20 research outputs found
Statefinder Revisited
The quality of supernova data will dramatically increase in the next few
years by new experiments that will add high-redshift supernova to the currently
known ones. In order to use this new data to discriminate between different
dark energy models, the statefinder diagnostic was suggested and investigated
by Alam et al. in the light of the proposed SuperNova Acceleration Probe (SNAP)
satellite. By making use of the same procedure presented by these authors, we
compare their analyzes with ours, which shows a more realistic supernovae
redshift distribution and do not assume that the intercept is known. We also
analyzed the behavior of the statefinder pair {r,s} and the alternative pair
{s,q} in the presence of offset errors
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.Comment: 8 pages, 7 figures - submitted to Astronomy and Astrophysics.
Comments are welcom
When did cosmic acceleration start? How fast was the transition?
Cosmic acceleration is investigated through a kink-like expression for the
deceleration parameter (q). The new parametrization depends on the initial
(q_i) and final (q_f) values of q, on the redshift of the transition from
deceleration to acceleration (z_{t}) and the width of such transition (\tau).
We show that although supernovae (SN) observations (Gold182 and SNLS data
samples) indicate, at high confidence, that a transition occurred in the past
(z_{t}>0) they do not, by themselves, impose strong constraints on the maximum
value of z_{t}. However, when we combine SN with the measurements of the ratio
between the comoving distance to the last scattering surface and the
SDSS+2dfGRS BAO distance scale (S_{k}/D_{v}) we obtain, at 95.4% confidence
level, z_{t}=0.84+{0.17}-{0.13} and \tau =0.51-{0.17}+{0.23} for
(S_{k}/D_{v}+Gold182), and z_{t}=0.88-{0.10}+{0.12} and \tau
=0.35-{0.10}+{0.12} for (S_{k}/D_{v} + SNLS), assuming q_i=0.5 and q_f=-1. We
also analyze the general case, q_f\in(-\infty,0) finding the constraints that
the combined tests (S_{k}/D_{v} + SNLS) impose on the present value of the
deceleration parameter (q_0).Comment: 7 pages, 3 figures. Replaced to match the published versio
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.Comment: Accepted for the Machine learning and the Physical Sciences workshop
of NeurIPS 202
Stress testing the dark energy equation of state imprint on supernova data
International audienceThis work determines the degree to which a traditional analysis of the standard model of cosmology (ΛCDM) based on type Ia supernovae can identify deviations from a cosmological constant in the form of a redshift-dependent dark energy equation of state w(z). We introduce and apply a novel random curve generator to simulate instances of w(z) from constraint families with increasing distinction from a cosmological constant. After producing a series of mock catalogs of binned type Ia supernovae corresponding to each w(z) curve, we perform a standard ΛCDM analysis to estimate the corresponding posterior densities of the absolute magnitude of type Ia supernovae, the present-day matter density, and the equation of state parameter. Using the Kullback-Leibler divergence between posterior densities as a difference measure, we demonstrate that a standard type Ia supernova cosmology analysis has limited sensitivity to extensive redshift dependencies of the dark energy equation of state. In addition, we report that larger redshift-dependent departures from a cosmological constant do not necessarily manifest easier-detectable incompatibilities with the ΛCDM model. Our results suggest that physics beyond the standard model may simply be hidden in plain sight
Periodic Astrometric Signal Recovery Through Convolutional Autoencoders
Astrometric detection involves precise measurements of stellar positions, and it is widely regarded as the leading concept presently ready to find Earth-mass planets in temperate orbits around nearby sun-like stars. The TOLIMAN space telescope [39] is a low-cost, agile mission concept dedicated to narrow-angle astrometric monitoring of bright binary stars. In particular the mission will be optimised to search for habitable-zone planets around {\}{\$}{\backslash}alpha {\$}{\$}\alpha$ Centauri AB. If the separation between these two stars can be monitored with sufficient precision, tiny perturbations due to the gravitational tug from an unseen planet can be witnessed and, given the configuration of the optical system, the scale of the shifts in the image plane are about one-millionth of a pixel. Image registration at this level of precision has never been demonstrated (to our knowledge) in any setting within science. In this paper, we demonstrate that a Deep Convolutional Auto-Encoder is able to retrieve such a signal from simplified simulations of the TOLIMAN data and we present the full experimental pipeline to recreate out experiments from the simulations to the signal analysis. In future works, all the more realistic sources of noise and systematic effects present in the real-world system will be injected into the simulations
Periodic Astrometric Signal Recovery through Convolutional Autoencoders
Astrometric detection involves a precise measurement of stellar positions,
