arXiv subjects

Soma Mukherjee

Publications and source records attributed to Soma Mukherjee.

9 recordsLinked to original sources

arXiv2024

An Optically Targeted Search for Gravitational Waves emitted by Core-Collapse Supernovae during the Third Observing Run of Advanced LIGO and Advanced Virgo

We present the results from a search for gravitational-wave transients associated with core-collapse supernovae observed optically within 30 Mpc during the third observing run of Advanced LIGO and Advanced Virgo. No gravitational wave associated with a core-collapse supernova has been identified. We then report the detection efficiency for a variety of possible gravitational-wave emissions. For neutrino-driven explosions, the distance at which we reach 50% detection efficiency is up to 8.9 kpc, while more energetic magnetorotationally-driven explosions are detectable at larger distances. The distance reaches for selected models of the black hole formation, and quantum chromodynamics phase transition are also provided. We then constrain the core-collapse supernova engine across a wide frequency range from 50 Hz to 2 kHz. The upper limits on gravitational-wave energy and luminosity emission are at low frequencies down to $10^{-4}\,M_\odot c^2$ and $6 \times 10^{-4}\,M_\odot c^2$/s, respectively. The upper limits on the proto-neutron star ellipticity are down to 3 at high frequencies. Finally, by combining the results obtained with the data from the first and second observing runs of LIGO and Virgo, we improve the constraints of the parameter spaces of the extreme emission models. Specifically, the proto-neutron star ellipticities for the long-lasting bar mode model are down to 1 for long emission (1 s) at high frequency.

astro-ph.HE↗

arXiv2021

Using supervised learning algorithms as a follow-up method in the search of gravitational waves from core-collapse supernovae

We present a follow-up method based on supervised machine learning (ML) to improve the performance in the search of gravitational wave (GW) burts from core-collapse supernovae (CCSNe) using the coherent WaveBurst (cWB) pipeline. The ML model discriminates noise from signal events using as features a set of reconstruction parameters provided by cWB. Detected noise events are discarded yielding to a reduction of the false alarm rate (FAR) and of the false alarm probability (FAP) thus enhancing of the statistical significance. We tested the proposed method using strain data from the first half of the third observing run of advanced LIGO, and CCSNe GW signals extracted from 3D simulations. The ML model is learned using a dataset of noise and signal events, and then it is used to identify and discard noise events in cWB analyses. Noise and signal reduction levels were examined in single detector networks (L1 and H1) and two detector network (L1H1). The FAR was reduced by a factor of $\sim10$ to $\sim100$, there was an enhancement in the statistical significance of $\sim1$ to $\sim2σ$, while there was no impact in detection efficiencies.

gr-qc↗

arXiv2021

Detecting and reconstructing gravitational waves from the next Galactic core-collapse supernova in the Advanced Detector Era

We performed a detailed analysis of the detectability of a wide range of gravitational waves derived from core-collapse supernova simulations using gravitational-wave detector noise scaled to the sensitivity of the upcoming fourth and fifth observing runs of the Advanced LIGO, Advanced Virgo, and KAGRA. We use the coherent WaveBurst algorithm, which was used in the previous observing runs to search for gravitational waves from core-collapse supernovae. As coherent WaveBurst makes minimal assumptions on the morphology of a gravitational-wave signal, it can play an important role in the first detection of gravitational waves from an event in the Milky Way. We predict that signals from neutrino-driven explosions could be detected up to an average distance of 10 kpc, and distances of over 100 kpc can be reached for explosions of rapidly rotating progenitor stars. An estimated minimum signal-to-noise ratio of 10-25 is needed for the signals to be detected. We quantify the accuracy of the waveforms reconstructed with coherent WaveBurst and we determine that the most challenging signals to reconstruct are those produced in long-duration neutrino-driven explosions and models that form black holes a few seconds after the core bounce.

astro-ph.HE↗

arXiv2018

Enhancing the Sensitivity of Searches for Gravitational Waves from Core-Collapse Supernovae with a Bayesian classification of candidate events

We demonstrate how a morphological veto involving Bayesian statistics can improve the receiver-operating characteristic (ROC) curves of the current search for core-collapse supernovae (CCSNe) as implemented by the coherent Waveburst (cWB) algorithm. Examples involving two implementations of BayesWave (BW), one that makes no assumption of the polarization state of the gravitational wave (GW) and one that uses the same elliptical polarization settings adopted in previous usages for Binary systems are provided on the set of waveforms currently adopted for the first and second Advanced LIGO (aLIGO) science runs for the targeted CCSNe search. A comparison of the performance for all-sky searches versus the targeted searches with optical triggers is provided. The average cWB+BW ROC range improvements with respect to a fixed false-alarm rate (FAR) for slowly-rotating waveforms ranged from [+1.30%, +15.76%] while the improvement for rapidly-rotating waveforms were on the order of [+1.19%, +22.05%]. The application of BW to CCSN GW triggers also shows a significant reduction of the FAR while maintaining detection efficiency and remaining sensitive to a wide range of morphological CCSNe signals. It appears that the code developed for arbitrarily polarized signals outperforms the previous code for the GW morphologies tested.

