Today on Women in Engineering Day, we celebrate the engineers who make LISA possible — and reaffirm our commitment to advancing equity in STEM and astronomy. 💫 #WomenInEngineering #LISA #STEMEquity

[posted by the LISA DEI Committee]

📣 Registration is now open for the 2025 #LISASchool! The LISA School will take place from 6–17 October 2025 at the École de physique des Houches, nestled in the beautiful French Alps 🏔️

👉 Register here: https://indico.in2p3.fr/e/lisa-school

🗓 Registration closes: 11 July 2025 (midnight CET)

🧑‍🔬 Capacity: ~50 early-career scientists (first-come, first-served)

Topics include: GW fundamentals, instrumentation, waveforms, data analysis, astrophysics, cosmology, and fundamental physics.

Logistics:
- Dates: 6–17 October 2025 (arrival on the 6th, departure on the 17th)
- Location: École de Physique Les Houches, Mont Blanc Valley
- Fee: EUR 900 – includes lodging, meals, coffee breaks, IT/library access
- Financial Support: Available on request in the pre-registration form
- Commitment: Full attendance required (no partial participation)

📽️ Sessions will be recorded for wider public access!

In this episode of #LECSTalks we feature Maria Işfan, PhD student at University of Bucharest, who recently gave LISA Early Career Scientists a talk titled "Quantum Neural Networks for LISA Low-Latency Data Analysis Pipelines".

You can watch the talk on YouTube 📽 https://youtu.be/hStfmWsHlKo 📽

🎤 Tell us something about yourself
I am a PhD student at University of Bucharest and also a research assistant at the Institute of Space Science - a subsidiary of INFLPR in Romania. In my free time, I play guitar.

🎤 What can you tell us about Quantum Neural Networks for LISA Low-Latency Data Analysis Pipelines?
Quantum computing is advancing rapidly, allowing computational speed-ups. As LISA will require fast data processing, we are exploring how quantum neural networks (QNNs) can help gravitational waves (GWs) data analysis. QNNs can learn patterns faster and handle noisy or limited data better than classical methods. In our proof-of-concept studies, we trained QNNs on simulated gravitational-wave signals and achieved accurate signal classification. We also used QNNs to successfully identify most of the GWs signals in the Sangria LISA Data Challenge. These promising results suggest that QNNs could be a powerful tool in LISA’s low-latency data analysis pipeline.

#LISACommunityTalks

The #LISACommunity recently hosted a talk by Ollie Burke about a paper by Ollie Burke, Sylvain Marsat, Jonathan R. Gair, Michael L. Katz: "Mind the gap: addressing data gaps and assessing noise mismodeling in LISA", which can be found here 👉 https://arxiv.org/abs/2502.17426

Here's a summary of the paper:

"Communication outages and malfunctions for complicated space-missions are simply an inevitability. Although talented personnel will craft an instrument that runs as smoothly as possible, the space-based gravitational wave observatory LISA will be no outlier — LISA will observe gaps and gremlins in the data. These artefacts could violate a number of assumptions scientists usually place on the data prior to analysis, potentially hampering our ability to extract gravity waves from the data that are generated from collisions of the most extreme compact objects within our universe. This talk (with associated paper) details calculations, theory and computational techniques that can be used to mitigate the impact of data gaps on LISA-based gravitational wave data analysis."

#LISACommunityTalks

The #LISACommunity recently hosted a talk by David Maibach about a paper by Nils Deppe, Lavinia Heisenberg, Henri Inchauspé, Lawrence E. Kidder, David Maibach, Sizheng Ma, Jordan Moxon, Kyle C. Nelli, William Throwe, Nils L. Vu: "Echoes from Beyond: Detecting Gravitational Wave Quantum Imprints with LISA", which can be found here 👉 https://arxiv.org/abs/2411.05645