and is widely regarded as the leading concept presently ready to find
earth-mass planets in temperate orbits around nearby sun-like stars. The
TOLIMAN space telescope[39] is a low-cost, agile mission concept dedicated to
narrow-angle astrometric monitoring of bright binary stars. In particular the
mission will be optimised to search for habitable-zone planets around Alpha
Centauri AB. If the separation between these two stars can be monitored with
sufficient precision, tiny perturbations due to the gravitational tug from an
unseen planet can be witnessed and, given the configuration of the optical
system, the scale of the shifts in the image plane are about one millionth of a
pixel. Image registration at this level of precision has never been
demonstrated (to our knowledge) in any setting within science. In this paper we
demonstrate that a Deep Convolutional Auto-Encoder is able to retrieve such a
signal from simplified simulations of the TOLIMAN data and we present the full
experimental pipeline to recreate out experiments from the simulations to the
signal analysis. In future works, all the more realistic sources of noise and
systematic effects present in the real-world system will be injected into the
simulations.Comment: Preprint version of the manuscript to appear in the Volume
"Intelligent Astrophysics" of the series "Emergence, Complexity and
Computation", Book eds. I. Zelinka, D. Baron, M. Brescia, Springer Nature
Switzerland, ISSN: 2194-728
Supernova search with active learning in ZTF DR3
We provide the first results from the complete SNAD adaptive learning
pipeline in the context of a broad scope of data from large-scale astronomical
surveys. The main goal of this work is to explore the potential of adaptive
learning techniques in application to big data sets. Our SNAD team used Active
Anomaly Discovery (AAD) as a tool to search for new supernova (SN) candidates
in the photometric data from the first 9.4 months of the Zwicky Transient
Facility (ZTF) survey, namely, between March 17 and December 31 2018 (58194 <
MJD < 58483). We analysed 70 ZTF fields at a high galactic latitude and
visually inspected 2100 outliers. This resulted in 104 SN-like objects being
found, 57 of which were reported to the Transient Name Server for the first
time and with 47 having previously been mentioned in other catalogues, either
as SNe with known types or as SN candidates. We visually inspected the
multi-colour light curves of the non-catalogued transients and performed
fittings with different supernova models to assign it to a probable photometric
class: Ia, Ib/c, IIP, IIL, or IIn. Moreover, we also identified unreported
slow-evolving transients that are good superluminous SN candidates, along with
a few other non-catalogued objects, such as red dwarf flares and active
galactic nuclei. Beyond confirming the effectiveness of human-machine
integration underlying the AAD strategy, our results shed light on potential
leaks in currently available pipelines. These findings can help avoid similar
losses in future large-scale astronomical surveys. Furthermore, the algorithm
enables direct searches of any type of data and based on any definition of an
anomaly set by the expert.Comment: 22 pages with appendix, 12 figures, 2 tables, accepted for
publication in Astronomy and Astrophysic
Spectroscopic Confirmation of a Population of Isolated, Intermediate-Mass YSOs
Wide-field searches for young stellar objects (YSOs) can place useful
constraints on the prevalence of clustered versus distributed star formation.
The Spitzer/IRAC Candidate YSO (SPICY) catalog is one of the largest
compilations of such objects (~120,000 candidates in the Galactic midplane).
Many SPICY candidates are spatially clustered, but, perhaps surprisingly,
approximately half the candidates appear spatially distributed. To better
characterize this unexpected population and confirm its nature, we obtained
Palomar/DBSP spectroscopy for 26 of the optically-bright (G<15 mag) "isolated"
YSO candidates. We confirm the YSO classifications of all 26 sources based on
their positions on the Hertzsprung-Russell diagram, H and Ca II line-emission
from over half the sample, and robust detection of infrared excesses. This
implies a contamination rate of <10% for SPICY stars that meet our optical
selection criteria. Spectral types range from B4 to K3, with A-type stars most
common. Spectral energy distributions, diffuse interstellar bands, and Galactic
extinction maps indicate moderate to high extinction. Stellar masses range from
~1 to 7 , and the estimated accretion rates, ranging from
to yr, are typical for YSOs
in this mass range. The 3D spatial distribution of these stars, based on Gaia
astrometry, reveals that the "isolated" YSOs are not evenly distributed in the
Solar neighborhood but are concentrated in kpc-scale dusty Galactic structures
that also contain the majority of the SPICY YSO clusters. Thus, the processes
that produce large Galactic star-forming structures may yield nearly as many
distributed as clustered YSOs.Comment: Accepted for publication in AJ. 22 pages, 9 figures, and 4 tables.
Figure sets are available from
https://sites.astro.caltech.edu/~mkuhn/SPICY/PaperIII