astro-ph.HE↗

arXiv2016

GW150914: First search for the electromagnetic counterpart of a gravitational-wave event by the TOROS collaboration

We present the results of the optical follow-up conducted by the TOROS collaboration of the first gravitational-wave event GW150914. We conducted unfiltered CCD observations (0.35-1 micron) with the 1.5-m telescope at Bosque Alegre starting ~2.5 days after the alarm. Given our limited field of view (~100 square arcmin), we targeted 14 nearby galaxies that were observable from the site and were located within the area of higher localization probability. We analyzed the observations using two independent implementations of difference-imaging algorithms, followed by a Random-Forest-based algorithm to discriminate between real and bogus transients. We did not find any bona fide transient event in the surveyed area down to a 5-sigma limiting magnitude of r=21.7 mag (AB). Our result is consistent with the LIGO detection of a binary black hole merger, for which no electromagnetic counterparts are expected, and with the expected rates of other astrophysical transients.

astro-ph.HE↗

arXiv2012

A New Approach to Time Domain Classification of Broadband Noise in Gravitational Wave Data

Broadband noise in gravitational wave (GW) detectors, also known as triggers, can often be a deterrant to the efficiency with which astrophysical search pipelines detect sources. It is important to understand their instrumental or environmental origin so that they could be eliminated or accounted for in the data. Since the number of triggers is large, data mining approaches such as clustering and classification are useful tools for this task. Classification of triggers based on a handful of discrete properties has been done in the past. A rich information content is available in the waveform or 'shape' of the triggers that has had a rather restricted exploration so far. This paper presents a new way to classify triggers deriving information from both trigger waveforms as well as their discrete physical properties using a sequential combination of the Longest Common Sub-Sequence (LCSS) and LCSS coupled with Fast Time Series Evaluation (FTSE) for waveform classification and the multidimensional hierarchical classification (MHC) analysis for the grouping based on physical properties. A generalized k-means algorithm is used with the LCSS (and LCSS+FTSE) for clustering the triggers using a validity measure to determine the correct number of clusters in absence of any prior knowledge. The results have been demonstrated by simulations and by application to a segment of real LIGO data from the sixth science run.

gr-qc↗

arXiv2000

Data conditioning for gravitational wave detectors: A Kalman filter for regressing suspension violin mode

Interferometric gravitational wave detectors operate by sensing the differential light travel time between free test masses. Correspondingly, they are sensitive to anything that changes the physical distance between the test masses, including physical motion of the masses themselves. In ground-based detectors the test masses are suspended as pendula and, consequently, thermal or other excitations of the suspension wires' violin modes lead to a strong, albeit narrow-band, ``signal'' in the detector wave-band that can confound attempts to observe gravitational waves. Here we describe the design of a Kalman filter that determines the time-dependent vibrational state of a detector's suspension ``violin'' modes from the detector output. From the estimated state we can predict that component of the detector output due to suspension excitations, thermal or otherwise, and subtractively remove those disturbances from the detector output. We demonstrate the filter's effectiveness both through numerical simulations and application to real data taken on the LIGO 40 M prototype detector.

gr-qc↗

arXiv1999

Removing Instrumental Artifacts: Suspension Violin Modes

We describe the design of a Kalman filter that identifies suspension violin modes in an interferometric gravitational wave detectors data channel. We demonstrate the filter's effectiveness by applying it to data taken on the LIGO~40M prototype.

gr-qc↗

arXiv1998

Three types of gamma-ray bursts

A multivariate analysis of gamma-ray burst (GRB) bulk properties is presented to discriminate between distinct classes of GRBs. Several variables representing burst duration, fluence and spectral hardness are considered. Two multivariate clustering procedures are used on a sample of 797 bursts from the Third BATSE Catalog: a nonparametric average linkage hierarchical agglomerative clustering procedure validated with Wilks' $Λ^*$ and other MANOVA tests; and a parametric maximum likelihood model-based clustering procedure assuming multinormal populations calculated with the EM Algorithm and validated with the Bayesian Information Criterion. The two methods yield very similar results. The BATSE GRB population consists of three classes with the following Duration/Fluence/Spectrum bulk properties: Class I with long/bright/intermediate bursts, Class II with short/hard/faint bursts, and Class III with intermediate/intermediate/soft bursts. One outlier with poor data is also present. Classes I and II correspond to those reported by Kouveliotou et al. (1993), but Class III is clearly defined here for the first time.

astro-ph↗