Leandro Almeida (Laboratório Nacional de Astrofísica)

Ted_Presentation_FINK_240506.pdf

11:50 AM Lunch

Tutorial

34 Real-time analysis: filters & bots

Speaker: Dr Julien Peloton (CNRS-IJCLab)

2:45 PM Mini-break

35 Real-time analysis: science modules

Speakers: Andre Santos (CBPF), Dr Julien Peloton (CNRS-IJCLab), Phelipe Antonie Darc de Matos (CBPF)

3:30 PM Coffee break

Round Table

36 The future of Fink@Brazil

Speakers: Clecio de Bom (CBPF), Dr Emille Ishida (CNRS/LPC-Clermont), Martin Makler (UNSAM/CBPF)

The future of Fink@Brazil

Friday, May 10

CBPF: Office hours 1

Conveners: Dr Emille Ishida (CNRS/LPC-Clermont), Dr Julien Peloton (CNRS-IJCLab)

37 Strong Lensing

Speakers: Joao Paulo Franca (CBPF), Martin Makler (UNSAM/CBPF)

9:45 AM Mini-break

CBPF: Office hours 2

Conveners: Dr Emille Ishida (CNRS/LPC-Clermont), Dr Julien Peloton (CNRS-IJCLab)

38 AL classifier + GPU

Speakers: Andre Santos, Rose Clivia Santos

10:45 AM Mini-break

CBPF: Office hours 3

Conveners: Dr Emille Ishida (CNRS/LPC-Clermont), Dr Julien Peloton (CNRS-IJCLab)

39 HOLISMOKES

Speaker: Stefan Schuldt

11:45 AM Lunch

CBPF: Office hours 3

Convener: Nicholas de Souza

1:45 PM Mini-break

CBPF: Office hours 4

Conveners: Dr Emille Ishida (CNRS/LPC-Clermont), Dr Julien Peloton (CNRS-IJCLab)

40 pipeline

Speakers: Gustavo Oliveira Schwarz, Lilianne Nakazono (IAG-USP)

2:45 PM Mini-break

CBPF: Office hours 5

Conveners: Dr Emille Ishida (CNRS/LPC-Clermont), Dr Julien Peloton (CNRS-IJCLab)

41 follow-up

Speakers: Carel van Gend, Maria Pruzhinskaya (LPCA)

3:45 PM Mini-break

CBPF: Office hours 6

Conveners: Dr Emille Ishida (CNRS/LPC-Clermont), Dr Julien Peloton (CNRS-IJCLab)

42 exoplanets